2022 Research Projects

ANT-01 : Public Perceptions of Old Age-related Problems

Primary Mentor: Amarasiri De Silva

Number of Interns: 3

The project involves a learning activity popularly known as interviewing in social sciences. The interview data in this project will focus on people’s perception of old age-related problems. Interviewing requires specific skills, and the project will provide training on such skills at the beginning. The mentor will train the students to collect and analyze interview data from the communities close to the interns. The interview data can be collected using audio scripts, interview notes, or both. The mentor will discuss the tools for data collection, and the interns will be trained to use the tools. The interns interested in video making are encouraged to document the interview process and the key factors emerging from the interview. The students do not require any pre-qualifications.

ANT-02: Stories of the Elderly in the Diaspora During the Pandemic

Primary Mentor: Annapurna Pandey

Faculty Advisor: Kati Greaney

Number of Interns: 4

“In an April 2020 essay entitled “The pandemic is a portal,” Arundhati Roy wrote: “Historically, pandemics have forced humans to break with the past and imagine their world anew. This one is no different. It is a portal, a gateway between one world and the next.”
In this project, we will collect stories about aging and how the older population in the Indian diaspora has grappled with the challenges of covid and imagine the alternative possibilities engendered by it. As I see it, to understand the covid crisis, we need two telescopes. One is devoted to the past experiences of the elderly population and the other to see how they have been able to cope with new challenges – social isolation, loneliness, anxiety, mental health, death in the family and friends community, etc created by the pandemic.
In this project, you will collect personal stories of the experiences of the elderly by using face interviews, video chats, and zoom. You will be looking for people in your neighborhood and community for these interviews. You may choose a specific topic related to aging:
1. How do the elderly approach or understand aging, the choice of living at home or institutions like old age homes, assisted living, in-home health care. How has Pandemic played a role in the life experience of the elderly?
2. Imagining or examining policies or programs that support aging and the Pandemic
3. Forms of engagement with aging and Pandemic (e.g., fiction, poetry, film, photography)”

AAI-01: UNet and GANs in Medical Images

Primary Mentor: Nahid Nasiri

Faculty Advisor: Prof. Gabriel Elkaim

Number of Interns: 5

UNet is a convolutional neural network architecture that expanded with few changes in the CNN architecture. It was invented to deal with biomedical images where the target is not only to classify whether there is an infection or not but also to identify the area of infection. On the other hand, medical images suffer from lack of enough images for deep learning purposes. To compensate this problem, we will study Generative Adversarial Networks (GANs) which is one of the vital efficient methods for generating a massive, high-quality artificial picture. GANs is a class of generative models that was introduced by Goodfellow et al. It is one of the most-cited papers in computer science (nearly 26000 at the time of writing of this proposal), which proves this method’s popularity and importance in the machine learning and deep learning fields. Yann LeCun, who is a pioneer in the modern revolution in deep neural networks, declared GANs as “the most interesting idea in the last 10 years in machine learning.” For diagnosing particular diseases in a medical image, a general problem is that it is expensive, usage of high radiation dosage, and time-consuming to collect data. Hence GAN is a deep learning method that has been developed for the image to image translation, i.e. from low-resolution to high-resolution image, for example generating Magnetic resonance image (MRI) from computed tomography image (CT).

AAI-02: Training spiking neural networks using backprogation through time (BPTT)

Primary Mentor: Peng Zhou

Faculty Advisor: Prof. Sung-Mo Kang

Number of Interns: 3

Deep neural networks (DNN) have solved numerous problems in computer vision, speech recognition, and natural language processing, but suffer from the high energy consumption when running in CPU/GPU, especially as compared with the brain. Neuromorphic computing provides an approach to overcome the von Neumann bottleneck, and it requires a new set of neural networks — spiking neural networks.

AAI-03: Behavior Modeling with Reinforcement Learning

Primary Mentor: Golam Md Muktadir

Faculty Advisor: Prof Jim Whitehead

Number of interns: 3 R

Reinforcement Learning is a subdomain of Machine Learning, where intelligence is learned without supervision. It has successfully been applied in robotics, finance, design, etc. In my research, I use it to model human behavior for simulation. We will be learning some algorithms and applying them in real-world experiments.

AAI-04: Artificial Intelligence in Self-driving Cars

Primary Mentor: Majid Moghadam

Faculty Avisor: Prof. Gabriel Elkaim

Number of Interns: 4

Self-driving cars use a stack of multiple sensors to observe the environment and make decisions. Artificial intelligence algorithms in recent years have helped these vehicles to improve their intelligence significantly. In this project, the students will learn how self-driving cars see, think, and take actions using AI algorithms. Students will involve in simple Python programming tasks to build an AI agent that plays a game just like humans do.

AAI-05: Learning-based Framework for Heart Disease Identification

Primary Mentor: Xinyi Wu

Faculty Advisor: Zouheir Rezki

Number of Interns: 3

Computer-assisted test interpretations have efficiently supported doctors in addressing early diagnosis of heart disease during routine examinations. In particular, an electrocardiogram (ECG), one of the most popular cardiac tests, is a quick and painless tool for early diagnosis. It presents the status of the patient’s heart condition, depending on precision of test interpretation. The objective of this research project is to substantially enhance heart disease identification via a comprehensive learning-based framework leveraging physical tests such as ECG test, cardiac stress test, etc.

AAI-06: Deep Learning for Aerial Vision-and-Language Navigation: Control a Drone with Language Instructions

Primary Mentor: Yue Fan

Faculty Advisor: Prof. Xin Wang

Number of Interns: 3

“Vision-and-language navigation (VLN) is a task that requires an embodied agent (an intelligent agent that interacts with the environment) with vision sensors to follow language instructions and navigate to destinations. Recently, VLN has received a lot of interest for its application in developing house service robots. However, VLN also has great potential for drones in outdoor environments, for example, a VLN-enabled drone can provide people with a hands-free drone control experience and make the drone easier to use.

In this project, interns will work on a novel topic, aerial VLN, where the goal is to build a VLN-enabled drone in a computer simulation environment. There are three major challenges:
• Creating a vision system to find targets.
• Constructing a language understanding module.
• Designing a drone control module to generate drone actions.
Interns will use Python and Deep learning frameworks to tackle challenges and finally realize controlling the drone in the simulator with language instructions.”

AAI-07: VLM: Vision-and-Language Manipulation Tasks

Primary Mentor: Kaizhi Zheng

Faculty Advisor: Prof. Xin Wang

Number of Interns: 3

“Recent progress in embodied AI pushes intelligent robotic systems to reality closer than any other time before. One crucial ability of embodied agents is to finish tasks by following language instructions. Language can represent complicated tasks and distinguish their differences, and it is natural for humans to use language to command an embodied agent. Therefore, it is important to fill the blank of the last mile of embodied agents – a language-guided robot manipulation system.
In this project, interns will work on a novel topic, Vision-and-Language Manipulation (VLM), where the goal is to build a language-guided robot manipulation system in a computer simulation environment. There are two major challenges:
1. Designing a language and vision understanding module to reason the correct target object.
2. Designing a manipulation module to generate robot arm actions.”

AAI-08: Causality in Artificial Intelligence

Primary Mentor: Xuehai He

Faculty Advisor: Prof. Xin Eric Wang

Number of Interns: 3

“Machine learning based solutions suffer from different issues. Current Machine learning algorithms can be biased, suffer from a relative lack of explainability, and are limited in their ability to generalize the patterns they find in a training data set for real world applications. It has become important to improve generalization. Judea Pearl and Dana Mackenzie’s published “”The Book of Why””, which highlights the main limitations of current machine learning solutions and the importance of causality in artificial intelligence. Causality would enable us to go one step further and figure out what would happen if we decide to change some of the underlying assumptions in our model.

Exploring causality in machine learning would help us build better solutions in areas as diverse as computer vision, natural language processing, health care, and etc. In detail, there are a large number of interesting applications of causality in vision-and-language to help one develop an intuition for the types of problems where causality can be useful such as: (1) building visual-and-question answering system, (2) text and image style transfer, (3) text-to-image generation, (4) image-to-text generation, (5) vision-and-language navigation and many others.” The SIP interns will learn: (1) the concept behind artificial intelligence, machine learning, and causality (2) how to implement machine learning algorithms using Python and PyTorch. (3) Furthermore, they will learn how to read a research paper and implement it. （4) Interns can choose one from the following tasks as the application filed for testing the designed method, including but not limited to: training an AI chatbot, building an AI question answering sysetm, building a text-to-image generator, building an AI-assisted CT imaging analysis system, etc

ACS-01: Art and Science: Visual Storytelling Through Archives, Research, and Design

Primary Mentor: Saul Villegas

Faculty Advisor: Professor Jennifer Parker

Number of Interns: 7

California’s economy is the largest in the United States, and is the fifth-largest in the world. A large part of California’s economy consists of its agricultural and industrial production located in the Central Valley. These high-production industries siphon the area’s natural resources, leaving behind ecological damage that directly impacts underrepresented communities, both human and nonhuman, living in the region. The aim of this research project will be focused on 3D world-building and immersive storytelling to explore speculative futures that reimagine a future Central Valley landscape and embraces the symbiotic relations of all species living there (past and future) as a creative practice to reimagine possibilities for large agricultural food systems. SIP interns will develop research assets such as speculative design and learn to create digital assets for virtual 3D world-building to be viewed on a computer browser, phone, or tablet.  Speculative design, sometimes called critical design or design fiction, asks us to zoom out beyond user-centered design and ask what the effects of our designs could be on future societies. Outcomes from this research will be published as a virtual exhibition through the OpenLab Collaborative Research Center. Creating a virtual hub on Mozilla Spoke will allow for active participation in exhibiting their research for a diverse community while investigating virtual spaces that reimagine the cultivation practices as both sustainable and not sustainable.

