Meet the NSF CSGrad4US Fellowship Recipients!

The Computing Research Association, in partnership with the National Science Foundation, is proud to announce the recipients of the NSF Computer and Information Science and Engineering Graduate Fellowship Program (NSF CSGrad4US). NSF CSGrad4US aims to increase the number of diverse, domestic graduate students pursuing research and innovation careers in computer and information science and engineering fields. Each year, a diverse cohort of NSF CSGrad4US fellows is selected based on their demonstrated interest and potential in pursuing a doctorate in a CISE field. There are currently 116 NSF CSGrad4US Fellows enrolled in 57 institutions across the United States. Below, our Fellows have provided some information about themselves, their interests, and their research goals.

Max Aalto, Massachusetts Institute of Technology

Electrical Engineering and Computer Science — Materials Design 

Max is developing computational methods for crystal and electronic structures prediction in conjunction with high-throughput devices for materials synthesis and characterization, with the objective of building the infrastructure for novel paradigms in the design and discovery of new material systems.

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Emir Accilien, Rutgers University 

Electrical and Computer Engineering — Wireless Communication and Spectrum Sharing

Emir Accilien is a computer scientist.  He worked for more than 10 years for the US Army within the Army Futures Command. He graduated from Montclair State University, NJ with a B.S. in Computer Science.   He later earned his M.S in Software Engineering from Monmouth University, NJ.  Prior to earning his bachelor’s degree, he served in the US Navy where he earned the Kosovo Campaign Medal – Awarded for supporting NATO’s peacekeeping mission in Yugoslavia and the Navy and Marine Corps Achievement Medal – Awarded for Meritorious service in a combat situation during operation Enduring Freedom.  He’s currently enrolled at Rutgers University, NJ where he’s pursuing his Ph.D. in Electrical and Computer Engineering.  In his spare time, he enjoys cycling, lifting weights, swimming, and most of all spending time with his family.

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Giri Anantharaman, Carnegie Mellon University

Computer Science — Robotics

I am first year PhD student at Robotics Institute, Carnegie Mellon University advised by Guanya Shi and Max Simchowitz. My research interests revolve around decision making in sequential, interactive and dynamic settings, grounded in a principled theoretical and algorithmic framework. I hope that such a framework can help reason about trade-offs and provide realistic guarantees, when applying learning techniques along side classical control ideas in robotics. Before starting PhD, I worked in the industry for about three decades as a software engineer across mobile, data and AI domains.

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Samantha Archer, Stanford University 

Electrical Engineering — Computer Architecture 

Samantha is currently a PhD student in the Electrical Engineering Department at Stanford University. She is broadly interested in computer architecture, computer systems, and formal verification. Before beginning her graduate education, she worked as an ASIC physical design engineer at NVIDIA. She graduated from Duke University in May 2020, with a BSE in Electrical & Computer Engineering and BA in Mathematics. In addition to academic research, she enjoys running, reading, cooking, teaching, and being outdoors. 

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David Atkinson, Northeastern University 

Computer Science — Interpreting Deep Learning Models

I am a first-year PhD student at Northeastern University, advised by David Bau and Byron Wallace. My research interests revolve around interpreting and controlling large language models. Before starting the PhD, I worked as a software engineer at an agricultural startup, and then as a researcher at Epoch, investigating when and how we should expect advanced AI capabilities to arrive. I majored in Math and Computer Science at the University of Colorado, Boulder, where I did NLP research advised by Chenhao Tan.

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Alexander Bandukwala, University of Michigan

Computer Science and Engineering — Programming Languages

I’m a researcher in the Future of Programming Lab at the University of Michigan, where my work focuses on programming languages, specifically Hazel, a live functional programming language. I’m interested in utilizing the intersection of Programming Language Theory and Human-Computer Interaction to make the power of computing better for everyone. My long-term vision is to extend live programming into creating malleable software by integrating programming language structures into end-user interfaces. Before my research career, I worked as a professional software engineer for over a decade.

