2020 Research Projects

Title: Game Simulation Engine for Evolutionary Game Theory Research
Primary Mentor: Golam Md. Muktadir
Faculty advisor: Prof. Luca de Alfaro
Location: Remote/online
Number of Interns: 3
Project description:
The mentor’s research group is developing a complete game simulation engine for research purposes in the area of Evolutionary Game Theory. In an example scenario, there will be a grid world with different kinds of animals and resources. The engine will simulate evolution of the environment over time and try to find if it can reach a sustainable state. This project is developed in Python and TensorFlow. There are also some AI animals who are weak but can learn to survive!
Tasks:
The SIP interns’ primary tasks will be to learn Python and add to research ideas. Their secondary tasks will include designing and implement a few animals and running simulations. Designing a good animal is difficult because, if the animal is too strong, the world may collapse, and if it is too weak, it may go extinct! This is also the fun part.
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; game theory; machine learning
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Machine Learning and Mineral Identification on Mars
Primary Mentor: Genesis Berlanga
Faculty advisor: Prof. Quentin Williams
Location: Remote/online
Number of Interns: 3
Project description:
NASA’s Mars rovers take thousands of images and spectra every day. Analyzing this information is a massive task that takes months of work, but with the help of computers, scientists can shorten the time it takes to arrive at exciting results. In order to train a computer to be a geological assistant, the SIP interns will program a computer to automatically identify rocks and minerals found on the surface of the Moon and Mars. The interns will help build neural networks modeled after the circuitry of brain neurons to train the computer to accomplish this task using rocks we find on Earth. This research project will inform future research for Mars rovers like Curiosity or the upcoming Perseverance, by finding ways to simplify rock and mineral identification while roving the surface of another planet.
Tasks:
The SIP interns’ tasks will include: (1) identifying spectra and images of rocks and minerals relevant to the Moon and Mars; (2) programming in Python, MATLAB, or R; and (3) building a neural network that automatically identifies minerals. Computer programming experience is encouraged but not necessary. The mentor will provide training.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Technology and Oral Story Collection of Indian Immigrants in the USA
Primary Mentor: Dr. Annapurna Pandey
Secondary Mentor: Kati Greaney
Location: Remote/online
Number of Interns: 4
Project description:
These days, one often hears that we human beings are primarily story tellers. We tell stories about ourselves as well as about others. What these stories tell us is the rich experience human beings have acquired in their life. The world in which we live today is largely created by technology. The mentor and SIP interns will use various tools provided by technology in their digital story telling research. This project will encourage SIP interns to collect stories about the immigrant experience in the United States. For the last three decades the mentor has been working on the Indian diaspora in the Greater Bay Area, California. The mentor has made two films, “Homeland in the Heart” and “Life Giving Ceremony of Jagannath” documenting the involvement of Odia people (people from the state of Odisha) in building a community and developing a sense of belonging to the United States. The mentor would like to broaden the scope of this research by incorporating the experiences of other Indian immigrants.
Tasks:
This project will give an opportunity to the SIP interns to collect oral history material about the experiences of immigrant parents, grandparents, and their American-born children. The material will include streaming audio and written transcripts accessible online in digital formats. The mentor and SIP interns will use various available technology tools. The mentor’s aim in this project is to collect interviews of Indian immigrants in the USA. The SIP interns will interview various members of the Indian community and collect their experiences in this country compared to their experience in their homeland that they have left behind. These interviews are a unique source of contemporary history through the experiences of the immigrants. Past studies have shown that this kind of research has revealing consequences for both the researchers as well as the subjects of their research.

Title: What Happens Around Supermassive Black Holes
Primary Mentor: Dr. Martin Gaskell
Location: Remote/online
Number of Interns: 3
Project description:
Astronomers now believe that every large galaxy contains a supermassive black hole in its center. Because of the tremendous energy released as the black hole grows by swallowing gas, these black holes can be readily detected as so-called “active galactic nuclei” (AGNs) back to very early times in the Universe. The details of how supermassive black holes form and grow and how this is related to the formation of normal galaxies is one of the central mysteries of contemporary astrophysics. The mentor’s research group is analyzing spectra and spectral variability to try to understand how AGNs produce the intense radiation seen, what the structure of material around the black hole is like, and how supermassive black holes grow.
Tasks:
SIP intern involvement in the project will consist of analyzing multi-wavelength spectral observations of relatively nearby actively accreting supermassive black holes to try to understand the emissions and how the black holes grow. This work will involve compiling data sets, applying corrections, making statistical estimates of parameters, and comparing the results with theoretical models of processes going on around black holes.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://campusdirectory.ucsc.edu/cd_detail?uid=mgaskell
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	OFF	ON	ON	ON	ON	ON

Title: Cosmological Galaxy Simulation Data Post-Processing
Primary Mentor: Clayton Strawn
Faculty advisor: Prof. Joel Primack
Location: Remote/online
Number of Interns: 2
Project description:
Cosmological galaxy simulations have become increasingly meaningful in the last few decades, and mock “observational” tests of simulations can set meaningful constraints on how accurately the physical assumptions built into the simulation emulate the real Universe. In this project, the mentor intends to use mock quasar/galaxy absorption spectra created with the new software TRIDENT to emulate observations of the region directly outside of galaxies proper but within their dark matter halo, the circumgalactic medium (CGM). The CGM is relatively difficult to observe, because gas is not dense enough to form stars, and therefore this region is only detected in absorption, so only by simulating this observed quantity can one evaluate the simulation’s CGM.
Tasks:
The plan is for the SIP interns to help organize and collect data on these mock absorption spectra. This will involve creating useful interface methods between spectrum images and observational analysis methods, which have before always been applied only to observed spectra rather than simulated ones. The interns will become familiar with contributing to open-source software, as well as writing/testing/debugging well-documented code for science use. (The URL below is not made by the mentor’s research group and collaborators, but is a useful introductory page to look at.)
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://trident-project.org
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Photometric Variability in the NGVS: Milky Way Halo RR Lyrae, Tidal Disruption Events in Star Clusters, and Distant Quasars
Primary mentor: Yuting Feng
Faculty advisor: Prof. Raja GuhaThakurta
Other mentor: Prof. Eric Peng
Location: Remote/online
Number of interns: 3
Project description:
Despite its static appearance at first glance, the Universe is constantly changing. Monitoring the sky for these changes is time-consuming, but doing so allows us to identify unique celestial phenomena. Most images taken of the sky are not suitable for studying the “time-domain” because they are not taken with an appropriate spacing in time. The Next Generation Virgo Cluster Survey (NGVS), a deep, multi-color imaging survey of the closest cluster of galaxies, adopted an observing strategy that spaced observations for a given field over a time period of hours to years. While not designed for time-domain studies, this observing strategy allows us to look for things in the sky that change in brightness. This project will focus on looking for three different types of variability, each with its own separate science question, although the technical aspects of the three are nearly identical. The three types are: (1) RR Lyrae variable stars in the outskirts of our Milky Way galaxy, excellent probes of our Galaxy’s assembly history via the cannibalism of smaller galaxies, (2) tidal disruption events of stars in globular clusters by their central intermediate mass black holes, and (3) variability of distant quasars caused by stochastic accretion of material onto the supermassive black holes that power them.
Tasks:
The SIP interns will use deep time series imaging of the sky to identify variable stars that could be RR Lyrae or quasars, using a set of known RR Lyrae and known quasars as training sets. They will first use colors to identify possible RR Lyrae and quasar candidates, and then determine the brightnesses of these candidates as a function of time. While there is no training set for tidal disruption events (TDEs), there are clear theoretical expectations for the time behavior of such events. This project will require the use of image analysis tools to measure brightnesses of individual stars, quasars, and star clusters. The SIP interns will develop computer scripts to do the data analysis in an automated fashion. They will then develop tests for variability, and fit RR Lyrae and TDE light curves to the data.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Title: Photometrically Variable Stars in the Andromeda Galaxy
Primary mentor: Sagnick Mukherjee
Faculty advisor: Prof. Raja GuhaThakurta
Other mentor: Monika Soraisam
Location: Remote/online
Number of interns: 3
Project description:
The mentor’s research group has been exploring photometrically variable stars in the Andromeda galaxy (M31). Photometrically variable stars are those that undergo variations, often repeated or even strictly periodic variations, in their brightness due to pulsations. Recent large time-domain surveys (e.g., the POMME survey with the Canada-France-Hawaii Telescope and MegaCam imager) have discovered thousands of variable stars in M31. In addition, the Hubble Space Telescope (HST) has been used to carry out a large near UV/optical/near IR imaging survey called PHAT that covers a fraction of the bright disk of M31, and the mentor’s group has led a large Keck DEIMOS spectroscopic survey of M31 stars called SPLASH. The combination of variable star light curve data from time-domain observations, HST brightness and color measurements, and Keck spectra presents a unique opportunity to understand the nature of these variable stars.
Tasks:
The SIP interns will cross match variable stars found in one or more of the time-domain surveys with HST PHAT survey photometric data and Keck DEIMOS spectroscopic data. The matched data set can then be used to construct a variety of color-magnitude diagrams (CMDs) and color-color diagrams. The SIP interns will work on new ways to identify variable stars in the PHAT dataset. They will group the variable stars according to CMD location and study systematic trends in light curve properties across and within the different groups.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://sagnickm.github.io/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Proving the Evolution of Galaxy Dark Matter Content Since Cosmic Noon
Primary mentor: Jack Lonergan
Faculty advisor: Prof. Guillermo Barro
Other mentors: Prof. Elisa Toloba, Prof. Raja GuhaThakurta
Location: Remote/online
Number of interns: 3
Project description:
The goal of this research project is to learn about the kinematics, stellar ages, and dark matter content of distant galaxies observed at cosmic noon, when the Universe was only half of its current age. For this analysis, the SIP mentor and interns will make use of a large sample of deep (8+ hr) galaxy spectra taken with the state-of-the-art Keck 10-m telescope as part of the HALO7D survey. These spectra provide detailed data on the stellar continua and emission lines of galaxies which can be used to determine their stellar ages and dark matter content. Ultimately, the goal of this research project is to compare the average properties of these galaxies at cosmic noon to those of well-known galaxies in our local environment. Such comparisons will help us understand the evolution of the main galaxy properties with cosmic time.
Tasks:
For this research project, the SIP interns will use the vast collection of galaxy spectroscopic data taken as part of the Keck-based HALO7D survey. This data set will be combined with extensive photometric and structural information based on data products from the Hubble Space Telescope based GOODS and CANDELS surveys. The interns will handle images and catalogs to make diagnostic diagrams and to analyze the properties of galaxies. Furthermore, the interns will use spectral co-addition and spectral fitting techniques to extract information about the galaxies from the emission and absorption lines detected in their spectra. One such property is the dynamical mass, which is a direct probe of dark matter content. The analysis requires the use of programming scripts written in Python. However, previous programming knowledge is not required.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Globular Clusters in the Hubble Frontier Field Cluster Abell 2744
Primary mentor: Justin Barber
Faculty advisor: Prof. Elisa Toloba
Other mentor: Prof. Raja GuhaThakurta
Location: Remote/online
Number of interns: 3
Project description:
The goal of this research project is to learn about the stellar distribution and chemical properties of globular clusters (GCs; small groups of stars that orbit around a galaxy) in the Pandora cluster of galaxies (large collection of gravitationally bound galaxies). GCs are fossil records of the violent interactions that shaped these massive galaxy clusters and the galaxies in them. The goal of this study is to gain new insight of cluster formation processes.
Tasks:
The SIP interns will use the deepest images that the Hubble Space Telescope (HST) has taken for any clusters of galaxies, the so-called Hubble Frontier Fields. The mentor’s research team has catalogs of all the objects found in these very deep HST images. The interns will use these catalogs to distinguish between different kinds of objects: galaxies in the cluster, galaxies in the background of the cluster, and GCs in the cluster of galaxies. Once the samples are separated, the SIP interns will analyze the properties of these GCs using density plots, color-magnitude, and color-color diagrams.