ACS-02: Arctic Sea Ice Filmmaking

Primary Mentor: Rebecca Ora

Faculty Advisor: Jennifer Parker

Number of interns: 3

Arctic seas hold a multitude of unique life forms highly adapted in their life history, ecology and physiology to the extreme and seasonal conditions of this environment. In this region the impacts of climate change are strongest: effecting acidification, warming, and reduction of sea ice. The aim of this research project is to learn about Arctic Sea Ice and research techniques for nonfiction filmmaking to provide the context, footage and other visuals, narration, and interviews that will appear in a short film about Sea Ice. The SIP interns will learn how to develop a story, plan for preproduction, and post- projection, editing practices and learn technique for how to research nonfiction filmmaking and conduct archival research. Archives have a diverse range of different research materials, including still photos, footage, newspapers, and online articles, paintings, etchings, sketches, letters, journals, and diaries.

ACS-03: Animating Science: Microplastics in Arctic Sea Ice

Primary Mentor: Annika Berry

Faculty Advisor: Prof. Jennifer Parker

Number of Interns: 4

Millions of tons of plastic waste enter our oceans each year, compile in vast gyres, and break down into invisible microplastics that affect our entire ecosystem. This research project will examine polluting microplastics in Arctic Ice that harm animals and marine ecosystems as research for a series of animated sequences. These segments will potentially contribute to a short film about microplastics to be included in a movie about Arctic Ice and shared on social media platforms. The SIP interns will learn how to conduct research, develop a story, experiment with DIY bioplastics and algae-based materials for stop-motion animation, create digital assets, and learn various animation techniques and editing practices to communicate science.

AST-01: How Often Do Quasars Masquerade as RR Lyrae Candidates in Time Domain Photometric Surveys

Primary Mentors: Kayla Bartel, Kyle Nguyen

Faculty Advisor: Raja GuhaThakurta

Number of Interns: 3

It was long thought that galaxies are so-called “island universes.” Recently, however, more and more distant stars have been discovered in the halo of our Milky Way galaxy, and their distance suggests that the halos of galaxies are actually much larger than previously thought, implying that galaxies in our Local Group are comparable in size to the typical distance between them. These distant stars are a particular class of variable star called an RR Lyrae, which have very identifiable patterns of temporal variation in brightness and also happen to be excellent standard candles (i.e., they all have roughly the same intrinsic luminosity). This is a major reason as to why they are studied. To further our understanding of our own Milky Way galaxy and RR Lyrae stars, it is crucial to identify clean samples of them in large astronomical time-series photometric data sets. The photometric variations of RR Lyrae can be confused with the light curves of other types of photometrically variable astronomical objects such as distant quasars and active galactic nuclei (AGNs), so it is often necessary to visually vet the light curves. The identification of and discrimination between different kinds of variable objects can be assisted by computer algorithms that search for certain qualities present in the light curves of these variable objects; the goal of this research project is to develop such computer algorithms.

AST-02: Weak CN Stars, Carbon Stars, and Other Exotic Stars in M31, M33, and the LMC

Primary Mentor: Douglas Grion Filho

Faculty Advisor: Raja GuhaThakurta

Number of Inters: 4

The Andromeda galaxy (M31), the nearest galaxy larger than our own galaxy, its companion the Triangulum galaxy (M33), and the Large Magellanic Cloud (LMC), a Milky Way dwarf satellite galaxy, 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 group currently understands this phenomenon as being due to the natural mixing that occurs in fast rotating stars and is using state of the art stellar models to characterize their evolution.

AST-03: Creating and Analyzing Scaling Relations Between Galaxy Cluster Properties

Primary mentor: Paige Kelly

Faculty Advisor: Tesla Jeltema

Number of Interns: 4

Galaxy clusters formation is dependent on the expansion history of the universe. This makes them excellent probes of dark energy throughout time. SIP interns will be able to analyze some of the properties of galaxy clusters that make them useful in placing cosmological constraints.

AST-04: Investigating the Kinematical and Chemical Structure of Andromeda’s Stellar Disk

Primary Mentor: Amanda Quirk

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 5

The Andromeda galaxy (M31), the nearest galaxy similar to the Milky Way (MW), is ideal for studies of disk galaxy formation. M31 is the only other MW-like galaxy where individual stars in the stellar disk can be studied using spectroscopy. M31’s stellar disk also has the advantage of being viewed from an external perspective similar to more distant galaxies. Although the MW is known to possess a stellar disk composed of both “thin” and “thick” components, it is unclear if M31’s disk has a similar two-component structure. The MW’s thin (thick) disk spans a smaller (larger) height away from the disk plane and its chemical composition is relatively rich (poor) in heavy elements. Most current evidence points toward M31’s disk being dominated by a single thick component. This raises the question of (1) whether multiple-component disks are representative of disk galaxies in the broader universe and (2) what types of evolutionary processes dictate disk structure. The goal of this project is to address these questions by investigating the kinematical and chemical structure of M31’s stellar disk.

AST-05: What Happens Around Supermassive Black Holes

Primary Mentor: Martin Gaskell

Number of Interns: 3

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.

AST-07: Applications of Machine Learning to the Classification of Cherenkov Images of Very High-Energy Particle Showers

Primary mentor: Megan Splettstoesser

Faculty Advisor: Prof. David Williams

Number of Interns: 3

High-energy cosmic particles reaching Earth interact in the atmosphere, producing a shower of lower energy particles. Shower images can be captured by telescopes (e.g., imaging atmospheric Cherenkov telescopes), and these images can be used to differentiate gamma-ray showers from showers produced by cosmic rays (mostly protons). Existing classification algorithms use summary properties of the images, rather than the images themselves, to distinguish between cosmic ray events and gamma-ray events. Interns will work directly with the images to implement machine learning methods in order to differentiate events. In addition, interns will work on various aspects of the problem to improve shortcomings of the machine learning neural network in use. By allowing better rejection of the cosmic ray background, which greatly exceeds the rate of gamma-ray events, improved classification can substantially improve the sensitivity of existing and future gamma-ray telescopes. Because the project uses data from the VERITAS Collaboration, there will be some limits and constraints on what results can be presented in various public contexts, consistent with the VERITAS Collaboration publication policies.

AST-08: Automating a Multiwavelength Analysis of the Blazar Mrk 421

Primary Mentor: Olivier Hervet

Number of Interns: 3

“Blazars are the brightest stationary sources in the Universe. Their powerful plasma jets, powered by feeding supermassive black-holes, emit radiation across the whole electromagnetic spectrum, from low radio frequencies to very-high-energy gamma rays. The general behavior of these sources is still puzzling to the scientific community, especially on how they seemingly erratically produce massive flares with complex counterparts.
At UCSC, we are tackling this issue by organizing a dense monitoring campaign of one of the brightest blazar, Mrk 421, over multiple years and with various telescopes. Having already gathered a large amount of data, we now need to organize, analyze, and diffuse this dataset to the scientific community. We plan to develop an automatic pipeline at UCSC that would analyze new data every day and centralize the results onto a web platform.”

AST-09: The Triangulum Galaxy (M33): Ionized Gas Kinematics and Physical Conditions

Primary Mentor: Khang Ngo

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 3

What fills the space between stars in a galaxy? The tenuous gas and dust that fills this space is referred to as the interstellar medium (ISM). This project focuses on understanding the ionized gas component of the ISM in the disks of the nearby Andromeda (M31) and Triangulum (M33) galaxies. The SIP interns will work with multi slit spectra obtained by the mentoring team using the DEIMOS instrument on the Keck II 10-m telescope. The interns will use Python spectroscopic data analysis techniques to detect and carry out a detailed characterization of the spectral emission lines associated with the ionized gas ISM component of these two galaxies, and draw conclusions about its rotational dynamics, chemical composition, and physical properties.