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Oliver Bentham, University of Utah

Kahlert School of Computing — Natural Language Processing

I spend my time working on problems at the intersection of natural language processing and machine learning. I’m particularly interested in model architectures, their structural learning biases, and alternatives that impart the ideal biases for effectively modeling language. In my free time, I enjoy playing soccer with friends and hiking.

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Alexander Bock, Rice University

Department of Computer Science — Computational Biology 

Alex is a first-year Ph.D. student at Rice University in Dr. Lydia Kavraki’s group. With backgrounds in biology and natural language processing, Alex is interested in synthesizing protein sequence and structure data to infer protein function, biophysical properties, and evolutionary relationships. During his undergraduate study, he gained experience with the research process and with computational biology. After graduating from Tufts University in 2019 with a degree in computer science and biology, he spent four years as an engineer in the R&D sector working on problems including multimodal learning, topic modeling, and computational antibody design. His work with impactful advisors and driven researchers in a variety of fields inspired him to return to the academic world and pursue a career in formal research. Beyond thinking about proteins, Alex enjoys biking and playing blues keyboard.

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Sarah Boelter, University of Minnesota 

Computer Science and Engineering — AI and Robotics

Sarah graduated with her undergraduate degree in Computer Science from University of Alaska Anchorage.  She worked for four years in the aviation industry as a software engineer before beginning her doctorate in Computer Science at the University of Minnesota in Minneapolis.  Her research interests include the intersection of artificial intelligence and robotics in aviation and space, and is currently working on robotic autonomy in high-risk planetary environments.  Sarah is on the University of Minnesota Nordic Ski Team, enjoys spending time outdoors, and volunteering with local animal rescues. Sarah is supported in her studies by her spouse, Patrick and her cat, Squeak. 

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Eric Bond, University of Michigan

Computer Science and Engineering — Programming Languages (Type Theory)

I am a programming language researcher interested in mathematical models of language semantics. Before joining University of Michigan, I worked as an industry researcher in formal verification.

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Forrest Bucklew, Mississippi State University

Computer Science — Human Computer Interaction

Forrest has spent her life in beautiful middle Tennessee, and is proud to be giving back to her community by serving as a member of law enforcement there while pursuing her degree online. After completing her first undergraduate degree in Psychology from Vanderbilt University, Forrest discovered Computer Science as an outlet for her passion for problem solving. She then completed a Masters in Cybersecurity from Johns Hopkins University, before taking a break to focus on her new career in Digital Forensics and her family. She is now ready to dive back into academia once again, and is pursuing her PhD in Computer Science from Mississippi State University. When not busy with work or school, she spends her time with her two beautiful children and her incredibly supportive husband.

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Ilan Buzzetti, UT Dallas

Computer Science — Formal Methods

I am excited to be back at my alma mater to pursue a PhD. When I first graduated I decided to enter industry and made peace with the idea that I was done with academia for this lifetime, though I had enjoyed it. I’m grateful that the CSGrad4US fellowship has empowered me to return for a doctoral degree with peace of mind over my finances. I love computer science, but what I’m truly passionate about is kindness. My mission in this lifetime is practicing kindness to myself and kindness to others. This awareness was sorely missing in my life during my undergrad experience and I hope that the demonstrated benefits of the practice make it contagious to my stressed and burdened peers.

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Alyssa Cantu, Rice University

Computer Science — Computational Biology

Alyssa is a proud native of the Rio Grande Valley region of South Texas, a region located on the border of Texas and Mexico. She graduated from Brown University with an A.B. in Computational Biology in 2018. Prior to beginning her Ph.D. program, she worked as a Computational Biologist at the Texas Advanced Computing Center in Austin, Texas. She is currently a third-year Ph.D. student in Computer Science at Rice University. She is generously funded by the NSF CSGrad4US program as one of its inaugural fellows. Her research interests broadly encompass Computational Biology, and she is currently studying alternative splicing differences across Alzheimer’s Disease-associated cell types under the supervision of Dr. Vicky Yao.