Required skills for interns prior to acceptance: Computer programming; statistical data analysis
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: The Globular Cluster Systems of Virgo Cluster Dwarf Galaxies
Primary mentor: Prof. Eric Peng
Other mentors: Prof. Raja GuhaThakurta, Kaixiang Wang, Youkyung Ko
Location: Remote/online
Number of interns: 3
Project description:
Star clusters are collections of thousands to millions of stars formed together and still bound together by their gravity. The oldest and most massive star clusters are called “globular clusters” (GCs) for their round appearance. GCs can be seen at much greater distances than individual stars, because they shine with the combined luminosity of many stars coming from a relatively small amount of volume. This project will look for GCs around low-luminosity galaxies in the nearest cluster of galaxies, the Virgo cluster. One of the challenges in finding star clusters around nearby galaxies is that the brightness of the galaxy itself gets in the way. In the project, the SIP interns and mentors will model the smooth galaxy light and subtract it from the images in order to better find faint star clusters.
Tasks:
The SIP interns will work with images of galaxies in the Virgo cluster from the Next Generation Virgo cluster Survey (NGVS), a deep survey of the entire Virgo cluster with the Canada-France-Hawaii Telescope (CFHT). Using cutout images of the galaxies, the interns will use custom software (IRAF’s ELLIPSE and its modification, ISOFIT) to model the galaxy light and subtract it from the images. After the subtraction of the galaxy light, the SIP interns will use the SExtractor (Source Extractor) software to find GCs in the image.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://kiaa.pku.edu.cn/~peng
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Gamma-Ray Analysis of the Most Energetic Blazars to Probe the Cosmos
Primary mentor: Dr. Olivier Hervet
Faculty advisor: Prof. David Williams
Location: Remote/online
Number of interns: 3
Project description:
Supermassive black holes at the center of distant galaxies can be very powerful factories of gamma rays. Along their journey toward the Earth, a fraction of these gamma rays are absorbed by the optical-infrared radiation field bathing the Universe (a.k.a. “Extragalactic Background Light”, or EBL). By quantifying this absorption on a sample of the brightest gamma-ray blazars, one can measure the EBL density and deduce information on the global composition and evolution of the Universe. As a first step, the SIP interns will extract gamma-ray spectra from data obtained by the NASA’s Fermi-LAT Space Telescope. Their results will contribute to the creation of a map of the EBL density across the sky and the quantification of possible anisotropies.
Tasks:
The SIP interns will work on gamma-ray data collected by the NASA space telescope Fermi-LAT, on a sample of bright gamma-ray blazars selected by the mentor. The interns will perform a full gamma-ray analysis of approximately 12 years of cumulated observations to produce the best possible gamma-ray spectrum for each of the selected sources. With the mentor’s support, the interns will carefully follow the different analysis steps from raw data to clean, scientifically workable, results. By working on a local computer cluster at SCIPP, the SIP interns will develop skills on Linux-bash commands and Python scripts. The interns will also get insights into statistical data analysis, astrophysical ideas, and the operation of a gamma-ray telescope. They will need UCSC sundry accounts with the ability to connect to the UCSC VPN in order to have secure access to computer servers on the campus network.
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://fermi.gsfc.nasa.gov/science/eteu/ebl/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	OFF	ON	ON	ON	ON

Title: Optimizing Analysis of Very High-Energy Gamma-Ray Data
Primary mentor: Prof. David Williams
Other mentors: Dr. Olivier Hervet, Prof. Amy Furniss
Location: Remote/online
Number of interns: 3
Project description:
Very high-energy (VHE) gamma rays are used to study some of the most powerful systems in the Universe, including pulsars, supernova remnants, and the black holes at the centers of galaxies. They are also the most energetic form of electromagnetic radiation observed from astrophysical sources, a trillion times more energetic than optical light and a million times more energetic than X-rays. Using data from the VERITAS VHE gamma-ray telescopes, the SIP interns will investigate several ideas for improving the way the data are analyzed, with the goal to develop analysis methods that are more sensitive. These studies will primarily use data from the Crab Nebula, a strong and steady VHE gamma-ray source powered by the Crab Pulsar. If time allows, the methods developed may be applied to some other gamma-ray sources of interest.
Tasks:
The SIP interns will learn to use computer programs for analyzing the VERITAS data and run the analysis on data sets from one or more of the objects of interest. The interns will also learn to inspect the output of programs which test the VERITAS data quality in order to remove poor-quality data (usually the result of bad weather) from the sample. They will compare different ways of doing the analysis in order to identify an optimal approach that gives the best (in the sense of most definitive) results. In doing so, the SIP interns will gain familiarity with standard tools used for astrophysics and particle physics data analysis and with working in the Linux computing environment. The interns will need UCSC sundry accounts with the ability to connect to the UCSC VPN in order to have secure access to computer servers on the campus network.
Required skills for interns prior to acceptance: Some familiarity with computer programming preferred
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://scipp.ucsc.edu/~daw/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Weak CN Stars, Carbon Stars, and Other Exotic Stars in M31 and M33
Primary mentor: Caelum Rodriguez
Faculty advisor: Prof. Raja GuhaThakurta
Other mentors: Antara Bhattacharya
Location: Remote/online
Number of interns: 4
Project description:
The Andromeda galaxy (M31), the nearest galaxy larger than our own galaxy, and its companion the Triangulum galaxy (M33) serve as an excellent laboratories for the study of stellar populations including rare stars. Carbon stars constitute one such class of rare stars. The distinguishing characteristic of these stars is their atmosphere contains carbonaceous molecules such as CN, CH, and C_2 that make their presence known via broad absorption bands in the spectra of these stars. The mentor’s research group, working with previous SIP interns, has discovered a new class of rare stars called “weak CN” stars in which the CN spectral absorption feature at about 8000 Angstrom is much weaker than in the spectra of carbon stars. The initial discovery/classification of the weak CN stars was based on visual inspection of spectra and the group has since been working on automated classification with the goal of using state-of-the-art machine learning methods. Other rare stars in M31 and M33 include two classes of emission line stars.
Tasks:
The SIP interns will analyze 1D spectra obtained with the DEIMOS spectrograph on the Keck II 10-meter telescope. The interns will work with visually-classified and machine-classified populations of rare stars in M31 and M33. The interns will use existing Python software and write custom software to analyze and compare these M31 and M33 samples in terms of the following diagnostics: various HST-based color-magnitude diagrams (with theoretical stellar tracks overlaid), fraction relative to normal oxygen-rich stars, co-added Keck DEIMOS spectra, kinematics (line-of-sight velocity dispersion and asymmetric drift relative to neutral hydrogen), etc.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://www.astro.ucsc.edu/faculty/index.php?uid=pguhatha
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Galactic Evolution Through the Far-Ultraviolet Lens
Primary mentor: Sara Crandall
Faculty advisor: Prof. Graeme Smith
Location: Remote/online
Number of interns: 2
Project description:
In this research project, the SIP interns will be astronomical “archeologists”, looking into the Universe’s past to understand the Milky Way’s evolution. Contradictions in how astronomers interpret the evolution of our Galaxy often stem from difficulties in determining stellar ages. The interns will learn how to use far-ultraviolet observations from the Galaxy Evolution Explorer (GALEX) space-based telescope to estimate stellar ages. With stellar ages in hand, the mentor and interns will test two important relations that give us insight into the Milky Way’s evolution: the age-velocity relation and the age-metallicity relation. The nature of these two relations is currently contentious. However, with far-ultraviolet—determined ages, the mentor and interns will test these relations with recently innovated methods.
Tasks:
The SIP interns will use programming tools to calibrate a relationship between age and GALEX telescope far-ultraviolet observations. This calibration will be used to test the age-velocity relation. In addition, the interns will use programming tools to search online databases and find an overlapping sample of stars with observations taken by GALEX and stars with metallicities in the the Geneva-Copenhagen Survey. This sample will be used to test the age-metallicity relation.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Identifying Exoplanets with Detectable Precession Rates with Dynamical and Light Curve Modeling of Multi-Planet Systems
Primary mentor: Patrick Maragos
Faculty advisor: Prof. Daniel Jontof-Hutter
Other mentors: Prof. Elisa Toloba, Prof. Raja GuhaThakurta
Location: Remote/online
Number of interns: 2
Project description:
Exoplanet transit surveys like Kepler and TESS have discovered hundreds of compact multi-planet systems, in which several planets are in a transiting configuration. As a planet’s orbit precesses due to the gravity of its neighbors, its transit impact parameter may change by a detectable amount over time. In this research project, the SIP mentor and interns will calculate the rate of change of impact parameter for hypothetical systems using N-body simulations, and use the results to identify systems of potentially detectable changes in impact parameter from Kepler and TESS. Future transit light curves of these systems may constrain the planetary masses or hint at additional undetected planets.
Tasks:
The SIP interns will do the following: (1) explore the effect of orbital periods on transit light curve; (2) explore the effects of orbital-to-stellar radius ratio, inclination, eccentricity, and argument of periastron on the transit light curve; (3) write code to convert inputs for the Batman Python package into impact parameter, and measure changes in transit depth and transit duration from Batman output; (4) learn to construct dynamical models of planets with the Rebound software; (5) get a simple simulation running and print out orbital elements over time; (6) write code to measure inclination variations from simulation output, plot inclination and impact parameter variations over time, and give examples of transit curves with Batman; and (7) apply these codes to some known exoplanet systems.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Evolved Stars with Weak Paschen Series Absorption and Small Proper Motions in the HALO7D Survey
Primary mentor: Kevin McKinnon
Faculty advisors: Prof. Raja GuhaThakurta
Location: Remote/online
Number of interns: 3
Project description:
The mentor’s research group is the driving force behind the HALO7D survey, which measures 3D positions, 3D velocities, and chemical properties of stars in the Milky Way’s stellar halo. The overarching goals of the HALO7D survey are to measure the dark matter content and map the accretion history of our Milky Way galaxy. The survey data consists of proper motion and angular position measurements from Hubble Space Telescope (HST) imaging/astrometry and radial velocity and chemical abundance measurements from Keck/DEIMOS spectroscopy. In the dataset, a handful of stars were measured to have small proper motions and observed to have weak Paschen series absorption in their spectra, implying that these stars are evolved (e.g., red giant branch or horizontal branch stars) and significantly more distant than the typical star in the HALO7D survey. This research project will investigate the properties of these rare and unusual stars and look for similar stars in the to-be-analyzed HALO7D expansion dataset.
Tasks:
The SIP interns will explore stellar spectroscopic and photometric properties, especially of distant, evolved stars in the Milky Way’s remote stellar halo. The interns’ tasks will include learning the basics of stellar evolution and stellar structure, especially the physical and chemical processes that produce various absorption signatures in a stellar spectra. They will manipulate and model data from the HALO7D survey, and study a rare and interesting class of stars which could turn out to be useful tracers of the remote halo of our galaxy.
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; statistical data analysis; general data processing/manipulation; mathematical modeling; stellar astrophysics concepts
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: The Accretion History of the Milky Way: Mock Observations of Galaxy Formation Simulations, Analytical Models, and the HALO7D Survey
Primary mentor: Miranda Apfel
Faculty advisors: Prof. Raja GuhaThakurta
Location: Remote/online
Number of interns: 3
Project description:
In the Lambda-Cold Dark Matter cosmological paradigm, galaxies are embedded in massive dark matter halos and are theorized to grow by accreting smaller dwarf galaxies associated with dark matter subhalos. Clues to the history of this growth can be found by studying the detailed properties — 3D kinematics, 3D positions, and chemical abundances — of stars located in the sparse outer parts of the galaxy comprising the so-called stellar halo. The goal of this research project is to see how much one can tell about the growth of our Galaxy’s stellar halo given different types and amounts of data.
Tasks:
The SIP interns will use a software package called Galaxia to construct mock observations of realistic numerical simulations of galaxies with different accretion histories. The interns will also explore the Besançon analytical model of our Milky Way galaxy. They will then analyze these results using custom Python software, and use them to make statistical predictions about what galaxies with different accretion histories will look like now, and how much observational data is required to distinguish between different accretion histories. The interns will compare these predictions to data obtained with Hubble Space Telescope and Keck telescope. The SIP interns will learn about how telescope data are processed and turned into information such as proper motion, radial velocity, etc.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Spectroscopy of Milky Way Halo Stars in COSMOS: Dynamics, Dark Matter, Accretion History, and Chemical Enrichment
Primary mentor: Prof. Raja GuhaThakurta
Secondary mentor: Kevin McKinnon
Location: Remote/online
Number of interns: 2
Project description:
The Milky Way galaxy which we call home is embedded within a vast and massive halo that is composed of mostly dark matter with a (literally!) light frosting of old, chemically anemic stars. The 3D velocities and surface chemical composition of these stars contains information about the accretion history, dark matter content, and chemical enrichment history of our Galaxy. The mentor’s research collaboration is using precise sky positions (astrometry) of these stars from the space-based Gaia mission and Hubble Space Telescope over a 10+ year baseline to measure their proper motions, two components of their velocity. Earlier this year, the group obtained spectra of these stars using the Keck II 10-meter telescope on Mauna Kea, Hawaii and the DEIMOS spectrograph. These stellar spectra will be used to measure the third (line-of-sight or radial) velocity component of these stars and their detailed chemical abundance patterns.