AST-10: The Kinematics, Physical Conditions, and Chemical Abundances of Ionized Gas in the Andromeda Galaxy (M31) and a Comparison to the Triangulum Galaxy (M33)

Primary Mentor: Aparajito Bhattacharya

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Inters: 4

The space between stars within galaxies is filled with interstellar medium (ISM), a cocktail of various gases and cosmic dust. In the vicinity of massive stars, and in star-forming regions, these gases get ionized and give off characteristic ISM spectral emission lines. The Andromeda galaxy (M31) and the Triangulum galaxy (M33) are spiral galaxies in the Local Group with active star forming regions. They provide an excellent opportunity to study the dynamical properties, physical conditions (e.g., density, temperature), and chemical composition of the ISM, through emission lines. For this project, the SIP interns will use data from the DEIMOS spectrograph of the Keck II 10-m telescope collected by the mentoring team as part of the Spectroscopic and Photometric Landscape of Andromeda’s Stellar Halo (SPLASH) survey and the Triangulum Extended (TREX) survey. The kinematics of the ionized gases due to rotational dynamics of the galactic disk, and any deviation from it, will be measured using the Doppler shift of ISM emission lines. The SIP interns will also study the chemical abundances of various components of ISM, and look for Rare Emission Lines In the Keck Spectra (RELIKS).

AST-11: Measuring the Completeness and Contamination Rate of RR Lyrae in the Photometric NGVS Dataset

Primary Mentor: Yuting Feng

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 3

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 (cadence). 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 research project will analyze two different types of variability: (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, and (2) variability of distant quasars caused by stochastic accretion of material onto the supermassive black holes that power them.

AST-12: Canada-France-Hawaii Telescope Legacy Survey Deep Fields: Lomb-Scargle Analysis of Variable Star Candidates

Primary Mentor: Spencer Jaseph

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 3

Studies of the density profile, substructure, and kinematics of the Milky Way’s extended stellar halo tell us about our Galaxy’s accretion history and dark matter content. It has long been recognized that RR Lyrae stars can serve as useful tracers of the Milky Way’s stellar halo. These stars have a characteristic periodic pattern of brightness variations that distinguish them from other astronomical sources of comparable apparent brightness and color. Moreover, RR Lyrae are excellent standard candles, and they can also be used to measure the chemical abundance of the halo. The Canada-France-Hawaii Legacy Survey (CFHLS) used the 3.6-m Canada-France-Hawaii Telescope and MegaCam imager to obtain a series of deep images in five filters (ugriz) along four lines of sight. In this research project, SIP interns will use the CFHLS database to study distant RR Lyrae stars.

AST-14: A Redshift Survey of Galaxies and Quasars in the Background of the Andromeda and Triangulum Galaxies

Primary Mentor: Atirath Dhara

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 3

Astronomers have carried out redshift surveys of distant galaxies to study the evolution of galaxy properties over cosmic time. The mentor’s research team used the Keck II 10-meter telescope and DEIMOS spectrograph to carry out the SPLASH spectroscopic survey of stars in the halo of the Andromeda galaxy (M31) and the TREX survey of stars in the halo of the Triangulum galaxy (M33), our nearest large galactic neighbors. An unintended by-product of the SPLASH and TREX surveys is a spectroscopic redshift survey of a few thousand distant galaxies and a couple hundred quasars in the background of M31 and M33. What is special about these background objects is that, unlike other distant galaxy redshift survey targets, these objects are star-like in terms of their image morphology (compact angular sizes). It is interesting to compare the physical properties of these point-like galaxies and quasars in the background of the M31 and M33 halos – e.g., redshift distribution, color-magnitude distribution, emission line strengths/ratios, etc. – to those of galaxies and quasars targeted in traditional redshift surveys. The SIP interns will apply the MARZ code to the reduced 1D spectra of galaxies and quasars in the background of M31 and M33 in order to determine their redshifts (via the Doppler shift method). The interns will also carry out a variety of other spectroscopic and photometric measurements (e.g., emission line strengths and line ratios, color-magnitude diagrams, etc.). A comparative analysis of the properties of the SPLASH and TREX survey background galaxy/quasar sample with the properties of the galaxies/quasars in the DEEP2/DEEP3 redshift survey will be particularly relevant.

AST-15: The SALVATION Project: Patterns in Spectroscopy

Primary Mentor: Rafael Nunez

Faculty Advisor: Monika Soraisam

Number of Interns: 4

The Spectroscopic Analysis of Luminous Variables and Transients in our Neighbor (SALVATION) project works to extend the documentation of luminous stars exhibiting photometric variability and exotic transients in Andromeda (M31). With the larger number of stars and higher metallicity than our own galaxy there is a rich variety in the stellar population of M31 that can be observed. The SALVATION project utilizes the alert broker ANTARES to ingest alerts from the ZTF survey and run filters on them that determine whether an alert is of interest in real-time. We follow-up interesting candidates with Lick Observatory Shane 3-meter telescopes Kast Dual Channel Spectrograph.

AST-16: Canada-France-Hawaii Telescope Legacy Survey Deep Fields: Properties of Quasars and Active Galactic

Primary Mentor: Nuclei Casey Peters

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 3

The Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) has a Deep Fields component, and the time-domain aspect of that data set forms the basis of this research project and the AST-12 project. The Deep Fields component consists of deep multi-epoch imaging of four 1 square degree fields in the ugriz filters. The CFHTLS Deep Fields data set has excellent single-epoch depth/photometric precision and spectacular cadence: well over 1000 measurements across the five bands over a period of about a decade. This research project will focus on quasars (a.k.a. quasi-stellar objects of QSOs) and active galactic nuclei (AGNs) in the database – i.e., sources powered by accretion disks around supermassive black holes that exhibit aperiodic short-term and long-term brightness variations.

AST-17: Cosmological Galaxy Simulations

Primary Mentor: Clayton Strawn

Faculty Advisor: Prof. Joel Primack

Number of Interns: 3

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. This project will 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.

AST-18: Time-Series Spectroscopy of RR Lyrae Stars in the Milky Way

Primary Mentor: Roy Doyel

Faculty Advisor: Prof. Raja GuhaThakurta

Number of Interns: 4

RR Lyrae stars are important tracers in the study of the structure of our Milky Way galaxy because of two characteristics: (1) they have a characteristic pattern of periodic brightness variations; and (2) they are “standard candles”, meaning that they all have roughly the same time-averaged luminosity. Moreover, the stellar pulsations that RR Lyrae undergo provide insight into the interior structure of stars and the stellar evolutionary processes of stars that are located in the so-called “instability strip” in the Herzsprung-Russell diagram. This research project will explore time-series spectra of relatively bright RR Lyrae in the Milky Way to better understand: (a) how to use these stars as kinematical/dynamical tracers of the Milky Way’s dark matter halo; and (b) the physics of stellar pulsations.

AST-19: Classification of Small HI Cloudlets in the High Velocity Cloud — AC Complex

Primary Mentor: Dr. Yong Zheng

Faculty Advisor: Dr. Raja GuhaThakurta

Number of Interns: 4

Just like clouds floating in the atmosphere of the Earth, there are numerous small clouds made of neutral hydrogen floating in the hot corona of the Milky Way. A classification of these clouds will help understand some of the most fundamental questions related to the evolution of the Milky Way. For example, how are stars formed? What will the Milky Way look like in the next few billion years? This research project will aim at classifying the physical properties (size, shape, mass, etc.) of these hydrogen clouds and understanding how the evolution of these HI clouds affect the evolution of the Milky Way.

BME-01: Phasing Long Reads and Evaluating Diploid Assemblies

Primary Mentor: Mobin Asri

Faculty Advisor: Prof. Benedict Paten

Number of Interns: 5

“The genome of a human being is diploid, which means that it inherits one copy from each parent. Each parental copy is usually referred to as a haplotype. A diploid genome assembly ideally represents the sequence of an entire genome including the sequences of both haplotypes. There has a huge effort to develop automated assembly tools that can produce diploid assemblies. However these automatically produced assemblies may have different types of errors like collapsed sequences or false duplications. One way to find and fix such issues is through aligning reads (short sequences of DNA) to assemblies. Reads can be produced by different sequencing technologies like Oxford Nanopore Technologies or Pacific Biosciences.
Flagger is an under-development pipeline that takes the read alignments to a diploid assembly and flags the misassembled blocks. These blocks are identified based on the coverage of the aligned reads. One part of this project is to fine-tune the parameters of this pipeline and evaluate its output. One obstacle for read-based evaluation pipelines is that in some homologous regions of the genome where the paternal and maternal haplotypes are highly similar, it is not easy to find the correct location of the reads. Having errors in both assembly and reads will add to the difficulty of this problem.
SecPhase is another under-development tool that takes the read alignments as input and calculates marker consistency scores for all of the alignments of the same read. It will revisit the best alignment for each read based on the calculated scores. The second part of this project is to fine-tune and evaluate the output of SecPhase.”

BME-02: SARS-COVID-19 Variants Study

Primary Mentor: Gepoliano Chaves

Faculty Advisor: Nader Pourmand

Number of Interns: 8

BME-03: Comparative Genomics of Encephalitozoon hellem Strains

Primary Mentor: Anne Caroline Mascarenhas Dos Santos

Faculty Advisor: Dr. Jean-Francois Pombert

Number of Interns: 3

Microsporidia are a group of understudied fungi-related obligate intracellular parasites and the human-infecting species (e.g. Encephalitozoon spp.) are category B priority pathogens by the CDC. The Encephalitozoon species are known to have the smallest known eukaryotic genomes, which makes them excellent candidates to study parasitism from a genomic perspective. In this project, we sequenced the Encephalitozoon hellem 50451 genome with long- and short-read sequencing technologies and aim to assemble and annotate the genome using freely available tools, and performs comparative genomics analysis between different E. hellem strains, which could help in the development of future outbreak traceability approaches.