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Miguel Angel Chapa, Arizona State University

Computer Engineering (Electrical Engineering) — Nano-scale electronics, sensors, and devices, computer engineering

My name is Miguel Angel Chapa, I am a 2022 CSGRAD4US Fellow currently pursuing my Ph.D. in Computer Engineering with a focus on Electrical Engineering at Arizona State University. I am looking forward on building and creating novel nano-scale electronics, sensors, and devices to enable the next revolution in the CISE discipline and further improve STEM and Medicine. 

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Peter Chang, MIT

EECS — Deep Learning, AI for Science

Peter G. Chang is a first-year Ph.D. student in EECS, advised by Prof. Sendhil Mullainathan. He received his A.B. in Physics and Mathematics and S.M. in Computer Science, both from Harvard University. He is primarily interested in augmenting scientists with tools to understand the world.

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Jiahui Chen, University of Texas Austin

Electrical and Computer Engineering — AI

Jiahui Chen is a first year PhD student at the University of Texas Austin, who is interested in the controllability of generative AI and uncertainty quantification in machine learning. Before her PhD she spent 3 years as a software engineer at Facebook/Meta working on various AI projects including: the text-to-image models that power AI-generated stickers in Messenger and WhatsApp, a PyTorch library for uncertainty estimation, and models that detect harmful content on Instagram. During her PhD, Jiahui hopes to work towards making generative models more controllable and is broadly interested in how to make AI more robust and responsible.

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Andy Chung, University of Michigan

CSE — Machine Learning

Andy Chung is a PhD student at the University of Michigan. His research focuses on leveraging large language models to build embodied agents. Previously, he worked as a software engineer at Amazon. Andy received his bachelor’s degree in Computer Science from Georgia Tech.

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Allee Clark, Howard University 

Electrical Engineering and Computer Science —  Affective Computing 

I am a Computer scientist by undergraduate education, and returned to Howard University to complete the graduate program. Currently researching large language models for decision making in an Affective Computing lab. I spent my gap years in academia as an engineer on software infrastructure teams driving production readiness for life critical systems. My career interests include cars, robots and software.

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Stephen Crawford, Brown University 

Computer Science — Robotics 

I work in the Brown ACT Lab where I study the coordination and cooperation of robot teams. My research focuses on supplementing model predictive control with machine learning and creating new representations of complex systems.

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Zach Cutler, University of Utah

Computer Science — Data Visualization

I’m a first year PhD student at the University of Utah School of Computing working with Alexander Lex. My current research is focused on making user studies easier to design, run, and analyze. I’ve previously worked mostly on provenance in visualization systems, and helped develop both the Trrack and Trrack-Vis libraries. Other research interests of mine include storytelling, and learning how to better communicate with non experts with visualization. Outside of work I play a lot of basketball, tennis, and chess.

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Tonya Davis, Purdue University

Computer Information Technology — Computer Science Education 

Tonya earned a BS in Computer Science Engineering and a Master in Business Administration from Washington University in St. Louis.  She started her career in industry as a Marketing Representative for IBM after undergrad and as a Senior Product Manager for Texas Instruments after graduate school before segueing into education.  She taught as an adjunct instructor at Wharton County Junior College and The University of Houston Downton before teaching secondary math and computer science.  She is passionate about broadening participation in computer science and STEM and helping to eliminate barriers to access and engagement for students traditionally left out of tech careers. Her research interests are in computer science education teaching and learning.  She is currently a mentor in the MENTORS in CS (Matching Experienced and Novice Teachers for Ongoing Rigorous Support in Computer Science) RPP with the Computer Science Teachers Association supported by the National Science Foundation.

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Melissa Dutz, Toyota Technological Institute at Chicago

Computer Science — Algorithms 

Melissa is a Computer Science PhD student advised by Avrim Blum at TTIC. She’s broadly interested in algorithms, including algorithmic game theory, online and approximation algorithms. She’s also excited about exploring learning theory and computational social choice. Prior to joining TTIC, Melissa worked at Meta (then Facebook) for several years as a software engineer on the Differentiable Programming team. Her undergraduate degree is in Computer Science from Pomona College. 