Tasks:
Through this research project, the SIP interns will become intimately familiar with astronomical spectra. They will use the Interactive Data Language (IDL) program zspec to measure/vet measurements of stellar radial velocities and learn to recognize and flag residual instrumental and atmospheric artifacts. They will compare the performance of the IDL-based spec2d and Python-based PypeIt data reduction pipelines. Time permitting, the SIP interns will learn about the basics of chemical abundance measurements from stellar spectra.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://www.astro.ucsc.edu/faculty/index.php?uid=pguhatha
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Looking for Evidence of Intermediate Mass Black Holes in Star Clusters in the Virgo Cluster
Primary mentor: Vivian Tang
Faculty advisors: Prof. Raja GuhaThakurta, Prof. Piero Madau
Other mentor: Yuting Feng, Prof. Eric Peng
Location: Remote/online
Number of interns: 3
Project description:
Research over the last few decades has established that galaxies comparable in mass to the Milky Way host massive central black holes whose mass M_BH is tightly correlated with their host galaxy mass M_gal. Extrapolating this M_gal–M_BH relation down to star clusters of mass 10⁶ M_sun would suggest that star clusters should host intermediate mass black holes: M_BH ~ 10³ M_sun. However, observational evidence for/against the presence of intermediate mass black holes has remained the subject of vigorous debate. One useful observational signature of intermediate mass black holes in dense star clusters is the temporary light burst caused by a tidal disruption event (TDE): the tidal disruption of an unfortunate star that strayed too close to the black hole’s event horizon. The Next Generation Virgo Cluster Survey (NGVS) has obtained repeat brightness measurements of tens of thousands of star clusters in the Virgo cluster of galaxies. This research project will be the theoretical counterpart to the observational TDE component of the SIP 2020 research project AST-03.
Tasks:
The SIP interns will carry out the following set of theoretical calculations under the close guidance of the primary and secondary mentors: (1) trying out different slopes for the M_gal–M_BH relation to predict the abundance and mass of black holes in Virgo star clusters, (2) probability of TDEs given the density of stars in the vicinity of the central black hole, and (3) simulating the cadence of the NGVS observations on the theoretically predicted set of TDEs.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://www.ucolick.org/~pmadau/Research_Highlights.html
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Kinematics, Structure, and Time Evolution of the Stellar Disks of the Andromeda and Triangulum Galaxies
Primary mentor: Amanda Quirk
Faculty advisor: Prof. Raja GuhaThakurta
Other mentor: Dr. Mark Fardal
Location: Remote/online
Number of interns: 3
Project description:
In the widely accepted Lambda Cold Dark Matter paradigm for structure formation in the Universe, galactic disks form via the dissipational collapse of gas within large massive dark matter halos. The model does a good job of explaining the general properties of disks (e.g., scaling relations), but several key questions remain. This research project addresses the question of dynamical heating of disks — i.e., the puffing up of disk thickness and increase of velocity dispersion over time. The mentor’s research group has assembled a large and rich data set comprised of Hubble Space Telescope (HST) photometry and Keck DEIMOS spectroscopy of tens of thousands of stars in the nearby Andromeda (M31) and Triangulum (M33) disk galaxies. The SIP interns and mentors will use this M31 and M33 data set to test the latest theories about the kinematics, spatial structure, and temporal evolution of galactic disks.
Tasks:
The SIP interns tasks will fall into two categories: (1) analysis of serendipitous sources in the 2D spectra — making astrometric measurements, cross-matching these spectroscopic sources to the HST source catalog to collate spectral type information, velocity, and photometry, and combining the serendipitous sources with the target list; and (2) Monte Carlo modeling of the finite scale height of the stellar disk and fitting the model to the full stellar velocity distribution as a function of sky position.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://www.astro.ucsc.edu/faculty/index.php?uid=pguhatha
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Targeted Sequencing for Studying 1q21
Primary mentor: Colleen Bosworth
Faculty advisor: Prof. Sofie Salama
Location: Remote/online
Number of interns: 2
Project description:
This project looks at a small piece of the genome called 1q21. Mutations in this region are strongly associated with a variety of developmental disorders including autism, congenital heart disease, and certain cancers. To study these disorders, the mentor’s research group needs high quality sequencing data using both long (e.g., Oxford Nanopore) and short (e.g., Illumina) reads. The SIP interns will be designing targeted sequencing techniques to study 1q21 and identify deleterious mutations in patient and normal samples.
Tasks:
The SIP interns will be working hands on in a molecular biology lab, growing human lymphoblast cells, extracting gDNA, running electrophoresis gels, and depleting off-target DNA from their samples. Interns will be performing library preps on their samples. They will have access to the UCSC Genomics Institute’s compute cluster, where they will learn bioinformatics analysis pipelines. Interns will be expected to read assigned papers providing background on the project and to ask questions when they are unfamiliar with concepts or vocabulary they are unfamiliar with. By the end of the summer, the SIP interns will evaluate the usefulness of their targeting technique for detecting mutations in their samples.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work
URL: https://hausslergenomics.ucsc.edu/
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: DNA/RNA Modification Detection Algorithms Comparison
Primary mentor: Andrew Bailey
Faculty advisor: Prof. Benedict Paten
Location: Remote/online
Number of interns: 3
Project description:
Cell regulation depends on interactions between proteins and nucleic acids like DNA or RNA. Understanding the specific chemistry of DNA and RNA is crucial for understanding cell function. For example, we determine the DNA and RNA sequence of cancer cells in order to determine what genes have been mutated. Standard DNA/RNA sequencing determines the order of the “canonical” nucleotides adenines (A), thymines (T), guanines (G) and cytosines (C). However, there are “non-canonical” nucleotides which traditional sequencing platforms cannot detect but nanopore sequencing can detect. The mentor is interested in using nanopore sequencing to detect these “non-canonical” or “modified” nucleotides.
Tasks:
This project asks SIP interns to compare the state of the art nanopore sequencing modification detection tools. Interns will learn the mechanics of nanopore sequencing and how this sequencing platform is different than other sequencing platforms. SIP interns will learn about the importance of modified nucleotides for cell regulatory function. During this process, SIP interns will be installing software on servers, analyzing sequencing data and comparing accuracy metrics between the various software tools. The plan is to spend the first 3–5 weeks getting up to speed on the technology, tools and basic computer skills required. As long as interns are showing progress, the final half of the program will allow the interns more flexibility to work when and where they want.
Required skills for interns prior to acceptance: Computer programming; statistical data analysis
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Machine Learning in Biology
Primary mentor: Brian Mullen
Faculty advisor: Prof. James Ackman
Location: Remote/online
Number of interns: 3
Project description:
Development of functional brain regions has been shown to be associated with spontaneous and sensory signals throughout the nervous system. The mentor’s research group is attempting to map brain regions throughout development. One facet of mapping involves understanding how an animal is behaving. This research project will use computer vision (openCV) packages available to Python to track and identify mouse movements. The mentor and SIP interns will use Machine Learning algorithms (scikit-klearn) to build a classifier to determine the state of an animal. Ultimately, the SIP interns and the mentor will use their results to correlate with brain activity at various stages of development. This will give insight into how experience influences brain function.
Tasks:
The SIP interns will go through a complete Machine Learning project to classify the state of the animal based on videos of the body acquired while the researchers took brain recordings. First, the interns will hand-score videos that they will use to train the classifier. Second, the interns will determine, explore, and select appropriate metrics. Third, the interns will train classifiers using several methods. Finally, the interns will test the classifier to determine the efficacy of their machine learning task, looking at the quality of their classifier. If time permits, the SIP interns can start to assess how the brain functionally behaves during these different states and explore methods of analysis.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Genetic Sequencing Accuracy/Error Models
Primary mentor: Letitia Mueller
Faculty advisor: Prof. Melissa Cline
Location: Remote/online
Number of interns: 3
Project description:
Currently, the mentor is working in a lab that is focused on breast, ovarian, and colorectal cancer research. The mentor would love to have the SIP interns join her on a subproject about testing the role of mutational signatures in categorizing genetic variants of uncertain significance (VUS). Currently, roughly one-third of the germline variants are classified as VUS. Because the prognosis of these variants is uncertain, the test reports can lead to patient distress, and clinical mismanagement. These points underscore the need for high-quality interpretation of VUS. The mentor’s research group is interested in understanding whether mutational signatures may add to the evidence gathering to classify variants of uncertain significance, to ultimately classify them as pathogenic or benign.
Tasks:
The SIP interns will assist in analyzing and interpreting patient data. This work is usually done on a remote server, and by manipulating the data in a program written in Python. Although the work will be based on writing Python programs, the mentor is happy to take on interns who have little to no programming experience. The mentor is looking for interns who have a can-do attitude and a genuine interest in bioinformatics!
Required skills for interns prior to acceptance: Computer programming and data analysis experience preferred but not required
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: SARS-COVID-19 Variants Study
Primary mentor: Gepoliano Chaves
Faculty advisor: Prof. Nader Pourmand
Location: Remote/online
Number of interns: 3
Project description:
In the past, the mentor has been working with genomic/genetic variations implicated in Huntington’s disease, a debilitating neurodegenerative disease with overlapping molecular mechanisms with metabolic conditions such as diabetes. Due to the present outbreak of COVID-19, the research group led by Prof. Nader Pourmand was approached and asked about the possibility of developing a DNA-sequencing platform to detect the Coronavirus based on a DNA-sequencing method called NGS, Next Generation Sequencing. In this research project, the mentor intends to use some of the knowledge that he has gained in working on the development of a Coronavirus detection platform to present the SIP interns with basic strategies to study the genome of the virus. These include the design of primers to amplify genomic regions of interest and the notion of variant call, identifying Single Nucleotide Polymorphisms (SNPs), which can explain the inter-species jumps of viral strains making them prone to infect different species.
Tasks:
The SIP interns will work on the following tasks: (1) primer design for genomic region amplification; and (2) identification of Single Nucleotide Polymorphisms in the genome of the Coronavirus.
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; protein engineering
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	OFF	OFF

Title: Rip Current Detection: A Machine Learning Approach
Primary mentor: Akila De Silva
Faculty advisor: Prof. Alex Pang
Location: Remote/online
Number of interns: 3
Project description:
Rip currents are the main beach hazard affecting beachgoers who could even face death as a result. The mentor is currently working on building an application, using artificial intelligence (AI) and machine learning (ML), that could easily detect and visualize potentially hazardous rip currents. This summer, the mentor and SIP interns will use ML and AI techniques to classify coastal images as rip current or non-rip current images. Furthermore, during this classification process, the SIP interns will visualize what unique features could be used to identify images of rip currents from non-rip currents. Finally, if time permits, the SIP interns will gain exposure to detection and localization of rip currents in a coastal image.
Tasks:
The SIP interns will: (1) learn to use Python for programming; (2) gain exposure to machine learning frameworks such as TensorFlow and Keras; (3) gain exposure on how to collect data from online resources; (4) gain exposure to multiple ML/AI techniques for image classification; (5) gain exposure to detect and localize objects in an image (if time permits); (6) learn how to critically read reserach papers; and (7) learn how to be effective in a team research environment.
Required skills for interns prior to acceptance: Computer programming; statistical data analysis
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://users.soe.ucsc.edu/~audesilv/
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Fluid Flow Pattern Analysis and Visualization
Primary mentor: Fahim Hasan Khan
Faculty advisor: Prof. Alex Pang
Location: Remote/online
Number of interns: 3
Project description:
Flow visualization (FlowVis) is a subfield of scientific visualization and closely associated with computer graphics. Most fluids (gases and liquids such as air, water, etc.) are transparent, and their flow patterns are invisible to the human eyes without methods to make them visible. Flow visualization is the process of making the physics of fluid flow directly accessible to visual perception by making the flow patterns visible to get qualitative or quantitative information on them. These flow visualizations are often rendered using the same 2D and 3D computer graphics pipelines used for movies, games, and related applications thus making them closely associated with computer graphics. The mentor’s research group works in the field of scientific visualization and computer graphics. One of his research focus involves the challenges of analyzing and visualizing time-varying 3D flow in an efficient manner for various real-life applications. One of the critical tasks in flow analysis and visualization is optimally utilizing the graphics processing unit (GPU) of computational devices. The GPU plays two different crucial roles in this process, (1) using GPGPU (General-purpose computing on graphics processing units) for faster processing of huge amount of flow data, and, (2) rendering the high-quality 3D graphics for visualizing the flow. This research group is currently working towards the goal of developing an iPhone/Android app to analyze and visualize rip current patterns from live video of a cellphone camera in a superimposed fashion. This app will render the visual information of the flow pattern directly on the live video, effectively converting mobile devices to visual analysis tools to be used by surfers and swimmers in real life.