CHE-01: Single Atom Catalysts Towards Hydrogen Evolution Reaction: A Theoretical Study

Primary mentor: Qiming Liu

Faculty Advisor: Prof. Shaowei Chen

Number of Interns: 3

Single atom catalysts (SAC) have been hailed as a novel candidate for the electrochemical production of hydrogen gas. In this study, single atoms of noble metals will be embeded in trasition bulk metal like Cobalt. We will use density functional theory (DFT) to understand how will the coblat matrix interact with noble metal atoms and affect the hydrogen evolution reaction. Quantum Espresso will be used as the calculation tool to understand the electronic structures of the materials including the electron density of states and charge transfer happened between the matrix and the isolated noble metal single atoms. H adsorption will also be probed theoretically. The mentor will be teaching the basic knowledge of catalysis and theoretical calculations, as well as some basic commands of Linux system.

CHE-02: Review of Carbon-based Materials Applied in Zinc Metal Anodes for Aqueous Rechargeable Batteries

Primary Mentor: Dun Lin

Faculty Advisor: Prof. Yat Li

Number of interns: 3

“Zinc-ion batteries (ZIBs) are regarded as a promising candidate for next-generation energy storage systems
due to their high safety, resource availability, and environmental friendliness. Nevertheless, the practical applications of ZIBs have been impeded by the instability of the Zn metal anodes. Carbon-based materials, with various unique merits, have become one of the most popular choices to be incorporated with high-performance n metal anodes for
improved stability. At this moment, it is highly necessary to overview the numerous published works of carbon-based materials applied in Zn metal anodes in order to inspire their future developments.”

CPM-01: Developing VR Games for Psychosocial and Social-Emotional Support of Special Needs Populations

Primary mentor: Tiffany Thang

Faculty Advisor: Dr. Sri Kurniawan

Number of Interns: 5

This project focuses on the development of virtual reality games to supplement existing resources for psychosocial and social-emotional support for special needs populations, such as individuals with Autism Spectrum Disorders (ASD) or children with Cleft Lip and/or Palate (CLP). This summer we will learn more about our user population and how to develop a serious VR game that addresses their psychosocial and social-emotional needs and supports their well-being.

CPM-02: Educational Technologies and Data Analysis

Primary Mentor: Dustin Palea

Faculty Advisor: Dr. David Lee

Number of interns: 3 This study asks the question: How might we design systems that scale experiential learning? To investigate this question, we’re working on building a crowd annotation platform called Annota. This web application introduces learners to the qualitative coding process by allowing them to make annotations oninterview transcripts. Importantly, they are not only practicing their annotation skills but (we believe) that they are also producing valuable data that can then be used to help other learners i.e. learnersourcing. One simple example is learners that may ask: How many annotations should I make on this interview transcript? By having many students annotate the same transcript on our platform we may be able to answer this question by providing learners with the average number of annotations that others have made. Our hypothesis is that if we are able to provide this and similar data to learners, then they will be able to learn qualitative analysis methods in a more scalable manner. Rather than teaching through traditional methods which can be costly and thus limited (e.g. apprenticeship learning), our hope is to provide more opportunities to learners who can instead rely on technology and their peers. This study aims to identify what system design and pedagogical strategies are well suited to support this.

CPM-03: Exploring Novel Forms of Large-scale Interaction

Primary Mentor: Kehua Lei

Faculty Advisor: Dr. David Lee

Number of Interns: 3

The goal of this research is to explore novel large-scale interaction design. We now have two research directions. The first one is exploring activities for small groups that can not only support group bonding but also help groups to contribute to their communities while they are participating in the activities. We designed a platform providing such small group activities for team leads to browse and choose from. The other direction is exploring and understanding algorithms and interactions for conversations with large groups of people at a time.

CPM-06: Wave Function Collapse for Music Metacreation

Primary Mentor: Cassandra Ravenbrook

Faculty Advisor: Prof. Nathan Altice

Number of Interns: 5

Music metacreation is a field of study interested in the analysis and development of systems that create music. Wave Function Collapse (WFC) is a constraint solving algorithm based on adjacency rules. WFC has been explored in the creation of visual video game assets. However, it’s affordances for generating music from underlying rules has not been deeply examined. To apply WFC to a musical context we will develop model the relationship between musical elements as adjacency rules. We will apply these models to a collection of songs and use WFC to generate new songs within the same genres.

CPM-07: Design and Development of a Social Wearables Kit for Use in Educational Live Action Role Play

Primary mentor: Selin Ovali

Faculty Advisor: Prof. Katherine Isbister

Number of Interns: 4

Our aim is to create modular, packaged material to instruct people to facilitate a short LARP camp (a structured, live-action roleplay experience that teaches through social enactment and reflection). The goal of the camp is for K-12 students to learn to build and interact with social wearable devices. There have been previous iterations of the camp, so the students will not only be doing new research, but also synthesizing existing data and creating new material. This project lies at the intersection of social emotional technologies, edu-LARP, and enhancement of learning through collaboration. This project exists within the same ecosystem as CPM-04.

CPM-08: “Making Games Research Reproducible / Searching for “”Balanced”” Games”

Primary Mentor: Samuel Shields

Faculty Advisor: Prof. Eddie Melcer

Number of Interns: 3

“Game Design contains several “Dark Arts”: design skills that are extremely difficult to quantify, have few generalized approaches that will work across use cases, and are highly sensitive to changes in environment. For example, approaches to making a “balanced” game ecosystem can vary from applied data analysis to intense user testing to extended theory discussions. Game balance changes over time, even without developer changes to the game: players learn new strategies, new mechanics, and new social dynamics to keep changing the multiplayer feel of the game.

Creating games successful in design “Dark Arts” is a recipe for a healthy, long-lived game and large player base. However, due to their hard-to-pin-down nature, doing so also involves an insane amount of human and compuer overhead to create. Studios often report significant crunch and resource shortages when attempting to get these emergent properties to work in their games. Automated Game Design (AGD) is a relatively new research practice of creating systems that procedurally generate new games with novel artifacts, mechanics and rules. These systems are interesting as they can search wide ranges of hypothetical games for gameplay characteristics that we might find interesting, fun, or otherwise. We can also use them to try and identify what constraints might bring out games with special emergent properties.

This project’s aim is to extend existing AGD projects at UCSC (BrawlerAGD, WarFront) to investigate new automated approaches to optimizing emergent properties in games such as “balance” with less human and computational resources than present day. This project specifically includes 1) the creation of a multiplayer-strategy game generator and 2) the creation of new AI agents and heuristics for a fighting game generator. Students will be asked to contribute to both game design and research tasks as the AGD is tuned to create different types of games and interactions. These produced artifacts will then be run through user testing to understand quality differences between different generation procedures.”

CPM-09: Developing VR Games for People with Autism Spectrum Disorder (ASD)

Primary Mentor: Achi Mishra

Faculty Advisor: Prof. Sri Kurniawan

Number of Interns: 5

This project focuses on the development of an independent living and self-care based virtual reality (VR) game for individuals with Autism Spectrum Disorder (ASD). The game will be rooted in already existing research about ASD and VR serious games. At the completion of the project, we aim to contribute to this body of research in a meaningful way while taking into account user specific needs as well as technical requirements.

CPM-10: Scenario Generation for Autonomous Vehicle

Primary Mentor: Abdul Jawad

Faculty Advisor: Dr. Jim Whitehead

Number of Interns: 3

Scenario-based testing of autonomous vehicles (AVs) in virtual environments has become an essential component of vehicle safety validation efforts because it is scalable, cost-effective, and safe. Critical and challenging scenarios for AVs are the key part of scenario-based testing. The recent trend for generating these critical and challenging scenarios is using game engine-based simulation tools such as Carla (an Unreal game-engine-based simulator) and Apollo (a Unity game-engine-based simulator). This research project will explore techniques for generating critical and challenging scenarios in the open-source simulation tool Carla. Specifically, critical scenarios that arise due to human limitations (cognitive, perceptive, motor, etc.) will be the core interest of the project. SIP interns will get involved in programming in Python. They will explore existing literature in the related fields (AV testing, driver behavior modeling, scenario generation).