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Yara El-Tawil, University of Michigan

CSE — Speech, AI, Health, and HCI

Yara is currently a PhD student in the Department of Computer Science and Engineering at the University of Michigan. Her research interests center around AI for healthcare, including using speech and activity data to track mood and monitor mental health symptoms and human-centered design to ensure these systems benefit the patients who use them. Before beginning her graduate education, she worked as a software engineer at Microsoft. She graduated from the University of Michigan in 2020 with a dual BS in Computer Science and Biopsychology, Cognition, and Neuroscience. In her spare time, she enjoys reading, crochet, pottery, being outdoors, and traveling.

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Jordan Esiason, North Carolina State University

Computer Science — Machine Learning/AI

Jordan Esiason is a graduate student at North Carolina State University’s Department of Computer Science. He received his Bachelor’s degrees in Statistics and Music Composition at the University of Massachusetts Amherst in 2019. He worked in the computer science education research field for SageFox Consulting Group after graduation, becoming involved in a number of grant-funded projects that aimed to increase the accessibility and quality of computer science education in the United States. His research interests currently include applying natural language processing and artificial intelligence to computer science education, such as in the creation of intelligent tutoring agents.

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Ekaterina Fedorova, University of Pennsylvania

Computer and Information Science — Algorithmic Game Theory, Incentive-Aware ML, Strategic Classification

Ekaterina Fedorova is a PhD student at UPenn Computer Science in the intersection of CS theory, economics, and human-computer interaction. She is broadly interested in users’ interactions with algorithms, social recommendation systems as mechanism design, and strategic classification. Ekaterina is currently working on projects involving theoretically modeling strategic/non-myopic agents in recommender systems and altruistic agents in strategic classification settings. She is advised by Dr. Danaë Metaxa (UPenn) and works closely with Dr. Chara Podimata (MIT). Prior to being a CS PhD student, Ekaterina received a B.A. with majors in statistics and economics from UC Berkeley and worked as a researcher at UChicago.

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Evan Frenklak, University of Texas at Austin

Electrical and Computer Engineering — Computational/Medical Imaging

Evan is a first year PhD student and CSGrad4US fellow at UT Austin, working with Professor Jon Tamir in the Computational Sensing and Imaging Lab. Previously Evan worked in industry on radar R+D, including field projects with NASA to develop low-cost geophysical imaging platforms. His research interests include optimization models and signal reconstruction for accelerated MRI. A current project examines the impact of sampling patterns used in public MRI datasets on downstream analysis and algorithm training. Evan hopes to bridge ideas from applied math, computer science, and engineering to improve clinical outcomes and make medical imaging more accessible.

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Kate Glazko, University of Washington

Computer Science — HCI/Accessibility 

Kate is a first-year PhD student at UW advised by Dr. Jennifer Mankoff. Kate’s current research centers around the opportunities that generative AI could present for making areas such as design and fabrication more accessible. She is likewise interested in solutions to the ableist and inaccessible outputs that GAI models can sometimes produce. Kate’s research interests also include more approachable, engaging ways of communicating information such as health feedback. Kate additionally collaborates with Dr. Elizabeth Murnane (Dartmouth) Dr. James Landay (Stanford), Dr. Paula Moya (Stanford) on the WhoIsZuki and Perfecto projects which study novel methods of encouraging exercise in at-risk individuals. Previously, Kate did her MS and BS in CS at the University of Southern California where she worked on projects relating to human-robot interaction and neurodiverse learners. She additionally spent 6+ years in the software industry working in a range of roles including engineering and product management.