Tasks:
The SIP interns will be involved in a research project for superimposed visualization of flow pattern analysis. They will learn basic programming using Python and/or C++, do literature reviews on a topic and read related research papers, and work on an academic research project. The interns will practice extensive and effective use of Google and other online tools to solve programming problems. The SIP interns will have exposure to a few visualization software tools, the 3D graphics development pipeline for developing visualization applications, and the use of GPU for both computations (GPGPU) and rendering (3D graphics). Depending on their level of expertise, the interns will participate in the development of the iPhone/Android app for rip current visualization. If time permits, the interns will have exposure to some machine learning techniques for pattern analysis of flow data. Some previous experience with programming is preferred, but not required.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://www.soe.ucsc.edu/people/fkhan4
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Exploring Distributed Learning Paradigms
Primary mentor: Harikrishna Kuttivelil
Faculty advisor: Prof. Katia Obraczka
Location: Remote/online
Number of interns: 3
Project description:
Machine learning is a field that is proliferating and drawing in high interest and investment as it continues to diversify to meet the demands of its various applications. Recently, factors including privacy concerns, increasing prevalence and computational ability of personal devices, the proliferation of IoT devices, and the general trend of decentralization in technology have led to the increased research and development of distributed learning paradigms. This research project applies networking principles to validate the principles of federated learning and extend it into decentralized learning approaches while using real, low-cost devices to form a network and collectively gather data and learn from all nodes within the system.
Tasks:
The SIP interns will first learn about the basics of machine learning and implementing machine learning algorithms within Google’s TensorFlow platform. Then interns will assist the mentor in configuring Raspberry Pi devices for TensorFlow, and then assess the performance of these devices in handling basic machine learning tasks. The interns will then learn about the basics of distributed machine learning. They will help organize multiple Raspberry Pi devices into a network and support the mentor to develop communication protocols efficient for the intended application. Finally, the interns will get a chance to experiment with different variations of distributed learning by implementing such schemes on the Raspberry Pi network and analyzing the results of the experiments. Throughout the process, the SIP interns will also aid the mentor in collecting, labeling, and processing data to use for the tests. The interns will gain skills in research, programming, applying machine learning, distributed systems, networking, and data processing.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Data Prefetching in Hardware
Primary mentor: Peter Braun
Faculty advisor: Prof. Heiner Litz
Location: Remote/online
Number of interns: 2
Project description:
As CPUs become faster and more powerful, the memory becomes a greater bottleneck in the traditional von Neumann computer architecture. If a program requires some data that is not already in cache, it must wait 100x longer to receive it than the time it takes to access cached data. This can lead to a significant increase in program runtime. One approach to this problem is data prefetching. A prefetching algorithm generally observes the sequence of memory accesses and predicts what memory address will be needed in the near future, prefetching it to cache. Current prefetching algorithms do not perform well on certain important classes of applications. This motivates the study of why these algorithms perform poorly. Are there patterns within the memory access stream that are not currently understood and exploited? Can one develop a general prefetching algorithm, perhaps leveraging recent advances in machine learning?
Tasks:
The SIP interns will collect instruction traces for a variety of applications. The interns will compare characteristics of the trace with the full application and determine which subsection of the full trace is representative of the full program using a microarchitectural simulator. This will include adapting scripts written in Bash/Python, using the Linux command line, and learning fundamental computer architecture principles.
Required skills for interns prior to acceptance: Computer programming; Linux
Skills interns will acquire/hone: Computer programming; Linux; Bash scripting; Python; computer architecture
URL: https://people.ucsc.edu/~hlitz/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Neural Data-toText Generation
Primary mentor: Rongwen Zhao
Faculty advisor: Prof. Alex Pang
Location: Remote/online
Number of interns: 3
Project description:
Data-to-text is one of the key tasks in natural language generation. It aims to generate meaningful and fluent natural language text from non-linguistic input data. In this research project, given a set of ⟨ Subject, Predicate, Object ⟩ RDF triples, the SIP mentor and interns will try to build a system which can generate a single sentence or a sequence of sentences describing these triplets explicitly. Traditionally, most approaches that have been designed are rule-based and too complex in general. Recently, neural methods based on deep learning (DL) have produced state-of-the-art performance in natural language generation. The SIP mentor and interns will build a neural network utilizing different latest language models. Finally, the system will be tested on benchmark datasets with the goal of outperforming other models.
Tasks:
The SIP interns will: (1) learn how to use Python for programming; (2) gain experience in dealing with large text datasets; (3) gain exposure to popular deep learning frameworks, e.g., PyTorch; (4) gain exposure to the latest language models for natural language generation; (5) learn how to build and train a neural network for text generation; (6) learn how to read related research papers; and, most importantly, (7) work collaboratively as a team.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://webnlg-challenge.loria.fr
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Neural Style Transfer (Transferring Style from Famous Paintings)
Primary mentor: Saeed Kargar
Faculty advisor: Prof. Faisal Nawab
Location: Remote/online
Number of interns: 4
Project description:
One of the biggest developments in deep-learning-driven image modification is Neural Style Transfer (NST), introduced by Leon Gatys et al. (2015). NST refers to a class of software algorithms that manipulate digital images or videos to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks in order to perform the image transformation. Common uses for NST are the creation of artificial artwork from photographs, for example by transferring the appearance of famous paintings to user supplied photographs. Several notable mobile apps use NST techniques for this purpose, including DeepArt and Prisma. This summer, the mentor’s research group will apply an advanced machine learning technique to adapt the appearance or visual style of one image to another. The SIP interns will learn various deep learning concepts and tools — e.g., using the Keras library, pre-trained models such as the VGG19 network, and popular online tools such as Google Colab to solve programming problems. The mentor will work with the interns to implement an advanced technique of a research paper from scratch. The SIP interns will learn how to read a research paper and implement it, and will learn one of the most advanced concepts in deep learning territory. Finally, if time permits, the SIP interns will gain exposure to applying NST on video datasets too.
Tasks:
The SIP interns will gain exposure to: (1) Python programming; (2) machine learning frameworks such as TensorFlow and Keras; (3) data collection from online resources; (4) one of the most recent and advanced concepts in deep learning; (5) application of NST to image datasets (6) application of NST to video datasets (if time permits); (7) critical reading of research papers; and (8) collaborating effectively in a team research environment.
Required skills for interns prior to acceptance: Computer programming (familiarity with Python and Google colab)
Skills interns will acquire/hone: Computer programming in Python; machine learning and deep learning
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Question Answering Data Collection and Analysis
Primary mentor: Geetanjali Rakshit
Faculty advisor: Prof. Jeffrey Flanigan
Location: Remote/online
Number of interns: 3
Project description:
Natural language processing is about making computers learn language. It encompasses a lot of exciting problems like algorithms to teach a computer to translate input from one language to another, for example, English to French (machine translation), have a computer predict if a restaurant review written by someone is positive or negative (sentiment analysis), and so on. The goal of this research project is to build automated question answering systems/models that have a deeper understanding of the text in which to look for answers to the question asked by a user, similar to how humans might do it in a reading comprehension task. To this end, the mentor’s research group collects good-quality crowdsourced data, and investigates the quality and suitability of the data for use in building question answering models that are more interpretable.
Tasks:
The SIP interns working on this research project may help with collecting crowdsourced data from question answering/reading comprehension tasks. The main focus will be on analyzing these data, and creating automated tests to check the quality of data that are collected using crowdsourcing and their suitability for use in the question answering problem at hand. The interns will learn to program in Python, work with real world datasets, understand relevant concepts from natural language processing, and see these concepts in action. Based on the level of interest and preparedness of the interns, the mentor and interns may also do some statistical analysis of the data, to extract recurring patterns and cues helpful for finding answers, and possibly some machine learning/deep learning.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: http://users.soe.ucsc.edu/~geet
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Origami Robot: Modeling and Simulation
Primary mentor: Samira Zare
Faculty advisor: Prof. Mircea Teodorescu
Location: Remote/online
Number of interns: 3
Project description:
Origami is a newly emerging field in robotics that can help when limited space is available. They have many applications, from solar panels to medical devices. These deployable structures are able to move and change their shapes and structures based on their environment. For instance, solar origami panels can become compact to transfer and then deploy to their final structure. The mentor’s research group designs, models, and develops a dynamical simulation in Autodesk Inventor and uses MATLAB to analyze and understand their movements.
Tasks:
The SIP interns’ primary tasks will be learning Autodesk Inventor and MATLAB. Their secondary tasks will be to come up with Origami design ideas and apply a dynamical simulation to understand them. Finding an origami design that can be applied to real-world problems could be challenging. They can be too complex to control and model or too simple to perform any movement.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming (MATLAB, Python); statistical data analysis; 3D modeling; simulation
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Game Engine Based Simulation for Autonomous Vehicles
Primary mentor: Abdul Jawad
Faculty advisor: Prof. Jim Whitehead
Location: Remote/online
Number of interns: 3
Project description:
Rigorous testing is mandatory for autonomous vehicles to make them safe before we employ them in the real world. The goal of this research project is to use simulation technology in testing autonomous vehicles (creating realistic 3D environments and naturalistic non-player character behavior from other cars). Specifically, the mentor’s resaerch group uses Unreal Engine (widely used game engine) and Behavior Tree (widely used in AAA games) for simulating naturalistic car behavior. This research project is developed in C++.
Tasks:
The SIP interns’ task will include: (1) learning how to code in C++ in Unreal Engine; and (2) making a user interface for the designer to use the behavior tree tool. Overall, the interns will learn to work with Unreal Engine, build a plugin for the engine, and gain exposure to collaborative research work. They will also learn how to effectively search into the documentation. Finally, the SIP interns will gain exposure to the current research effort on autonomous vehicles.
Required skills for interns prior to acceptance: Computer programming
Skills interns will acquire/hone: Computer programming; UI design
URL: https://github.com/AugmentedDesignLab/CarBehaviorTree
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Mechanisms for Kelp Forest Resiliency
Primary mentor: Joshua Smith
Faculty advisor: Prof. Mark Carr
Location: Remote/online
Number of interns: 4
Project description:
In kelp forests along the central coast of California, active sea urchin grazing has shifted a once continuous kelp forest landscape to underwater ‘sea urchin barrens’ that are void of kelp and associated species. The mentor’s current research focuses on the processes responsible for these shifts from forested to barrens states and the recovery (i.e., resilience) of the forested ecosystem. The mentor’s research group explores sea urchin grazing behavior that has led to widespread kelp forest loss and how factors such as predators, disease, and disturbance might contribute to sea urchin population control and the recovery of kelp forest ecosystems.
Tasks:
The primary remote responsibility for SIP interns is three-fold: (1) improve the design of a machine-learning based program to quantify consumed algae particles in sea urchin diets (basic knowledge of a computer programming language such as Python, Java, and/or R is preferred); (2) conduct data analyses on long-term datasets collected through subtidal surveys; and (3) analyze high-resolution photo quadrats collected from subtidal surveys. The interns will be responsible for collecting data on the presence of invertebrates and algae from photo plots. Overall, the interns will gain skills and experience in: field research, laboratory safety and training, experimental design, and in applying novel technology to solving ecological problems. The final SIP product will hopefully culminate in a peer reviewed publication in a scientific journal.