CSE-01: Designing a Live American Sign Language (ASL) Interpreter Using Deep Learning-based NLP

Primary Mentor: Saeed Kargar

Faculty Advisor: Faisal Nawab

Number of Interns: 5

Artificial Intelligence is transforming every aspect of our life. Every day, we are witnessing new kinds of developments that would not have been possible without AI. For example, AI can help people with disabilities by making it easier for them to participate in everyday activities. It can benefit them by (1) helping them to communicate with others and being connected such as keyboard navigation optimization, which helps people with physical disabilities, or Audio descriptions content, which can be useful for people with a visual impairment; (2) helping them gain more autonomy when they’re getting around, such as Wheelmap, and Soundscape, which help wheelchair users and blind people to have a better experience when they are out to the city; (3) helping people with disabilities to Live independently such as smart virtual assistants and smart home. Sign language translation is the natural language processing task of translating between sign language signs and spoken language text. In this interesting and advanced deep learning project, we aim to make the communication for the hearing or speech-impaired people easy and hence will implement a sign language interpreter, which automatically recognizes and interprets live video feed of a person signing in American Sign Language from the camera to spoken language text. To implement this project, the students will learn various skills, tools, and concepts such as (1) data mining (2) advanced libraries and open-source platforms such as TensorFlow, Keras, OpenCV, (3) designing and implementing advanced deep learning models (4) using pre-trained models and getting familiar with transfer learning, and (5) training a CNN on the captured dataset and predicting the results. The main application of this project is to provide aid for the hearing- and speech-impaired to communicate with those who do not know the sign language. This summer, the interns will learn designing and implementing a real-world application of deep learning models. For this aim, the SIP interns will learn various deep learning concepts and tools — e.g., using the TensorFlow and Keras libraries, pre-trained models such as the MobileNet network, and popular online tools such as Google Colab and Jupyter notebook to solve programming problems. Furthermore, they will learn how to read research papers and implement them.

CSE-02: Deep Learning Algorithms for Application in Wound Healing

Primary Mentor: Sam Teymoori, Hamed Tangestani

Faculty Advisor: Marcella Gomez

Number of Interns: 3

“Artificial intelligence, such as machine learning and deep learning, offers incredible value to biology. By using machine learning techniques, it’s possible to analyze a great amount of biology-related images, which can be found in publicly available research papers.
One of the applications of deep learning is innervation images in wound healing. Wound healing requires the cooperative activity of numerous cell types and information guiding cells to grow and implement tissue morphogenesis to a specific (fully regenerative) outcome. In order to accelerate and improve the wound healing response, it is essential to provide instructive signals that mimic similar processes in highly regenerative animals. The most important conduits for such signals known to date are the nervous system and migratory immune cells. Innervation is a critical component of the repair process; not only is it required for regeneration in organisms that successfully repair complex tissues and organs.
In this project, we will introduce the mathematical concepts underlying Deep Learning, and through Python, step by step, we will make a deep learning model for biomedical images in Wound innervation. For this project, we will use the innervation images which has been treated with different drugs to train a deep learning model to quantify the growth of innervation.”

CSE-04: Origami Robot: Modeling and Simulation

Primary Mentor: Samira Zare

Faculty Advisor: Dr. Mircea Teodorescu

Number of Interns: 3

Deployable structures have been proposed as alternatives to rigid structures in applications where payload size is restricted. 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 Python to analyze and understand their movements.

CSE-05: Reinforcement Learning Application in Robotic System

Primary Mentor: Pegah Ojaghi

Faculty Advisor: Michael Wehner

Number of Interns: 3

Autonomous learning of in-hand manipulation is a litmus test for bioinspired robots and artificial intelligence (AI) algorithms. One of the applications of artificial intelligence algorithms is autonomously learning in-hand manipulation. The human hand’s ability to interact with the world is important to our biomechanical, manipulative, perceptual, cognitive, psychological, social, linguistic, and artistic everyday activities. Recently, dexterous manipulation of objects, which is a fundamental everyday task for humans, has attracted the attention of many researchers in the robotic community. Despite the necessity of dexterous manipulation for robotic systems that operate in a wide variety of human-centric environments, achieving dexterous manipulation for autonomous robots is challenging. In this project, we will introduce the mathematical concepts of reinforcement learning and how to set it up for the application in robotic systems. We will use OpenAI gym and MuJoCo engine to implement the RL in robotic application.

CSE-06: Spike Sorting: Basis and Implementation

Primary mentor: Jinghui Geng

Faculty Advisor: Prof. Mircea Teodorescu

Number of Inters: 3

Electrophysiology in neuroscience is a way to study the electrical properties of cells and tissues. The spiking activity in neuronal cells reveals critical information, such as complex spatiotemporal patterns and persistent activities. Spike sorting is a standard tool to detect neuronal activities from electrophysiological data. In this project, the interns will understand how spike sorting works and run a spike sorting on neural recordings. They will further analyze the result to extract statistical features and visualize the data in time series. The interns will have the freedom to explore spike sorting algorithms, discover neural network transitions, and use machine learning to characterize the neuronal cells.

CSE-07: Implementing Neural Network from Scratch to Using Library (PyTorch)

Primary Mentor: Pooneh Safayenikoo

Faculty Advisor: Prof. Andrew Quinn

Number of Interns: 4

“Deep learning is the next wave of Artificial Intelligence (AI) it has a wide range of applications from image classification, speech recognition to natural language processing. Deep learning also has a lot of attention recently in small devices such as phones, robots, and self-driving cars. Artificial Neural Networks (ANNs) use to solve highly non-linear problems like recognition, classification, and segmentation. The solution is mostly obtained using a network of deep convolutional and/or fully connected layers with many filters in each layer.
In this program, the interns will learn how neural networks work and how they can implement a simple neural network in python. Also, they learn how to build and deploy a real-world deep learning model application this summer. To achieve this goal, interns will learn about numerous deep learning ideas and tools, such as using the PyTorch libraries, pre-trained models from the small network like LeNet-5 to bigger networks such as ResNet network, and popular datasets like MNIST, and CIFAR datasets, and how to use GitHub, and Jupyter notebook to tackle programming challenges. They will also learn how to read research papers and put them into practice.”

CSE-08: Autonomously Driving Tricycle

Primary Mentor: Won Ko

Faculty Advisor: Prof. Dejan Milutinovic

Number of Interns: 3

Among mobile robot designs with two, three, and four wheels, the three-wheel variant, or tricycle, is the least considered one. On the other hand, tricycles are very popular in the design of recreational, sturdy and lightweight, or energy-efficient vehicles. The tricycle kinematics is very interesting considering that it can be driven by the front or back wheel(s) and the analysis is necessary to understand trade-offs in the tricycle design. Once its geometry is fixed, for the autonomous navigation of the tricycle, the mounted camera has to be calibrated so that it can be used as the source of information for a navigation algorithm.

CSE-09: Citizen Science Mobile Apps with Integrated Machine Learning Models

Primary Mentor: Fahim Hasan Khan

Faculty Advisor; Prof. Alex Pang

Number of Interns: 3

Citizen science involves the participation of non-scientists in data collection according to specific scientific protocols and in the process of using and interpreting that data. Increasingly, citizen science platforms are going mobile with the growing power of mobile computation. The mentor’s research involves developing an open-source software platform that allows a domain researcher to quickly create a citizen science mobile app with integrated machine learning (ML) models for collecting data with real-time analysis. The mentor is currently working on creating ML-powered mobile apps and server-side infrastructures of the citizen science platform. In this SIP research project, the interns will help the mentor to develop and test the citizen science mobile apps and use them to collect data. The collected data will be later used for more training and optimizing the ML models.

CSE-10: Adaptive, Dynamic Load Balancing for Data Center and WAN Traffic

Primary Mentors: Lakshmi Krishnaswamy

Faculty Advisors: Dr. Katia Obraczka

Number of Interns: 3

Geo distributed data centers are an example of low latency, multi-hop networks. They need to handle application traffic of different characteristics. With the large-scale deployments of 5G in the near future, there will be even more applications, including more bulk transfers of videos and photos, augmented reality applications and virtual reality applications which take advantage of 5G’s low latency service. With the development and discussion about Web3.0 and Metaverse, the network workloads across data centers are only going to get more varied and challenging. All these add to heavy, bulk of data being sent to the data centers and over the backbone network. These traffic have varying quality of service requirements, like low latency, high throughput and high definition video streaming. Wide area network (WAN) flows are typically data heavy tasks that consist of backup data taken for a particular data center. The interaction of the data center and WAN traffic creates a very interesting scenario with its own challenges to be addressed. WAN and data center traffic are characterized by differences in the link utilizations and round trip times. Based on readings and literature review, there seems to be very little work on load balancers that address the interaction of data center and WAN traffic. This in turn motivates the need for designing load balancers that take into account both WAN and data center traffic in order to create high performance for more realistic scenarios.