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Steven Golob, University of Washington Tacoma

School of Engineering and Technology — Privacy-preserving machine learning, fair AI, differential privacy

Steven is a lifelong student of mathematics and computer science. Having spent time at the Boeing Company as a software engineer, working on the flight simulator, he is now in the PhD program at the University of Washington Tacoma. There, he was the lead of a team that won 1st place in the SNAKE challenge on privacy attacks against synthetic data. He is also the recipient of the SET Outstanding Graduate Research Award (2023) and the Andrew and Julie Fry Innovation Award (2023) for his contributions to the solution with which his research team won 2nd place in the NSF-hosted U.S.-U.K. PETs Prize Challenge 2023. His research interest is in privacy-preserving machine learning (PPML) and fair AI, critical fields that address the widespread concerns of handling personal data securely and equitably. Steven sees research in PPML as a way to contribute meaningfully toward technologies that have huge impacts in people’s lives. At the same time, working in academia will allow him to fuel his passion for helping and mentoring his fellow computer science students.

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Alfredo Gomez, Carnegie Mellon University

Language Technologies Institute — Natural Language Processing 

Alfredo is a first-year PhD student in the Language Technologies Institute at Carnegie Mellon University. His research interest is in assessing the trustworthiness of Natural Language Processing systems. Previously, Alfredo worked as a software engineer at the California Institute of Technology where he aided in bringing software engineering and machine learning practices into the domain of nanotechnology. Alfredo holds a Bachelor of Science in Computer Science and Mathematics from Harvey Mudd College.

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Erica Goodwin, University of Chicago

Computer Science — Computer Science Education 

Erica is a first-year PhD student researching computer science education. Previously, Erica worked as a software engineer at Gusto and graduated with a joint Computer Science and Math degree from Harvey Mudd College.

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Tim Goodwin, UC Santa Cruz

Computer Science — Distributed Systems and Programming Languages

I am a 2nd year PhD student in the LSD lab at UC Santa Cruz. I am broadly interested in distributed systems and the abstractions we use to build them. Lately, I’ve been focusing on cloud-native programming models and the challenges they present to developers. Prior to grad school, I juggled my time between software engineering, music, and running in NYC.

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Daniel Grzenda, University of Chicago

Computer Science — Systems, Machine Learning

Daniel Grzenda is currently an NSF Fellow studying Computer Science at the University of Chicago. He is a member of Globus Labs advised by Ian Foster and Kyle Chard. His work is focused on building systems to make AI-driven discovery faster, cheaper, and more resource efficient. Prior to returning to pursue his PhD, Daniel worked at two biotechnology startups in the Bay Area, and led initiatives for non-profit data science at the University of San Francisco and the University of Chicago. He has recieved a Masters degree in Data Science from the University of San Francisco and undergraduate degrees in Biology and Computer Science from the University of Vermont.

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Michael Hajostek, University of North Dakota

Computer Science

A first year PhD student at UND, he had completed his undergrad from Minnesota State University Moorhead with a double B.S. in Computer Science and Information Technology. Spent a few years in industry working on a diverse set of projects for a variety of clients from startups to federal agencies. In his free time he helps coach and lead the MSUM Club hockey team, likes to garden and go for runs.

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Seungwook Han, MIT

EECS — Generative Modeling, Foundation Models, LLM Alignment

Seungwook is currently a PhD Student at MIT CSAIL advised by Prof. Pulkit Agrawal. He received his undergraduate degree from Columbia. Previously, he has worked on improving the training efficiency of generative models. More recently, he has been building foundation models for decision making and investigating how to align language models more efficiently for the personalization of LLMs.

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Demetrius Hernandez, University of Notre Dame

Computer Science and Engineering — Cyber-Physical Systems and Assured Autonomy

Demetrius Hernandez is a first-year Ph.D. student in the Computer Science and Engineering Department at the University of Notre Dame, advised by Dr. Jane Cleland-Huang. His research focuses on cyber-physical systems, particularly autonomous drones for emergency response scenarios. Prior to his doctoral studies, Demetrius worked as a Computer Scientist at White Sands Missile Range, where was awarded with the Commander’s Coin for Excellence and a Certificate of Special Congressional Recognition in recognition of his outstanding achievements and contributions. As a graduate of the University of Texas at El Paso, a Hispanic-Serving Institution, Demetrius is passionate about K-12 outreach and advancing STEM opportunities for underrepresented communities.