Required skills for interns prior to acceptance: Computer programming (strongly emphasized)
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis; field work; application of novel technologies to solving ecological problems (e.g., machine learning, particle recognition)
URL: http://www.joshuagsmith.com
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Heating Up the Battle of the Sexes: Temperature Effects on Reproductive Behavior
Primary mentor: Doriane Weiler
Faculty advisor: Prof. Suzanne Alonzo
Location: Remote/online
Number of interns: 3
Project description:
Rising global temperatures are one of many pervasive effects of anthropogenic climate change. Temperature shapes the rate of biochemical processes and has strong impacts on animal physiology and behavior. However, little research has been dedicated to understanding how chronic warming impacts reproductive behavior. This project uses western mosquitofish (Gambusia affinis) as a model system to explore how temperature shapes the evolution of male-female interactions. Mosquitofish are an invasive species of freshwater fish that have been widely introduced to consume mosquito larvae. Their mating system is characterized by persistent male mating attempts – males spend 70%–90% of their time pursuing females! While this behavior benefits males by increasing their reproductive success, it can be very costly for females, which can evolve special traits to avoid males, such as greater swimming speeds. Temperature may intensify or weaken this evolutionary “battle of the sexes,” depending on how it impacts both male and female behavior. To understand how temperature impacts mosquitofish reproductive behavior, SIP interns will help analyze behavior videos of mosquitofish from populations across a broad thermal gradient. This research project will provide students with a strong background in animal behavior experimental design and analysis.
Tasks:
The SIP interns will primarily assist with: (1) analyzing behavior videos to record male and female mosquitofish behavior at different temperatures, and (2) analyzing fish photographs using ImageJ software to measure morphological traits such as body length and pigmentation. Over the course of this project, the SIP interns will gain an integrative perspective on studying animal behavior and will have a genuine experience working on many aspects of behavioral research, from experimental design to data analysis. This research project is an ideal fit for interns who are interested in animal behavior, fish, aquatic science, and/or ecology.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis; field work
URL: https://doriweiler.wordpress.com/, https://alonzo.sites.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Hawksbill Sea Turtle Ecology and Conservation in the Gulf of California, Mexico
Primary mentor: Luli Martinez Estevez
Faculty advisor: Prof. Don A. Croll
Location: Remote/online
Number of interns: 3
Project description:
Hawksbill sea turtles are critically endangered worldwide. The Eastern Pacific population, which distributes between Mexico and Ecuador, is the most threatened hawksbill population. Unlike other global hawksbill populations that tend to forage on coral reefs, Eastern Pacific hawksbill turtles use mangrove estuaries for foraging and nesting. These habitats are also crucial for small scale fisheries in the Gulf of California, Mexico. By using different methods (i.e., acoustic and satellite telemetry, cameras, and habitat transects), this research project seeks to understand which habitats hawksbills use in the Gulf of California, how they use them, and how these habitats can be protected effectively.
Tasks:
This research project will give the SIP interns the opportunity to learn about conservation science and sea turtle research. The interns’ work will be entirely computer-based so bringing a laptop is highly encouraged. The interns will work on two main aspects: (1) analyzing underwater photographs to determine the abundance of the most important food items for the species; and (2) collect information on the spatial conservation strategies that are contributing to the protection of the species. The interns will learn how to do an efficient web search and how to build/analyze a database with the information. In a broader sense, the interns will learn the basic concepts of Conservation Biology and sea turtle biology. They will also get a sense of the work behind field science.
Required skills for interns prior to acceptance: Spanish reading proficiency (preferred, if possible – not required)
Skills interns will acquire/hone: Statistical data analysis; efficient web search
URL: https://ccal.ucsc.edu/
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Abalone Habitat Associations
Primary mentor: Taylor White
Faculty advisor: Prof. Pete Raimondi
Location: Remote/online
Number of interns: 3
Project description:
In Southeast Alaska, pinto abalone (Haliotis kamtschatkana) are listed as a “Species of Concern” under the Endangered Species Act, yet little is known about habitat types that promote large abalone populations, individual abalone growth rates, and recruitment of young of the year. These associations likely differ across Alaska, where there are different densities of sea otters. Sea otters have a substantial effect on their environment. By voraciously consuming herbivores like sea urchins and even abalone, otters indirectly promote algal species and therefore change available habitat. The focus of this research project is to determine abalone habitat associations and shifts in these relationships across Southeast Alaska.
Tasks:
The goal of this research project is to understand drivers of dense abalone populations and to identify specific habitat and substrate types that promote population growth. To determine these relationships, SCUBA divers collected photos along underwater transects at sites across Southeastern Alaska. The SIP interns will learn how to identify algae species in these photos. Then, using ImageJ software, the interns will record algae and substrate percent cover. Finally, using JMP Pro, the interns will determine novel correlations between previously collected data on abalone densities to habitat type and abundance. Additional comparisons may include urchin biomass and sea otter presence in relation to habitat. These associations are important for the management of abalone in Alaska.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis; marine species identification
URL: https://rclab.ucsc.edu/home
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	OFF

Title: Is Quicker Sneak-Spawning a Benefit of Helping a Reproductive Competitor?
Primary mentor: Matthew Kustra
Faculty advisor: Prof. Suzanne Alonzo
Location: Remote/online
Number of interns: 2
Project description:
Alternative reproductive tactics are observed in many species, often as two distinct male types, a territory holding male and a sneaker male that sneaks mating opportunities from the territory holding male. Although the maintenance of these two discrete tactics has been a major focus of research, we have little understanding of the evolution and maintenance of more than two tactics. The ocellated wrasse (Symphodus ocellatus), a Mediterranean fish species, has three alternative male reproductive tactics. Nesting males make nests, chase away sneakers, court females, and provide all parental care. Sneaker males try to join nesting males and females during mating events. Satellite males help the nesting male by chasing away sneakers but will also compete with them by joining mating events between the nesting male and females. The mentor’s research group is investigating the benefits of being a satellite male that may help maintain this third alternative reproductive tactic. In this research project, the SIP mentor and interns will analyze behavior videos to see if satellite males are able to join spawning’s between nesting males and females faster than sneaker males.
Tasks:
The SIP interns will primarily be analyzing underwater videos of fish mating behavior by recording the time it takes for sneaker and satellite males to reach and spawn at a nest. While collecting the data from these videos, the interns will learn how to properly manage collaborative data sets. Towards the end of the summer, the SIP interns will learn how to perform basic statistical analyses and make graphs in the R programming language. Additionally, the interns will learn more about behavior and evolution through paper discussions to put this research experience in the broader context of evolution and behavioral ecology.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://mattkustra.wordpress.com/, https://alonzo.sites.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Comparing Convergence, Biases, and Data Gaps in Carnivorous Marine Mammal Feeding Strategies
Primary mentor: Jezebel Powers
Faculty advisor: Prof. Rita Mehta
Other mentor: Dr. Sarah Kienle
Location: Remote/online
Number of interns: 2
Project description:
Carnivorous marine mammals (e.g., cetaceans, pinnipeds, polar bears, and sea otters) play important roles as top predators in marine ecosystems worldwide. However, these animals can be challenging to study due to their large body size, remote and often inaccessible habitats, and lack of representation in animal care facilities. Therefore, data on the foraging ecology and behaviors of many species are lacking and/or poorly understood. One of the major research interests of the mentors is characterizing and comparing foraging strategies of carnivorous marine mammals. These data are critical for understanding the role different variables play in shaping marine mammal feeding strategies and how these animals will (and are) responding to widespread environmental changes. The goal of the SIP project is to synthesize current knowledge of marine mammal foraging ecology and behavior, as well as highlight existing biases in available data and propose areas of research that are needed to better understand the feeding biology of these marine predators as they cope with a rapidly changing environment.
Tasks:
The SIP interns working on this research project will have three main responsibilities: (1) performing systematic literature searches on carnivorous marine mammal feeding biology, including behavior, performance, habitat use, and morphology, (2) conducting systematic image and video searches of opportunistically collected observations of wild feeding behavior, and (3) analyzing feeding images and video data to obtain quantitative data on marine mammal foraging ecology and behavior. The interns will gain extensive experience in the scientific process, from gathering background information, reading and evaluating the scientific literature, conducting data and statistical analyses, and presenting scientific research to the scientific community and general public. The research from this project will be incorporated into a scientific manuscript that will be submitted to a peer reviewed journal for publication.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis; literature searching, reading, and interpreting scientific articles; data entry; photo and video data analysis; scientific writing; creating scientific illustrations, figures, and tables
URL: https://sarahskienle.wordpress.com/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Electro Plasmonic Nanoelectrode: Label Free Neurophotonics for Ultrahigh Bandwidth Brain Computer Interface
Primary mentor: Ahsan Habib
Faculty advisor: Prof. Ahmet Ali Yanik
Location: Remote/online
Number of interns: 3
Project description:
Understanding how networks of neurons perform complex computations is one of the greatest scientific, engineering, and medical challenges of the 21st century. This goal remains inaccessible within the realm of electronics and demands fundamentally new techniques with significantly improved technical capabilities. In this research project, the mentor’s research group turns to optics since light offers unprecedented (time/wavelength division) multiplexing and information-carrying capabilities. Achieving electrophysiological recordings through optical means, on the other hand, largely depends on our ability to recruit reliable electro-optic translators converting electrophysiological signals into photons. Even after decades of research, state-of-the-art translators cannot provide the high signal-to-noise ratio requirements because of the low photon counts (e.g., voltage-sensitive dyes) or low electric-field sensitivities (e.g., plasmonic nanoantenna). The mentor’s research group recently invented a novel electro-optic mechanism for the translation of electrophysiological signals into strong optical signals with remarkably high sensitivities and signal-to-noise ratios. This novel approach presents a quantum technological leap for label-free optical imaging of electric-field dynamics with high spatiotemporal resolution and can pave the way to highly efficient brain-machine interfaces.
Tasks:
The SIP interns will work on the design of an implantable electrical field probe for the detection of neural activity. It will be necessary for the interns carry out the following tasks: (1) learning basic neuroscience; (2) learning the Finite Difference Time Domain (FDTD) method; and (3) developing in vivo probes using the FDTD method.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work
URL: https://www.yaniklab.science/, URL: https://advances.sciencemag.org/content/5/10/eaav9786.abstract
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Protection of Silver-based Astronomical Telescope Mirrors Using a Single Layer of Aluminum Oxide Formed by Various Atomic Layer Deposition – Optical and Structural Assessment
Primary mentor: Jacob Sands
Faculty advisor: Prof. Nobuhiko Kobayashi
Other mentor: Brian Giraldo
Location: Remote/online
Number of interns: 3
Project description:
Increasing the durability of silver-based mirrors without compromising the optical performance has been a challenge for years in the application of astronomical telescopes. While several successful implementations of silver-based mirrors exist (e.g., the Gemini telescopes), they often suffer from sacrificing the deep blue and UV portions of the spectrum. A single layer of aluminum oxide (AlOx) formed by atomic layer deposition (ALD) will be studied to assess its potential as a protection layer for the silver-based mirrors. The optical properties and structural integrity of the mirrors prepared under various ALD process conditions will be analyzed in detail.
Tasks:
The SIP interns will participate in the following activities under the supervision of a graduate student: (1) characterize optical properties and surface morphology of aluminum oxide protection layers using spectroscopic ellipsometry and various microscopes; (2) characterize optical properties and surface morphology of the Ag-based mirrors before and after high-temperature high-humidity endurance test; and (3) simulate spectral reflectivity using the transfer matrix method in conjunction with the effective medium approximation.
Required skills for interns prior to acceptance: Some computer programming experience preferred
Skills intern will acquire/hone: Computer programming; lab work
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Fano-Resonant Biosensing Using Finite-Difference-Time-Domain (FDTD)
Primary mentor: Mustafa Mutlu
Faculty advisor: Prof. Ahmet Ali Yanik
Other mentor: Ahsan Habib
Location: Remote/online
Number of interns: 2
Project description:
Nanohole arrays (NHAs) are a class of nanostructured material consisting of nanoscale voids fabricated on the surface of a metallic material (O’Mahony, 2011). Researchers do flow-through experiments to accumulate bioparticles on top of the nanoholes and then measure the characteristic changes. Measuring the change in the light spectrum through nanoholes is a reliable method. These plasmonic nanohole arrays exhibit extraordinary light transmission (EOT) spectra and that spectra enable a few unique ways for biosensing. The SIP interns will be doing some basic simulations about nanohole arrays and their optic/plasmonic characteristics. These simulations will be compared to actual experimental data that has been collected previously.