EEB-01: Can Cryptic Female Choice Create New Species?
Primary Mentor: Matthew Kustra
Faculty Advisor: Dr. Suzanne Alonzo
Number of Interns: 3
Post-mating prezygotic reproductive isolation barriers are reproductive barriers that occur after mating but before fertilization. Growing empirical evidence suggests that such barriers may be important in preventing individuals from different species reproducing with each other. However, there currently exists little to no theory on if these processes can help prevent individuals from different populations reproduce with each other. In this research project, the SIP mentor and mentees will develop mathematical models and code simulations to investigate the extent to which cryptic female choice (when females bias fertilization to favor a specific male) may contribute to the creation of new species.
EEB-02: Behavior of African Carnivores at Experimental Marking Sites
Primary Mentor: Whitney Hansen
Faculty Advisor: Dr. Suzanne Alonzo
Number of Interns: 3
Post-mating prezygotic reproductive isolation barriers are reproductive barriers that occur after mating but before fertilization. Growing empirical evidence suggests that such barriers may be important in preventing individuals from different species reproducing with each other. However, there currently exists little to no theory on if these processes can help prevent individuals from different populations reproduce with each other. In this research project, the SIP mentor and mentees will develop mathematical models and code simulations to investigate the extent to which cryptic female choice (when females bias fertilization to favor a specific male) may contribute to the creation of new species.
EEB-03: Influence of Aquaculture Gear on Seagrass Communities
Primary Mentor: Ben Walker Prof.
Faculty Advisor: Kristy Kroeker
Number of Interns: 3
The physical and biological presence of oyster aquaculture can have a variety of effects on local communities. Within a coast-wide effort to analyze the effects of aquaculture gear on seagrass beds and their ecosystems, cameras were deployed for 24 hour intervals at several sites in Northern California. Video footage was taken in seagrass and mudflat habitats, within and outside of aquaculture gear areas. Data from this footage needs to be processed and analyzed to understand the effects of oyster farming.


ECO-01: Social Impact Start-Up Entrepreneurship

Primary Mentor: Kati Greaney

Faculty Advisor: Kati Greaney

Number of Interns: 4

The mentor is working on a start-up project that aims to create a centralized platform where community members can come together to support a person or family when they are going through a difficult time. The focus of the research is in the area of grief and loss and seeks to understand how people can show support for one another in a meaningful way during a time of need. The team will be utilizing a design thinking methodology and developing a business model canvas around the findings from interviews. Additionally, interns involved in this project will have the opportunity to develop their own start-up business ideas and work on them through a series of modules and weekly presentations.

ELE-01: Decentralized Energy Management in Smart Grids

Primary Mentor: Fargol Nematkhah

Faculty Advisor: Prof. Yihsu Chen

Number of Inters: 3

High penetration of distributed energy resources (DERs) such as photovoltaic (PV) panels into electric grids along with the proliferation of new loads such as electric vehicles are challenging the traditional methods of operation and transforming the conventional grid into a cyber-physical system called the smart grid. Accordingly, the distributed nature associated with DERs calls for distributed management schemes where resource owners would be able to make decisions autonomously while satisfying the grid’s needs such as supply-demand balance and transmission lines’ capacity. To get a glimpse of how such schemes might work, consider the scenario where a PV panel has created more power than expected for the next five minutes; the surplus power can be used by another consumer at a different location who is in need of power while respecting the operational constraints of the grid. This method of power provision unburdens the electric grid and offers economic opportunities for resource owners. This summer, associated interns will gain insight on the basic principles of electric grid operation and get familiar with the classic and novel optimization problems in the context such as economic dispatch, unit commitment, and optimal power flow. They will learn to effectively read academic papers of the field and to transform their ideas into research work through mathematical modeling and computer programming.

ELE-02: DNA Hybridization Detection Using Fiber Optic Surface Plasmon Resonance Biosensor

Primary Mentor: Kamrun Nahar

Faculty Advisor: Prof. Ahmet Ali Yanik

Number of Interns: 3

Biosensors are the devices that are very important for many industry applications, such as medical diagnostics like DNA hybridization detection, Covid- 19 , enzyme detection, food safety, environmental monitoring etc. DNA hybridization detection is very important because more than 400 diseases are directly diagnosable and the number is growing. Fiber optic sensor is a kind of sensor that enjoys the advantages of compactness, light weight, high sensitivity and remote sensing, etc. Fiber-optic surface plasmon resonance (SPR) biosensor utilizes the surface plasmon waves (SPW) at the interface of metal and dielectric to probe the interactions between biomolecules and sensor surface. The interns will learn basic simulations of fiber optic-based surface plasmon resonance biosensor for DNA hybridization detection.

ELE-03: Design and Analysis of a Prism-based Surface Plasmon Resonance Biosensor

Primary Mentor: Reefat Inum

Faculty Advisor: Prof. Ahmet Ali Yanik

Number of Interns: 3

“The integration of a particular biological element with a sensor results in a
typical analytical device known as a biosensor, which is used to detect a target analyte. Biosensors, in addition to being employed in applications ranging from environmental monitoring to drug discovery, can fundamentally operate as low-cost and extremely efficient tools for early disease diagnosis. Among different biosensing techniques, Surface Plasmon Resonance (SPR) based optical biosensors offer label-free, highly reliable, ultrasensitive, and
real-time detection of target particles. SPR based sensing works on the generation of a surface wave at the metal-dielectric interface, resulting in a dip in the reflected light intensity at a resonance angle that is supposed to shift in proportion to the deposited mass on the sensor surface. The mentor’s research group is developing a custom-built SPR setup to run the sensing experiments. The interns will work on the preliminary simulation analysis with the mentor to optimize the parameters for the experimental protocol.”

ELE-04: Privacy of user data in smart Electric Power Systems

Primary Mentor: Shourya Bose

Faculty Advisor: Yu Zhang

Number of Interns: 3

USA and the world are rapidly moving to smart electric power systems (for example, smart grids) due to the wide variety of advantages they provide. In this context, ‘smart’ means that on top of electrical distribution infrastructure, there are computers and networking devices facilitating operation of said power systems. Such smart systems collect huge amounts of user data for their operation, and as we know, privacy of user data is paramount. In this project, we will carry out a preliminary analysis of data privacy issues which arise in smart grids.

ENV-01: Causes and Impacts of Urban Sprawl

Primary Mentor: Nazanin Rezaei

Faculty advisor:

Prof. Adam Millard-Ball

Number of Interns: 3

Urban form is one of the most central topics in the study of urbanism. Due to its negative environmental, social, and economic impacts, urban sprawl is considered as the unfavorable display of urban form. Most definitions of urban sprawl refer to the low-density and unplanned expansion of urban areas into suburbs. Moreover, it is widely recognized that transport infrastructure development plays a major role in shaping urban form. The aim of this project is to identify to what extent urban sprawl is affected by transport infrastructure, as well as assessing the impact of urban form on the environment.

ENV-02: Determining Drivers of Leaf Trait Development of California Coastal Plants

Primary menotr: Justin Luong

Faculty Advisor: Dr. Michael Loik

Number of interns: 3

Plant leaf traits have the potential to aid ecological restoration management, however, trait data is typically only collected for common species or special species of interest (i.e. endangered). For less common plant species, which contribute to the vast biodiversity in California, trait data are limited. There may be potential to predict trait values for less common plants that have not been measured because closely related species, and those in similar climates, may have similar traits. Our work seeks to determine whether leaf traits from three major plant families (Asteraceae, Fabaceae, Poaceae) are related to climate or plant evolutionary history (phylogenetics).
ENV-03 Estimating the Population Size of an Understudied Carnivore, the Fanaloka (Fossa fossana) Dr. Asia Murphy Dr. Chris Wilmers 4 The fanaloka (Fossa fossana) is a small fox-like carnivore from Madagascar. Determining population sizes is important for accurately listing the conservation status of species. The mentor and interns will work to identify individuals from pictures using their unique spot patterns, and from there, estimate how many animals are at research sites.

LAL-01: Kids and Care Work: Emotional Labor in Latino Mixed-Status Families

Primary Mentor: Karina Ruiz

Faculty Advisor: Dr. Jessica Taft

Number of Interns: 3

“Since the 1986 Immigration and Reform Act effectively illegalized migration, mixed-status families have become commonplace in the U.S. and they are now a prominent part of the contemporary immigrant experience. Mixed-status families are often examined through the eyes of adults and focus on the perspectives of parents. Few studies have considered how children contribute to the dynamics of a family, as they are usually framed as dependent actors. Mixed-status families encompass various footholds in the economic market, and they also present a high use of reproductive labor. Specifically, mixed-status families engage in caring labor to sustain the family unit and also to support individual members of the family (Scheuths and Lawston, 2015). Children are aware of these dynamics of care and work. My study aims to (1) to identify how children learn and do emotional labor at home (2) To relate citizen children’s emotional labor to the power dynamics of the mixed-status family as a whole. Because of the theoretically generative context of mixed-status families, this project will need to identify emotional labor in complex family dynamics. The research questions used to accomplish these goals are: 1. What behaviors (self-conduct) do children contribute to the mixed-status family?
2. What stances (deliberately adopted conduct) do children contribute to the mixed- status family?
This project will offer examples in which children learn to do emotional labor, instances of emotion work and emotional labor for others, and an overview of the impact of kids’ emotional labor on family dynamics. All activities will take place in participants’ homes, with audio recorded interviews, and some video recording for children’s activities and interactions with caregivers. Analysis will require transcription of interviews, field notes, and captioning of video recordings. Data will be analyzed in the context of individual family dynamics and in reference to other families.”