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David Hill, George Washington University 

School of Engineering and Applied Sciences — Federated Machine Learning

David is a highly skilled software engineer with nine years of experience spanning both the private and public sectors. He earned his B.S. in Computer Science from Howard University, where he conducted and published peer-reviewed research on a Framework for IoT Data Provenance. During his time at Howard, he also conducted and presented research on the cybersecurity of satellite middleware. David later obtained his Master of Engineering degree from the University of Virginia, contributing to cutting-edge research on Direct Carbon Capture technology. Currently, he is pursuing a Doctoral degree in Artificial Intelligence and Machine Learning, with research interests in federated learning and real-time machine learning applications. In his spare time, David enjoys walking his dog, Randy, and cultivating his garden of fruits and vegetables.

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Stella Holbrook, New York University Tandon School of Engineering

Computer Science — Big Data

Hi! I grew up in Seattle, Washington and got my bachelor’s degree in geography from the University of California, Santa Barbara. Before returning to school, I worked as a GIS analyst at a mapping company. I am beginning my research at NYU Tandon in its Visualization, Imaging and Data Analysis center. In my free time I enjoy reading books and going on runs.

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Justin Kay, MIT

EECS — Computer vision, machine learning, sustainability

Justin’s develops computer vision and machine learning methods for environmental science and conservation. Before starting his PhD, Justin co-founded Ai.Fish, a computer vision software company focused on applications in sustainable fisheries and ocean conservation. As CTO from 2019–2023 he led a range of computer vision projects in partnership with government organizations such as NOAA and DFO (Canada) and NGOs such as The Nature Conservancy, the Environmental Defense Fund, and the National Fish and Wildlife Foundation. His PhD research will focus on making computer vision and machine learning methods more reliable and informative for global-scale conservation science and decision-making.

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Eric Kapitanski, University of Southern California

Computer Science — Cybersecurity

Eric Kapitanski is a NSF CSGrad4US Fellow pursuing his PhD at the University of Southern California. His advisor is Dr. Jelena Mirkovic. Eric’s current research interests center around detecting and defending against cyberattacks at the network level.

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Kei Kebreau, City University of New York

Computer Science — Virtual Reality and Visualization

Kei is a first-year PhD student at the CUNY Graduate Center, advised by Prof. Wole Oyekoya. They earned B.S. degrees in Mathematics and Computer and Information Sciences from Cheyney University in 2021. Before their PhD, Kei worked as a software quality engineer at Mastercard. They also conducted research as an undergraduate research assistant at Brookhaven National Laboratory, using computation to minimize the chemical energy of cadmium selenide nanoclusters by manipulating their 3D structure. As an undergraduate research assistant at Cheyney, they evaluated emotion recognition toolkits and deployed machine learning models for use in a new Audiovisual Labeled Emotion Research Platform. Kei’s current research interests include 3D reconstruction, human-computer interaction, and emotion modeling. Outside of work, they enjoy gaming, music, and reading.

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Yulia Kumar, Rutgers University

Electrical and Computer Engineering — Distributed Machine Learning, Models Quantization

Yulia (Julie) Kumar has been a Lecturer at Kean University (Union, NJ) since 2019. She is a Kean University alumni with a BS in Computer Science and an MS in Computer Information Systems (with Distinction). Her courses include Client and Server-side Web Programming (HTML/CSS/JS/Frameworks), Software Development with Frameworks (C#,.NET), Python Programming (Python, its libraries and frameworks), Computer programming (Java), and Data Structures (Java). Previously, she taught Fundamentals of Computer Science and Intro to AI. Ms. Kumar began her teaching career at the college level in 2002 when she earned her doctorate and PhD in Finance in 2007 (outside of the US). Since receiving her degree, she has been back and forth between the industry and academia, ensuring her skills are sharp and current. Her current research interests include AI, in particular Machine Learning, Deep Learning, NLP, recommender systems, transformers, and LLMs; she is also interested in HCI and works on various topics related to CS education. Ms. Kumar began her journey toward PhD in Electrical and Computer Engineering @Rutgers in Fall 2024.