Tasks:
The SIP interns will learn about: (1) biosensing technologies; (2) nanohole arrays (NHAs) and their use in biosensing; (3) the FDTD (finite-difference-time-domain) method; and (4) the basics of the LUMERICAL photonics software package. The interns will work with the mentor to simulate the characteristics of NHAs using the LUMERICAL photonics simulation software.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work
URL: https://www.yaniklab.science/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Improving Coastal Prairie Restoration for Increased Resilience to Drought
Primary mentor: Justin Luong
Faculty advisor: Prof. Michael Loik
Location: UCSC Main Campus
Number of interns: 4
Project description:
Ecological restoration seeks to alleviate loss of unique ecosystems through native plant reintroductions and invasive species control. However, restoration outcomes can be unpredictable and may become more so with climate change. The mentor’s research group is interested in exploring new methods to improve restoration success in coastal prairies to improve coastal ecosystem resilience to droughts. The group has planted native plant species under rain-out shelters designed to simulate a 1-in-100 year drought. The group is interested in understanding if plant traits and evolutionary relationships are predictive of plant survival and growth. The mentor’s research group is also interested in whether plant traits can explain changes in plant communities. The SIP interns will be working in a lab and will be required to complete basic lab safety training. Work will take place at the UCSC Main Campus and the Coastal Campus.
Tasks:
The SIP interns will work on analyzing previously collected leaf samples for various leaf functional traits using the free imaging software: ImageJ. Leaf samples were previously collected from numerous plants along the California Coast. Understanding how leaf traits vary along a climatic gradient or within plant families can be important for improving ecological restoration and conservation by informing which species may be best suited for their local environment. The interns will meet regularly with the mentor to learn proper measurement techniques and various lab activities. The SIP interns will read a scientific journal article once every 1 to 2 weeks for group discussion. By week 5, students will begin learning the basics of data analysis to prepare for their final SIP presentation.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work; statistical data analysis; image analysis programming

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Investigating Taste and Perspective in Conversation
Primary mentor: John Duff
Faculty advisor: Prof. Pranav Anand
Location: Remote/online
Number of interns: 2
Project description:
What do you mean when you say a slice of pizza is “tasty,” a day at the beach was “fun,” or a poem is “beautiful”? And what does it mean when a friend disagrees with you? How do we decide who is right – or can you both be right? Words like “tasty” pose big questions about how humans understand truth and personal experience, questions that are important for researchers who study the structure of meaning in language (semantics). It turns out that with the right type of analysis, we can answer these questions! The goal of this research project will be to carefully examine transcripts and recordings of people having these kinds of arguments, and the ways they are resolved. This kind of detailed investigation will help us learn more about how humans talk about truth and opinions.
Tasks:
The SIP interns will learn how to use scientific tools to study language. Parts of this will involve reading lots of conversations and writing about them. But just because the mentor’s research group studies words doesn’t mean they don’t use numbers! The interns will also use scientific software to investigate those recordings, and write code to design professional graphs. Throughout the internship, the SIP interns will also get to study language generally. The skills the interns will learn will be useful for anyone interested in linguistics, foreign languages, psychology, or computer science. In order to facilitate remote communication and contributions to the project, the SIP interns must each have daily access to a computer with a webcam, a reliable internet connection, and ideally at least two Gb of free memory. They should have the ability to install new programs on this computer (the mentor will train them in the use of programs like Praat, ELAN, and RStudio), and be comfortable sharing their screen for training purposes.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
URL: https://linguistics.ucsc.edu/about/what-is-linguistics.html
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Negative Shifting in Mainland Scandinavian Languages
Primary mentor: Myke Brinkerhoff
Faculty advisor: Prof. Ivy Sichel
Location: Remote/online
Number of interns: 2
Project description:
In many of the languages spoken around the world, words are only allowed to appear in a specific fixed order. However, languages spoken in mainland Scandinavian are a well-known exception to this constraint on word order, with pronouns being allowed to shift to a position outside of the verb phrase. In addition to pronouns shifting, negative indefinites (e.g., ‘nobody’ or ‘nothing’) are also allowed to shift to positions quite similar to the one that pronouns occupy when shifted. This research project is interested in asking why negative indefinites would behave like pronouns and do they genuinely behave the same as personal pronouns. These questions will be answered by collecting data using computer code specifically written to search through a database of Swedish speeches for instances of these negative indefinites.
Tasks:
The SIP interns will learn how to apply the scientific method to language. This means that interns will learn how to think critically, how to formulate hypotheses, and how to test those hypotheses about some aspect of language. The interns will learn how to minimize potential confounds during data collection, while also learning how to write computer code to collect that data. If there is enough time, the SIP interns will also learn how to analyze audio recordings using computer software for prosodic evidence of the phenomena under investigation. The interns will also be asked to read and discuss relevant research articles to prepare them to think critically about the data.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; linguistic analysis; data analysisLab work
URL: https://linguistics.ucsc.edu/about/what-is-linguistics.html
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Characterizing the Role of hnRNP-A2/B1 in Immune Regulation
Primary mentor: Mays Mohammed Salih
Secondary Mentor: Tanya Ivanov
Faculty advisor: Prof. Susan Carpenter
Location: Remote/online
Number of interns: 3
Project description:
The innate immune response is the first line of defense against pathogens. The proper activation of this response is essential for resolving infections; however, uncontrolled activation could be deleterious and lead to a host of autoimmune diseases. The mentor’s lab is studying how an RNA processing protein, hnRNP-A2/B1, regulates the innate immune response in mice and humans. The mentor’s research entails deleting this protein from macrophages (immune cells) in mice, assessing protein production levels to estimate deleting efficiency, and assessing the change in immune response using different benchtop techniques. The mentor will implement an online teaching protocol where the SIP interns will utilize online/bioinformatics tools to study and characterize the RNA processing protein, hnRNP-A2/B1, and its role in immune regulation.
Tasks:
The SIP interns will be guided through a literature search using scientific databases to study the known structure and functions of the hnRNP-A2/B1 protein. The interns will be required to independently read and critically analyze scientific papers, participate in weekly discussion and Q&A sessions, formulate hypotheses, and design experiments to answer specific questions. The SIP interns will work in groups on weekly tasks, using databases and bioinformatics tools such as the UCSC genome browser to gather information about the gene of interest to answer specific questions about its structure and function. The interns will look for transcripts, design PCR primers, confirm primer design, analyze PCR results, and draw conclusions about the associated experiments.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work
URL: https://mcd.ucsc.edu/faculty/carpenter.html
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: What do Neurons Say? Decoding the Brain with Statistical and Computational Methods
Primary mentor: Yufei Si
Faculty advisor: Prof. David Feldheim
Location: Remote/online
Number of interns: 3
Project description:
“If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” Yet, neuroscientists are making significant progress in understanding the brain, and one of the first steps is to decode the neuronal code that our brain uses. We now know that neurons use electric signals as their language to communicate, and these signals can be recorded using in vivo electrophysiology techniques. However, how do we know what these neurons are talking about with their electric signals? The SIP mentor and interns will find out using statistical and computational methods. The interns will gain basic knowledge about the central nervous system, become familiar with basic ideas about physiology techniques, and learn about data analysis and programming.
Tasks:
The SIP interns will gain a basic understanding of the nervous system, learn about basic ideas of how to study the brain using available techniques, and practice basic computational analysis with existing electrophysiology data. Specifically, the SIP interns will take part in the following: (1) reading primary papers/textbooks from the field and understanding some general concepts of neuroscience; (2) understanding the logic behind the mentor’s project and experiments being performed; (3) learning about basic statistical and computational tools and understanding the analytical methods being used; (4) analyzing existing data as a final practice/project.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
URL: https://feldheimlab.mcdb.ucsc.edu/index.html
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Meiotic and Mitotic Chromosome Segregation
Primary mentor: Anna Russo
Faculty advisor: Prof. Needhi Bhalla
Location: Remote/online
Number of interns: 2
Project description:
How do cells ensure that they have the correct number of chromosomes after every cell division? Mistakes in cell division during meiosis or mitosis can lead to cells inheriting an incorrect number of chromosomes, which can result in infertility, miscarriages, genetic disorders, and cancer. The mentor’s lab is interested in understanding how chromosome segregation during meiosis and mitosis occurs so that these errors in segregation are prevented. The lab use a combination of genetics, microscopy, and biochemistry to better understand these two processes using the nematode Caenorhanditis elegans (C. elegans) as a model organism.
Tasks:
The SIP interns will gain a basic understanding of molecular and cellular biology. Potential experiments will include: (1) sing CRISPR/Cas9 gene editing to create florescent proteins or mutations of interest; (2) setting up genetic crosses and using Polymerase Chain reaction (PCR)/gel electrophoresis to genotype worms; and (3) performing either live microscopy to film cells undergoing mitosis or immunofluorescence to visualize meiotic chromosomes. The interns will also learn how to analyze and interpret their data, perform statistical analysis to assess significance, and plan and document experiments. The interns will also be highly encouraged to attend and participate in lab meetings, journal clubs, and learn how to read primary literature from the field.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work; statistical data analysis
URL: https://www.bhallalab.com
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Microbes, Amyloids, Evolution, and the Origins of Life on Earth
Primary mentor: Amanda Carbajal
Faculty advisor: Prof. Lynn Rothschild
Location: Remote/online
Number of interns: 3
Project description:
The details surrounding the origins of life on earth remain a mystery. We know, thanks to modern technological examination of microbial and geological fossil records, that the environment on earth when life emerged was hostile to life and unlike the earth we live in now. How could life have emerged in these hostile environments? We postulate that amyloids and prionogenic amyloids assisted the Last Universal Common Ancestor LUCA to survive these extreme environments by offering a physical shield-like entity and harboring transmissible phenotypic data. Nucleic acids like DNA and RNA are essential to life but their chemistry is highly sensitive and would have struggled greatly to stay stable and viable in these extreme environments of temperature, acidity, atmosphere components, and UV radiation. Prionogenic amyloids are UV resistant and can be passed among individuals in a species. They are associated with negative effects in mammals and humans via neurodegenerative diseases like Mad Cow Disease but are found in a myriad of other species that are evolutionarily older. Prions themselves are proteins that harbor transmissible information and cause protein folding changes and are not toxic in yeast and other microbes. Additionally, these prion amyloids harbor positive effects that be turned on like molecular switches to assist organism survival when triggered by environmental stress. The mentor’s research group hopes to understand these components in microbes, the oldest living entities on planet earth. Studying them will provide insight into many disciplines, shedding light on evolution, prions, amyloids, their effects on mammals versus microbes, and how they may have assisted LUCA, which gave rise to the three domains of life. Utilizing bioinformatics, the mentor’s group has scanned all annotated genomes to find potential protein candidates across species that could harbor prion amyloids based solely on their genetic code.
Tasks:
The SIP interns’ primary task will be to develop a helpful in-house database of the microbe species grouped into those that are confirmed, not yet confirmed, and negative for prion amyloids. This database will be used to track genetic drift among specific protein functions across all domains of life (Eukarya, Bacteria, Archaea). The interns’ secondary tasks will include learning what it means to be a scientist from reading peer-reviewed scientific journals, identifying strong and weak studies, and familiarizing themselves with the methods used in the field to achieve the proving of a hypothesis. The SIP interns will learn to network, collaborate, communicate, and see a project through. The interns will learn about a field that is emerging and one that few, if any, other labs are working on.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis; experimental design and protocols
URL: https://www.nasa.gov/content/research-overview
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Genome Analysis of a Novel Photoarsenotroph, Rhodobacter sp. str. ORIO.
Primary mentor: Sanjin Mehic
Faculty advisor: Prof. Chad Saltikov
Location: Remote/online
Number of interns: 3
Project description:
Arsenic is a naturally occurring poison found in nature. The mentor’s PhD project is focused on studying a process where bacteria use photosynthesis and arsenic to grow, called “photoarsenotrophy”. One day, the mentor’s research group hopes to understand how arsenic is transformed in nature so that they can protect life from arsenic poisoning. This summer, the SIP mentor and interns will perform bioinformatics, which means they will use computer software to perform their analyses. Specifically, the mentor’s research group wants to better understand the genes required for photoarsenotrophy. The SIP interns will ultimately learn how a bacterial genome is sequenced and analyzed. Specifically, the interns will do DNA/protein alignments, create phylogenetic trees, and design genetic engineering experiments.
Tasks:
The SIP interns tasks will include: (1) bacterial genome assembly/annotation, (2) alignments, (3) phylogenetics, and (4) genetic engineering design. The interns will learn how to search for the bacterial genes of interest, and create a list that can be used as a reference database. The SIP interns will then use the reference database to search large environmental data sets for particular genes of interest. Lastly, the interns will generate a report on the metabolic traits of a bacteria by analyzing its genome and looking for metabolic pathways.