LIN-01: Code-mixing & multilingual identity in Korean lyricism

Primary Mentor: Nikolas Webster

Faculty Advisor: Dr. Ivy Sichel

Number of Interns: 4

K-pop and other areas of Korean pop culture have seen increased global attention across the 2010s to present. While utilization of code-switching and Korean-English “Konglish” has been noted as a phenomenon in K-media, particularly in music lyrics, This is a socio-linguistic research project that is interested in investigating possible correlations between the utilization of Korean-English linguistic forms with the level of global audience/global attention. What linguistic forms does this global impact take? Has there been a change over time in the way linguistic forms have been utilized, as Korean pop culture has shifted from a national to largely global phenomenon?

MET-01: Arsenic and Pathogenesis

Primary Mentor: Juliana Nzongo

Faculty Advisor: Chad Saltikov

Number of Interns: 4

There is an urgent need to understand the risk factors associated with arsenic exposure and human health. One such emerging risk factor is the gastrointestinal tract (GIT) microbiome. The GIT microbiome is vital for digestion, proper nutrient absorption and regulation of the immune system, and is the first line of defense against ingested toxicants. This project is focused on investigating arsenic detoxification in gastrointestinal bacteria as a potential risk factor for human health. The lab strives to understand the mechanism of arsenic detoxification in bacteria exposed to environmental and gastrointestinal stimuli. This summer, SIP students, their graduate student mentor and faculty mentor will use bioinformatic tools to mine and analyze genomes of relevant bacteria for the prevalence and diversity of arsenic resistance genes. The SIP students will learn how to analyze large genomic data sets from pathogens and other metagenomic information available from the National Center for Biotechnology Information (NCBI). This will include aligning genomic data, building phylogenetic trees using python and R, and evaluating similarities and dissimilarities between candidate genomes.

MET-02: Automated Fog Collector

Primary Mentor: Meg Mathers

Faculty Advisor: Prof. Peter Weiss

Number of interns: 3

“Water can be collected from fog, which could potentially ease the effects of drought in California. An autonomous fog-sensing device is being designed and developed to collect fog water samples for the purpose of water analysis. Seven main features to be implemented: power grid independence, reduced fog detection latency, wireless data transmission, sample protection, automated doors, additional sensors, and on board electronics protection.”

MET-03: Toxin-Antitoxin Systems Prevalence in Microbial Systems

Primary Mentor: Caison Warner

Faculty Advisor: Prof. Manel Camps

Number of Interns: 6

Toxin-Antitoxin Systems are found in Bacteria, Archaea, and Bacteriophages. These systems produce both a toxin that can kill the host and an antitoxin which saves the host. These systems are numerous in hosts with antibiotic resistance, virulence, and phage defense. Given the increasing worries over antibiotic resistance, Toxin-antitoxin systems provide a new target for medicine. The mentor is interested in the prevalence of multiple systems found in the microbial world and their functions. The goal is to identify patterns of toxin-antitoxin system occurrence and infer their function. The mentor will teach interns how to use python and run various bioinformatics programs.

MCD-01: Neuronal population encoding

Primary Mentor: Dr. Brian Mullen

Faculty Advisor: Dr David Feldheim

Number of interns: 4

The Feldheim lab is interested in the superior colliculus (SC), a structure in the midbrain where visual, auditory, and somatosensory information are integrated to initiate motor commands. Sensory integration is key to perceiving and responding to our environment, however each individual neuron lacks consistency between individual trials to give a complete understanding of the environment. The population of neurons, i.e. circuit, will better inform the organism of its perceptions. As such, our lab has performed electrophysiogical recordings of neurons on awake mice while recording from the SC, while presenting various visual and auditory stimuli.

OCS-01: Quantifying the Social and Economic Benefits of Nature-Based Adaptation Solutions to Protect San Mateo County From Storms and Sea Level Rise

Primary Mentor: Rae Taylor-Burns

Faculty Advisor: Dr. Borja Reguero

Number of Interns: 4

Climate change is raising global sea levels and threatening coasts around the world with increased risk of flooding. The population bordering San Francisco Bay accounts for two-thirds of future flooding impacts in California. There is ample evidence for the beneficial role that wetlands can play in reducing flood risks, in addition to providing other co-benefits. While coastal development has altered or removed about 90% of the Bay’s historic tidal wetlands, restoring part of the Bay’s shoreline can help communities adapt to sea level rise. High and rising flood risk and opportunity for wetland conservation and restoration make San Francisco Bay well suited to serve as a study site for an investigation of the potential for marshes as nature-based solutions for flood defense – in particular, how marsh habitat conservation and restoration can protect the county’s levee system from overtopping, breaching, and hydraulic failure. The Coastal Resilience Lab at UCSC is looking for an undergraduate student to work with graduate student Rae Taylor-Burns and professor Borja Reguero to advance this work through a mixture of field and computer tasks.

PHY-01: Modeling Thermodynamics of Mono-crystallization Formation From Poly-crystalline Material Using a Chevron Beam

Primary Mentor: Soren Tornoe

Faculty Advisor: Dr. Nobuhiko Kobayashi

Number of Interns: 3

Mono-crystalline materials are both expensive and difficult to grow but are extremely valuable when it comes to the electronics industry, especially in the production of nano-scale components like transistors. One of the main methods for producing mono-crystalline materials requires growing them on a mono-crystalline substrate. However, this leaves the problem of attaining the substrate. Instead, this project focuses on a newer and less understood method: making poly-crystalline materials by forcing the material to undergo a phase change (solid to liquid and back to solid) through a technique called Laser Induced Crystallization (LIC). The goal of this project then, is to model the thermodynamics of the system to understand how this method is generating the single-crystal material.

PHY-02: The Dark Energy Survey Weak Gravitational Lensing Modeling Choices

Primary Mentor: Conghao Zhou

Faculty Advisor: Prof. Tesla Jeltema

Number of Interns: 5

Gravitational lensing, the phenomenon that describes the bending of light by mass, is our best probe of the large-scale structure of the universe. However, the mathematical formalism of gravitational lensing does not provide many direct intuitions. However, extracting cosmological information from the gravitational lensing data requires careful modeling which takes into account numerous systematic effects. In this project, we would participate in the collective effort of probing cosmology with state-of-the-art weak gravitational lensing data. In particular, we would contribute to the modeling choice of the Dark Energy Survey Y6 modeling key project. We would collaborate with world-class cosmologists and make our footprints in the history of human beings trying to understand the universe.

PSY-01: Earworms

Primary Mentor: Matt Evans

Faculty Advisor: Dr. Nicolas Davidenko

Number of Interns: 3

Have you ever had a song get stuck in your head? Probably! Nearly everybody regularly experiences “earworms,” but very little is known about how and why they happen. The scientific literature refers to earworms as Involuntary Musical Imagery (INMI), and empirical research on the phenomenon is quite limited. The SIP interns will help contribute to the scientific understanding of various aspects of this near-ubiquitous human experience.

PSY-02: Naive Biology: Do children think robots have biological insides?

Primary Mentor: Dr. Elizabeth Goldman

Faculty Advisor: Elizabeth Goldman

Number of Interns: 4

Robots are becoming a major part of our society. This research project aims to investigate how young children perceive 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. Specifically, we are investigating whether young children understand robots are mechanical and do not have the same biology as humans. In this research project, children will play a sorting game with the researcher. Children will be shown pictures of different robots, animals, and objects. The children will then be asked to identify whether something biological (e.g., bones) or mechanical (e.g., gears) goes inside the item in question. The SIP interns will then observe the children’s reactions and take detailed notes. This research project has already been designed and we are testing out the procedure with young children (e.g., making sure the young children can understand the directions and complete the study). 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 analyze the data they have collected. This research project could impact how robot designers create and build robots for young children. Join our team and discover how young children perceive robots!

PSY-03: Online Searching, Memory, and Metacognition

Primary Mentor: Dana-Lis Bittner

Faculty Advisor: Dr. Benjamin Storm

Number of Interns: 3

The internet is a vast and ever-growing source of information and we have come to rely on it more and more as an external memory store in recent years. The mentor is cognizant of the immense benefits that come with having access to all of this information at our fingertips, but wishes to explore potential negative or unwanted impacts of being able to look up information online that easily on our cognition and behavior. Some of the questions that are being explored in the mentor’s lab are “Do we grow dependent on the internet?”, “Does online searching change how we behave or think or assess our own knowledge?”, or “How does prior knowledge change how or whether we search for information online?”. More specifically, this summer, the mentor would like to investigate whether remembering with the internet (using the internet to jog your memory) could actually lead to inhibition or forgetting of other, previously learned information. Another avenue of research the mentor is thinking of exploring is investigating the metacognitive mechanisms underlying the Internet Fixation Effect (the increased likelihood to search for information online, following previous online searching). The mentor feels that the dynamic nature of online search engines and their use in everyday settings, such as work and education, makes for an exciting and highly relevant field of study.

PSY-05: Service-Learning Outcomes (academic, civic, and social) of College Students at a Hispanic Serving Institution: Does the Quality of the Courses Matter?

Primary Mentor: Miguel Lopezzi

Faculty Advisor: Dr. Regina Langhout

Number of Interns: 4

A service-learning course comprises three components: some class time, doing service in the community, and then writing reflections about what the student learned and experienced. Service-learning courses are important because they help students learn about social justice issues while also engaging 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 and service-learning programs. This summer, in this research project, we will work together to assess the quality of these courses using a service-learning course quality rubric. Then, we will see if these differences in quality relate to college student outcomes (academic, civic, and social) at a Hispanic Serving Institution using a diverse college student sample.

PSY-06: Immigrant People’s Communality

Primary Mentor: Daniel Rodriguez Ramirez

Faculty Advisor: Prof. Regina Langhout

Number of Interns: 3

One’s ability to make community is partly shaped by how one feels one belongs to a place and how one experiences social support, particularly for people who migrated to the US. Communality goes beyond making community, it refers to how people advocate for each other’s rights to thrive. Our study will transcribe and analyze interviews with immigrant people to better understand the factors that influence immigrants’ communality, from their own stories. Our project’s aim is to amplify immigrant people’s experiences advocating for each other and sharing resources to meet their needs in community, to inform strategies by which service-providers (e.g., clinics, schools, non-profits) can better mobilize resources for them during times of crises.

PSY-08: Brain Activity Underlying Visual Perception

Primary Mentor: Audrey Morrow

Faculty Advisor: Dr. Jason Samaha

Number of Interns: 3

This project uses cognitive neuroscience approaches to analyze brain activity associated with visual perception. Specifically, this mentor looks at electroencephalography (EEG) data, which is a record of electrical activity from cortical parts of the brain, to assess alpha waves and stimulus-locked event-related potentials (ERPs) from brain areas associated with vision. Alpha power and ERP amplitudes change during visual perception and are associated with changes in performance on perceptual tasks that use stimuli that are difficult to distinguish. The interns will gain an understanding of how these neural patterns are analyzed and what those analyses can tell us about brain activity when we attend to and perceive visual information.

PSY-09: Understanding how Minoritized Students of Color View Social Justice Research

Primary Mentor: Katherine Quinteros

Faculty Advisor: Dr. Rebecca Covarrubias

Number of Interns: 3

Minoritized students of color (e.g., first-generation, low-income) often challenge oppression through practicing resistant acts (e.g., challenging inequality, choosing to succeed in college). Research has focused on the various ways students practice resistance, but less so the consequences that resistance can have on students’ academic goals and well-being. This project aims to understand how resistance is linked to racial battle fatigue, which is the psychological, behavioral, and physical symptoms related to experiencing racism as a Person of Color. We will also explore how academic belonging and critical consciousness impact this relationship. The goal of this work is to better understand the experiences of minoritized students of color within a Minority Serving Institution (MSI) and offer recommendations for removing the barriers that minoritized students face which cause them to constantly need to practice their resistance.

PSY-10: Moral Disengagement in the George Floyd Media Coverage”

Primary Mentor: Jada Cheek

Faculty Advisor: Courtney Bonam

Number of Interns: 3

This project will focus on race and social justice; specifically how racial stereotypes not only apply to people, but also physical places. These space related racial biases can influence the environments around marginalized spaces and affect what resources are allocated to certain neighborhoods, profiling a physical space as lesser than when occupied by Black people vs. White people. The stereotypes and profiling can have a detrimental effect on the lives of the people that occupy them and maintain racial inequity.

PSY-12: Community-engaged Research with Youth Leaders

Primary Mentor: Sylvane Vaccarino

Faculty Advisor: Dr. Regina Langhout

Number of Interns: 3

Youth are not often considered as knowledgeable or meaningful contributors to improving their community. The mentor’s research project aims to change that by doing research with youth on making a positive change to their community. Youth are generally interested in the barriers to the community’s hopes and dreams. The project will analyze art based methodologies, audio transcriptions, and more. This project is a great introduction to social justice and community oriented projects.

PSY-14: Correctional Officer Abuse of Power

Primary Mentor: Jade Moore

Faculty Advisor: Dr. Craig Haney

Number of Interns: 3

The SIP interns will be investigating how correctional officers’ abuse of power may be impacting Black individuals in prison. Black individuals have historically been discriminated against in many areas of the U.S.’s criminal justice system (policing, sentencing, etc.). The SIP interns will be analyzing different media outlet reports (newspapers, broadcasts, etc.) to investigate how this discriminatory treatment may translate over in correctional institutions. The goals will be to try to establish if Black individuals are more often the targets of force in correctional settings and what kind of information the media has access to when investigating reports of correctional officers’ abuse of force.

PSY-15: Diverse Gender Construction Using Social Media

Primary Mentor: Zachary Keith

Faculty Advisor: Dr. Adriana Manago

Number of Interns: 3

Over the past two years TikTok has been a growing social media platform that allows for youth to scale their social connectivity and media consumption. Our project will seek to understand (1) the forms of media that youth are likely to engage with on TikTok (2) how gender diversity is represented in these media channels (i.e. gender non-conforming or transgender youth, gender ideologies, masculinity, femininity, or LGBTQIA+ identity construction). Interns will be able to tailor certain sections of the project to their own interests surrounding gender identity.

PSY-17: Political and Civic Identity on Social Media

Primary Mentor: Karinna Nazario

Faculty Advisor: Dr. Adriana Manago

Number of Interns: 3

Social media has played a part in many different movements in the past few years. Social media activism, which includes creating and sharing content arouind social issues, has been deemed “slacktivism” by some researchers, stating that online activism does not actually relate to offline activism. However, access and engagement on social media has been shown to provide exposure to adolescents about many different social causes, including movements like Black Lives Matter (BLM) and the #MeToo movement. This project is interested in how social media may be a tool for adolescents and emerging adults to explore different civic and social issues, which may play a big part in their civic identity development.

PSY-18: A Comment on Fake News

Primary Mentor: Elise Duffau

Faculty Advisor: Dr. Jean E. Fox Tree

Number of Interns: 3

The mentor is investigating what linguistic features are used to indicate hyper-partisan news. In other words, what flags certain speech or writing as hyper partisan. The mentor will test these linguistic features through manipulation of reddit comments and determine which correlate with the perception of highly polarized political speech.

SOC-01: Black Community-Based Education Curriculum

Primary Mentor: Theresa Hice-Fromille

Faculty Advisor: Dr. Rebecca London

Number of Interns: 5

Black community-based education spaces prioritize Black youth thriving. But what is a community-based education curriculum? How do Black community-based organizations build their curriculum and publicize their values, and to what ends? How does Black community-based education curriculum bring together local and global issues pertaining to race, gender, and youth? In this project, the mentor and interns will examine curriculum content in the form of fundraising material, public presentations, video and photographic documentation, art, and written narratives to determine (1) the curriculum produced by a Baltimore-based organization and (2) the ways in which the curriculum advances the organizations’ purported goals. This research utilizes concepts from sociology, feminist studies, critical race/ethnic studies, and education.

SOC-02: Mobilities (Migration and Higher Education), Gender, and Sexuality

Primary Mentor: Michelle Parra

Faculty Advisor: Dr. Julie Bettie

Number of interns: 4

How does pursuing mobility via migration and higher education shape US Latinas’ own gender and sexual identities as well as generational negotiations of gender and sexuality? Previous research finds that both migrating and attending college can shape people’s gender and sexual beliefs and behaviors. Scholars also note that undergoing a substantial mobility experience, such as migrating, can shape how Latinas employ generational negotiations of gender and sexuality. Less is known about how another mobility path, going to college, shapes the generational negotiations Latinas employ of these social forces. Hence, this project utilizes sociology, feminist studies, ethnic studies, and queer theory to examine how Latinas’ migratory and college-going experiences shape their own gender and sexualities and generational negotiations of these social forces within the family.

SOC-03: Latina Rural Girlhoods in the United States

Primary Mentor: Roxanna Villalobos

Faculty Advisor: Dr. Julie Bettie

Number of Interns: 4

Drawing from my own background, my dissertation project, “Latina Rural Girlhoods in the United States,” investigates the intersectional subject-formation and varied forms of mobility—economic, social, cultural, and spatial—of Latina girls that live in California’s rural Central Valley and have direct ties to region’s agricultural sector. The intervention promised by this research is to understand and shed light on racialized rural girlhood, an experience that largely remains understudied in the context of the U.S. This project asks 1) How does a rural region with a sizeable Latinx immigrant majority produce Latina girls’ gender, racial, class, and immigrant subjectivities? 2) How does growing up in poor rural communities of color impact Latina girls’ life trajectories and their opportunities for different types of upward mobility? Through this research, I employ a transnational feminist approach to examine how discourses of rural girlhood reveal nation-making projects of modernity, imperialism, and settler-colonialism, seeking to understand how rural girls of color in the U.S. navigate these discourses in their place-making practices and spatial mobility trajectories. Through my research, I aspire to uplift, understand, and learn from rural women and girls of color all over the world. I draw from literature in Latinx sociology, critical girlhood studies, rural feminisms, and feminist geographies to trace social formations of rural girlhood along transnational, national, and regional contexts.