Required skills for interns prior to acceptance: Computer programming; biology/DNA knowledge
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Relationship of North Pacific Marine Heat-Waves to Climate Extremes in North America
Primary mentor: Dr. Aikaterini Giamalaki
Faculty advisor: Prof. Claudie Beaulieu
Location: Remote/online
Number of interns: 2
Project description:
Climate change has already had noticeable impacts on our environment. Effects that have been previously predicted, such as extreme heat waves and droughts, are now occurring. Understanding extreme temperature events in the ocean as well as on land has been a major scientific concern. Extremely increased land temperatures have been reported in the last decade over North America. At the same time, the most extreme sea surface temperatures have also been documented in the North Pacific and the California Current. For example, 2016 has been the hottest reported year in North America, closely following the 2013–2015 extended marine heat-wave in the Northeast Pacific, named as ‘the Blob’. Nevertheless, little is known about the relationship between those marine and land heat-waves. The mentor’s research group focuses on quantifying these extreme events and further exploring the dynamical relationships between them. The group uses statistical and dynamical techniques applied on observations and modeled output in order to answer questions regarding the time and space that marine and land extreme events occur, and the physical mechanisms that may explain the development of such events.
Tasks:
The SIP interns will: (1) understand the basics of climate dynamics and the physical relationship between specific oceanic and atmospheric parameters through a literature review of the topic and discussions with the mentors; (2) learn basic programming using Python and/or R; (3) collect and process the publicly available observational datasets and/or model output such as sea surface temperature, land air temperature, sea level pressure, and wind; and (4) apply statistical methods/extremal networks in order to describe possible physical relationships.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Primary mentor: Ryan Tumbleson
Faculty advisor: Prof. Jairo Velasco, Jr.
Location: Remote/online
Number of interns: 2Project description:
Researchers are hitting a fundamental limit for how small and powerful electronics can be with our current technology. In this research project, the mentor and SIP interns will create and explore some of the smallest electronic devices in existence and study the exotic behavior of these devices that result from quantum mechanical phenomena. By stacking multiple layers of two-dimensional materials (thickness of one to a few atoms) on top of each other, the group will engineer devices that have novel properties that they can exploit and potentially implement in future nanotechnology.Tasks:
The primary objective of this research project is to create and understand the physics behind two-dimensional devices. The mentor will be in the lab fabricating devices while simultaneously live streaming the process and discussing it with the SIP interns. The mentor will provide an in-depth description of the current methods used to cut chips, pre-process them, pick up the two-dimensional materials using scotch tape (yes, just regular scotch tape!), stack them on top of each other, post-process them, and then characterize them. In addition to this, the interns will collaborate on processing the data obtained from characterizing equipment such as an atomic force microscope and a scanning tunneling microscope. These two microscopes provide a way to visually see the surface of a material at the atomic scale. Finally, there will be a Python coding portion of the project where the mentor will cover basic coding methods, fundamental calculations to the research area, and then investigate theoretical properties of the devices being fabricated to better understanding the underlying physics.Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab workURL: http://jvjlab.sites.ucsc.edu/This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Dielectric Response in Multiferroics and Novel Dielectrics
Primary mentor: Maverick McLanahan
Faculty advisor: Prof. Arthur P. Ramirez
Location: Remote/online
Number of interns: 2
Project description:
Dielectric materials are electrical insulators that polarize in the presence of an electric field. These materials are essential in energy storage applications and for improving semiconductor devices. This project will investigate the dielectric responses and electrical conduction mechanisms in multiferroics (materials with both electric and magnetic order) and novel dielectrics (e.g., titanates which may exhibit large dielectric constants). Multiferroics may display strong magnetoelectric coupling such that applied magnetic fields could be used to alter their dielectric properties, and materials with large dielectric constants will increase energy storage capabilities. Finding materials that possess either of these properties may ultimately result in potential candidates for future device applications.
Tasks:
This research project will consist of lab-work, software-hardware integration, and experimental data analysis. SIP interns will learn how to prepare crystal samples for dielectric measurements in cryostats – i.e., crystal orientation, cutting/polishing, and metal deposition. Measurements will be performed as a function of AC voltage source frequency and applied magnetic field, from room to liquid helium temperatures (300 K to 4 K). The interns will integrate data collecting software with cryostat hardware to run experimental trials. The experimental data will then be modeled to dielectric relaxation models to characterize the samples.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
URL: https://aprlab.sites.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Synthesis and Characterization of a New Two-Dimensional Material Heterostructure/Composite via Chemical Vapor Deposition Method
Primary mentor: Ashlyn Molyneaux
Faculty advisor: Prof. Aiming Yan
Location: Remote/online
Number of interns: 3
Project description:
Two-dimensional materials like graphene, boron nitride, and transition metal dichalcogenides (TMDs) have been of growing interest recently due to their novel chemical and physical properties. The materials are classified as 2D because they have a thickness of only one or a few atoms. There are many potential and promising applications of these materials in next generation flexible electronics. In order for these applications to be feasible, controllable and scalable syntheses of these materials are necessary. Atomically thin molybdenum disulfide (MoS2) is the TMD of interest for this research project. Under close mentoring and supervision, the SIP interns will learn the chemical vapor deposit growth method for controllable synthesis of this material and together the mentor and interns will work to further develop this method. The interns will then learn to use techniques such as optical microscopy, atomic force microscopy, and Raman spectroscopy to further understand and characterize this material.
Tasks:
First, the SIP interns will learn the growth method. Then, the interns will learn characterization techniques like optical microscopy, atomic force microscopy, and Raman spectroscopy. From weeks 4-7, the interns will use the CVD growth method to grow MoS2 and tweak growth parameters to get the best yield of the material. Each intern will learn each task well enough to perform it independently.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work
URL: https://sites.google.com/a/ucsc.edu/2300-delaware/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Terrestrial High-Energy Observations of Radiation (THOR) Deployment Site Research
Primary mentor: Jeffrey Chaffin
Faculty advisor: Prof. David Smith
Location: Remote/online
Number of interns: 3
Project description:
In 1994, a NASA spacecraft, the Compton Gamma-Ray Observatory, observed several very bright and very fast bursts of gamma radiation originating in Earth’s atmosphere. It has been found that these intense bursts of radiation are associated with lightning strikes and that the large electric fields generated in thunderclouds and at the tips of lightning leaders can act like natural particle accelerators several kilometers in length. These electric fields are capable of accelerating the free electrons in our atmosphere to relativistic speeds, and the subsequent collisions of these highly energetic particles with atmospheric nuclei results in the emission of gamma radiation. While this radiation takes several forms, the most dramatic and important are terrestrial gamma-ray flashes (TGFs). TGFs are bursts of gamma radiation (and byproducts such as neutrons and positrons) associated with a small percentage of lightning flashes and lasting less than a millisecond. During that brief time, TGFs are about as luminous in gamma-rays as the entirety of Earth’s atmosphere (which glows in gamma radiation from interactions of cosmic rays in air). This is incredibly bright and occurs just a few kilometers above us! In the part of a thundercloud where TGFs are generated, they might produce a radiation dose of up to 1 sievert, sufficient to produce immediate radiation sickness and a high risk of later cancers in humans. The mentor’s research group specializes in ground and in-situ (balloon and airplane) observations of TGF events, and they are currently in the process of designing a suite of new ground based detectors, the Terrestrial High-Energy Observations of Radiation (THOR) instruments, to improve their observations of TGF events in low altitude storm systems. A critical part of any field work is determining the optimum location to place each instrument. One needs to consider, tropopause height as it relates to storm altitudes, lightning frequency, TGF frequency based off satellite data, and current research into lightning processes and power per flash studies (Superbolts), as well as the possibility of tower locations (i.e., lightning rods), other infrastructure, and ease of access. This research incorporates particle physics, lightning physics, and general atmospheric science with an emphasis on instrumentation and field deployments.
Tasks:
The SIP interns will work remotely and be given the job (under the mentor’s supervision) of researching optimal field deployment locations for the new THOR instruments. This will be accomplished by a combination of data analysis and programming to create world maps that correlate weather system altitude patterns, lightning frequency, and TGF frequency from satellite data and current research on “superbolts” by Holzworth (2019) of the World Wide Lightning Location Network. Additionally, the SIP interns will need to engage in high level internet research to determine country by country information on communication tower distributions, infrastructure, and possible organizational contacts. On a daily basis, the SIP interns can expect to do the following: (1) work in a Python coding environment to parse large data sets and create global heat maps; (2) engage in research and information gathering via the internet; (3) document and summarize all information gathered to present to research group members on a weekly basis; (4) investigate “superbolt” data from Holzworth (2019) in the framework of possible field sites; (5) maintain a detailed laboratory notebook; and (6) participate in research group meetings.
Required skills for interns prior to acceptance: Computer programming (some knowledge of a coding language, preferably Python)
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	OFF	ON

Title: World of Robots: Child-Robot Interactions
Primary mentor: Elizabeth Goldman
Faculty advisor: Prof. Su-hua Wang
Other mentor: Sam Basch
Location: Remote/online
Number of interns: 3
Project description:
Robots are becoming a major part of our society. This research project aims to investigate how young children interact with robots. This is an important topic because many robots are being designed and marketed for children. However, we do not understand how these robots impact children and their development. In this research project, children will watch a robot perform different behaviors, and the SIP interns will then observe the children’s reactions and take detailed notes. After the robot exhibits these different behaviors, children will watch the robot attempt to complete a task and will then be given the opportunity to help the robot finish the task. This research project has already been designed. This summer, the mentor’s research team will work together to collect as much data as possible. The SIP interns and the mentor’s research group will then work together to code and analyze the data they have collected. This research project could impact how robot designers create and build robots for young children.
Tasks:
This research project is for SIP interns who are interested in learning about robots and who would like to work with children and families. No previous experience working with children is needed, as the interns will be taught how to work with children. The SIP interns will also learn valuable skills such as naturalistic observation, taking detailed notes, eye tracking (tracking the eye movements of young children), and data analysis. The interns will help set up the study, run the study, enter data, and analyze the data. In terms of data analysis, this research project will involve coding child behaviors and reactions. It will also involve coding those behaviors that the SIP mentor and interns have observed. The interns will be trained in coding and observation of videos, survey data, and interviews. The interns will see the research process from start to finish and will gain valuable experience of (remotely) working in a psychology research lab. SIP interns will also learn about designing a research proposal, formulating a research question, and conducting a literature review on a topic of their interest.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Remote lab work; statistical data analysis
URL: /https://elizabethgoldman.weebly.com
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	OFF	OFF

[/fusion_toggle][fusion_toggle title=”PSY-02: Reciprocity in Conversation” open=”no” class=”” id=””]Title: Reciprocity in Conversation
Primary mentor: Andrew Guydish
Faculty advisor: Prof. Jean E. Fox Tree
Location: Remote/online
Number of interns: 3
Project description:
Do people work together to create reciprocal balances across conversations? The SIP mentor is interested in conversational dynamics and how people carry and maintain conversations. In particular, the mentor is interested in conversational balance between participants throughout the course of the conversation, and how these balances influence how individuals communicate with one another.
Tasks:
The SIP interns will work on numerous aspects of development regarding psychological experiments. The interns will work with the mentor in the development of experiments examining areas of interest pertaining to cognitive psychology (e.g., discussing experimental design, conducting literature reviews on related concepts), work with real data (e.g., transcribing videos, examining transcripts), as well as running participants in psychological experiments under supervision. Through this process, the SIP interns will gain experience in the following: writing APA style annotated bibliographies; processes associated with experimental development; running human participants; analyzing real data in IBM’s SPSS; and development and use of Python algorithms for data parsing and analysis.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
URL: https://guydish.sites.ucsc.edu, https://foxtree.sites.ucsc.edu
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	OFF	ON	ON	ON	ON	ON	ON

Title: Understanding Misophonia: What is It and How Do the Senses Interact?
Primary mentors: (a) Allison Allen (b) Chris Kay
Faculty advisor: Prof. Nicolas Davidenko
Location: Remote/online
Number of interns: 4
Project description:
Do you or does anyone you know experience sensitivity to particular sounds, such as chewing or sniffling? Some people who experience a condition called misophonia report sensitivity to certain sounds that may not bother others, such as a person chewing, sniffling, or tapping. Hearing such sounds can cause the person to experience averse physical and emotional reactions that can interfere with everyday life. Despite the impact that misophonia can have on people’s lives, little work has been done to characterize and treat the condition. The mentors’ research will focus on characterizing misophonia and exploring how information from other senses (e.g., vision) can influence the misophonic experiences. As described below, there will be two sub projects, (a) and (b), within the same larger research project.
Tasks:
The SIP interns will have the opportunity to learn about misophonia and related perceptual processes, including vision and audition, and how processes can interact. This will be done by reading scientific articles each week and discussing them with the mentors. They will also: (a) gain experience collecting data in an online experiment and will learn how to program and analyze qualitative data using MATLAB; and (b) prepare audiovisual stimuli and use it to run a quantitative experiment.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
URL: https://davidenko.sites.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Exploring Everyday Helping
Primary mentor: Margie Martinez
Faculty advisor: Prof. Audun Dahl
Other mentor: Charles Baxley
Location: Remote/online
Number of interns: 2
Project description:
How do we come to help others? The mentor’s research group examines moral reasoning in relation to one’s actions in everyday contexts. The group is particularly interested in how parent-child interactions influence the development of helping behavior and how this may vary across different cultural backgrounds. This research project will examine how the daily routines of families impact judgments, reasoning, and decisions about helping behaviors. By examining the everyday experiences with helping, the interns and mentor can gain a better understanding of how children and adults come to the moral decision of who and when to help.
Tasks:
The SIP interns will be involved in most or all aspects of this research project. The interns may help design research studies, collect data (for instance, through interviews), analyze video recordings or interview transcripts, and/or discuss research articles. The interns may work with data from past or current projects exploring how children and young adults think about helping. The research group will discuss literature relevant to the project and moral development. This research project will provide an opportunity for interns to learn about and contribute to all stages of psychological research.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work; statistical data analysis; field work
URL: https://esil.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Children’s Learning Through Collaboration
Primary mentor: Samantha Basch
Faculty advisor: Prof. Su-hua Wang
Other mentor: Elizabeth Goldman
Location: Remote/online
Number of interns: 2
Project description:
The mentor’s research focuses on how toddlers and preschoolers learn through collaboration. This summer, the mentor’s research team will study parent-child collaboration during play. The team will study both natural play and structured play, with a special focus on parental question-asking. The hope is the results will shed light on how culture and context shape parent-child collaboration and learning.
Tasks:
The SIP interns will get experience with the full range of activities that occur in a developmental psychology lab, including scheduling, explaining informed consent, and running experiments. The interns will also learn how to collect and analyze observational data. These are important skills for any psychologist. Finally, the interns will have the chance to work with other members of the Infant and Child Development Lab on ongoing projects.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis; field work
URL: https://suhua.sites.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Exploring How Moral Reasoning Develops Over the Lifespan
Primary mentor: Charles Baxley
Faculty advisor: Prof. Audun Dahl
Other mentor: Margie Martinez
Location: Remote/online
Number of interns: 3
Project description:
Children, adolescents, and adults reason and make judgments about what is right and wrong. The mentor’s laboratory investigates how individuals at different ages reason and judge about moral issues, and how their judgments relate to their actions. The mentor’s research group studies how children and adults behave in different situations and interview them about their thoughts and feelings. For instance, why do young children think it is good to help others and bad to harm others? Why do students sometimes decide to cheat in school, even though they think it is generally wrong to do so? The overall goal of the mentor’s research is to understand how people make judgments and decisions surrounding right and wrong, and how one can help people make better decisions.
Tasks:
The SIP interns may help develop a new research project, as well as work on existing research projects. As part of this process, the interns will learn to develop theory by diving into the moral development literature and may also help develop interview protocol and materials. To gain experience with data analysis, the SIP interns will work with data from past projects that have explored topics such as academic misconduct. There will be weekly team meetings where the research group will discuss past literature related to the project and overarching theory. This research project provides an excellent opportunity for the SIP interns to learn about all stages of psychological research, from discussing scientific articles to reporting results.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis
URL: https://esil.ucsc.edu/
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Human and Artificial Agent Communication
Primary mentor: Elise Duffau
Faculty advisor: Prof. Jean E. Fox Tree
Location: Remote/online
Number of interns: 2
Project description:
As technology advances, artificial agents, such as Alexa and Siri, are becoming more and more integrated into our lives. The mentor is interested in expanding on how we communicate with artificial agents. More specifically, the mentor is interested in understanding the different ways in which we communicate with artificial agents, and how we can manipulate artificial agents’ communication styles to adapt to more casual interactions.
Tasks:
The SIP interns will gain experience in the various aspects of psychological experiments. The interns will work with the mentor in learning how to conduct research in cognitive psychology related to the area of interest. This will include engaging in experimental design, conducting literature reviews, working with data, and running participants in a psychological study using online methods with supervision. The SIP interns will gain experience in writing APA style annotated bibliographies, how to design an experiment, running online surveys, and analyzing data in SPSS, R, and Python.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; lab work; statistical data analysis
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	OFF	ON	ON	ON

Title: Gender, Orientation, and Identity in Online Roommates Ads
Primary mentor: Daniel Copulsky
Faculty advisor: Prof. Phillip Hammack
Location: Remote/online
Number of interns: 3
Project description:
Many college students and working adults find roommates online, with ads posted to sites like Craigslist, Facebook, and Reddit. Along with details about pricing and amenities, many ads mention personal info and values. Posters often hope to match with roommates based on identities like gender, sexual orientation, political affiliation, or dietary restrictions. This research will look at how individuals describe both their own identities and their preferences for potential roommate identities.
Tasks:
The SIP interns will: (1) read and discuss scholarly articles related to identity, gender and sexuality, bias and discrimination, and housing selection; (2) gather data from roommate ads posted to sites like Craigslist, Facebook, and Reddit; (3) read roommate ads, code identity references, and look for emerging themes; (4) analyze these codes to look for trends, including regional differences in ads; and (5) collaborate on ideas for a possible future experimental study looking at how individuals respond to identity preferences stated in housing ads.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis; qualitative data analysis
URL: https://psychology.ucsc.edu/about/people/grad-directory.php?uid=dcopulsk
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Exploring the Boundaries of Human Memory
Primary mentor: Mercedes Oliva
Faculty advisor: Prof. Benjamin Storm
Location: Remote/online
Number of interns: 2
Project description:
Memory serves a purpose in most aspects of our lives. That is the approach that the mentor’s research group takes, allowing a broad range of research questions. Specifically this summer, however, the SIP mentor and interns will be working on (at least) three projects, two of which consider the boundaries of retrieval-induced forgetting and retrieval-induced facilitation (specifically, feedback and expertise), and the third considers the various ways in which memory processes may function differently in ADHD populations. Depending on whether it will be possible to work with participants in-person, the mentor and interns may also return to a research project the considers the relationship between creativity, task switching ability, and memory.
Tasks:
The SIP interns will develop their critical reading skills, something that will serve them well in future academic endeavors. The interns will learn about all stages of a research project, from design and construction of study materials, to data collection, to data entry and management and basic statistical analyses. Depending on whether it is possible to run participants in-person, the SIP interns may have the opportunity to be introduced to various neuropsychological assessments with hands-on data collection.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Statistical data analysis; study design
URL: https://people.ucsc.edu/~bcstorm/research.html
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Texting, Video Chat, and Emotions
Primary mentor: Vanessa Oviedo
Faculty advisor: Prof. Jean E. Fox Tree
Location: Remote/online
Number of interns: 2
Project description:
The mentor’s research interests lie in the domain of technology assisted communication. Specifically, the mentor is interested in the way that people communicate and emotionally connect over different communication mediums, such as face to face, video chat, and texting. The current research project is examining differences in emotional communication when people interact via video chat versus text-based conversations.
Tasks:
The SIP interns will work with the mentor in the completion of experiments by reviewing experimental design, conducting APA style literature reviews, and completing APA style annotated bibliographies. The interns will also work with real data in which they will transcribe audio files, examine transcripts, enter and clean quantitative data using SPSS, and code qualitative audio files. In working with the mentor, the SIP interns will learn skills such as designing a research study, completing a literature review, creating research questions and developing hypotheses, entering quantitative and qualitative data, and analyzing the final data set.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work; statistical data analysis
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Communication in Varying Mediums
Primary mentor: Ryan Pili
Faculty advisor: Prof. Alan Kawamoto
Location: Remote/online
Number of interns: 3
Project description:
Recent advances in technology have provided new ways to communicate with others. Mediums such as video-chatting, instant-messaging, and voice-messaging each provide their own constraints on peoples’ conversations. How could these constraints influence how people communicate? This research project is a psycholinguistics study investigating how people might adapt their conversations to a given medium. The mentor’s goal for this project is to find possible sources of face-to-face and computer-mediated communication, to analyze differences in how people communicate. This research project is ideal for SIP interns who are interested in cognitive science, linguistics, computer-mediated communication, human-computer interaction, and video-games.
Tasks:
The SIP interns will learn to read research articles under the mentor’s guidance to understand the background of the project. With this context, the interns will code video and speech data of conversations for analysis. The SIP interns may also edit video-data, carry out preliminary data analysis, do online recruiting and scheduling of human participants, and administer computer experiments under the mentor’s supervision. The interns will gain hands-on experience with data visualization (MATLAB), video editing (Adobe Premiere), phonetics software (PRAAT), and face-recognition software.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Computer programming; statistical data analysis
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Understanding Spontaneous Communication
Primary mentor: Allison Nguyen
Faculty advisor: Prof. Jean E. Fox Tree
Location: Remote/online
Number of interns: 3
Project description:
Why do we use the words that we use, and do we signal specific things with our choices? It is possible that specific discourse markers are used in specific contexts. The mentor is interested in how and why information and stories spread through populations. The mentor is also interested in other aspects of spontaneous communication and how people talk to one another.
Tasks:
The SIP interns will code data, run statistical analysis on data collected, and transcribe videos. They will also be asked to read and discuss current literature in the area of cognitive psychology, especially in the area of communication and the spread of information. The SIP interns will be asked to write up brief reports and will be given training in APA-style writing. The interns will have the chance to learn about running psychology experiments using Google Forms and LimeSurvey and will get experience working with Excel and possibly Python.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Transcriptions; data coding; computer programming (depending on interest)
URL: http://www.allisongnguyen.wordpress.com
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Service-Learning Classes and College Student Outcomes
Primary mentor: Miguel Lopezzi
Faculty advisor: Prof. Regina Langout
Location: Remote/online
Number of interns: 3
Project description:
Service-learning classes are important because they help students learn while they are also engaged in the community and giving back. But, not all service-learning courses are the same. Yet, they are often treated the same in the research literature. In this research project, the SIP mentor and interns will work together to figure out differences across several service-learning courses to see if these differences help them to understand college student outcomes better. For example, are certain kinds of classes related to certain kinds of outcomes (academic vs. civic engagement
Tasks:
The SIP interns will listen to and transcribe (write down or type out), word for word, interviews between the graduate student researcher and professors who teach service-learning classes. The interns will also check the transcriptions for accuracy, and help to categorize the interviews to determine the quality and nature of the service-learning classes. The SIP interns will learn a lot about interviews, how to work with word data, and different models for service-learning classes. The interns will get to help make important decisions about categorization of the classes.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work
URL: http://lopezzi.sites.ucsc.edu
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON

Title: Synthetic Monologues and Dialogues
Primary mentor: Yasmin Chowdhury
Faculty advisor: Prof. Jean E. Fox Tree
Location: Remote/online
Number of interns: 2
Project description:
We communicate with synthetic (computerized) voices through various types of technology (e.g., Siri/Alexa) in our daily lives. Due to the increasing nature of this interaction, we want to research the persuasive implications behind these types of voices. In this project, the mentor and SIP interns will experimentally test how different synthetic voices, communicated through monologues and dialogues, influence persuasion and perceived power on various topics.
Tasks:
The SIP interns will read research papers, complete literature reviews, code data, run experimental participants (with supervision), and transcribe interactions. The interns will have bi-weekly meetings with research teams where they will discuss ongoing and upcoming work.
Required skills for interns prior to acceptance: None
Skills interns will acquire/hone: Lab work
Special age requirement: Interns must be 16 years old by June 22, 2020.
This research project will allow for remote participation by interns.

Program Week Number:	1	2	3	4	5	6	7	8
Mentor’s availability:	ON	ON	ON	ON	ON	ON	ON	ON