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Alexander Krentsel, UC Berkeley

EECS —  Networked Systems 

Alexander Krentsel is a PhD student at UC Berkeley in the NetSys lab, advised by Scott Shenker and Sylvia Ratnasamy. His research interests are in architecting internet control systems, and reasoning rigorously about their correctness and performance. His work focuses on control systems for wide-area networks, network verification, and on network-assisted control systems for autonomous vehicles. He received a B.S. in EECS and a B.A. in Music from UC Berkeley in 2019.

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Mora Labisi, Northwestern University

Computer Science & Learning Sciences — Computer Science Education, Software Engineering Education

Mora is a PhD Student in the Computer Science + Learning Sciences program advised by Nell O’Rourke. She is passionate about bridging the gap between computer science education and real-world software engineering. Prior to embarking on her Ph.D. journey, Mora was crafting code and systems as a Software Engineer at Starbucks. When not immersed in her work, Mora delights in caring for her hamsters, diving into language learning, singing at karaoke, and conducting monthly trivia nights for a small but mighty international group of friends!

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Leah Lackey, Cornell University

Electrical & Computer Engineering — Robotics

My research area is robotics, with a particular interest in motion planning and incorporating my past research experience with machine learning. I have always had a great interest in robotics due to its multidisciplinary nature and great potential to positively impact society. I also have a deeper, more personal draw to it from my experience undergoing a surgery with the da Vinci robotic surgical system. While I’m still deciding on a specific project to dive deeper into, I am heavily influenced by a desire to help people through robotics. My undergraduate Electrical & Computer Engineering degree was completed at Western Washington University in Bellingham, WA. I have also completed an internship at NASA and a research position at Yale University, which have helped hone my skills and interests.

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Vihan Lakshman, Massachusetts Institute of Technology

EECS — Machine Learning and Systems

I am a first-year computer science PhD student at MIT working at the intersection of machine learning, high-performance computing, and systems. Prior to MIT, I spent four years as a scientist at Amazon Search and another two years working on resource-efficient deep learning systems at an early-stage startup. And before that, I studied applied mathematics at Stanford University with a focus in theoretical computer science.

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Medina Lamkin, University of Washington

Paul G Allen School of Computer Science and Engineering — Data Visualization

Medina attended Portland State University where she earned her Bachelor of Science in Computer Science and a Minor in Mathematics. As an undergrad, she worked on a machine learning research project that sparked her interest in research. Following her graduation, she spent the next three years exploring different areas of research and learning about STEM education. Through this work, she collaborated with professionals from a wide variety of backgrounds, which highlighted the important role data visualization played in communicating information. This experience inspired her to pursue a PhD in computer science with a focus on data visualization. She is now a PhD student with the Paul G Allen School of Computer Science and Engineering at the University of Washington.

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Ashton Larkin, University of Washington

Computer Science — Robotics 

Ashton is a PhD student at UW advised by Siddhartha Srinivasa. His research interests are in dexterous and contact-rich robotic manipulation. Before his PhD, Ashton spent several years working as a software engineer in the robotics industry. He received his B.S. in Computer Science from Brigham Young University in 2019.

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Yucen Lily Li, NYU

Computer Science — Machine Learning 

Lily is a PhD student at NYU advised by Andrew Gordon Wilson. Her research broadly focuses on machine learning generalization and probabilistic deep learning, and she is also interested in applying uncertainty quantification to scientific domains. Previously, Lily was part of the Bayesian Modeling team at Meta, where she contributed to the development of probabilistic programming languages. She earned her undergraduate degree in Computer Science from Carnegie Mellon.

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Travis Lloyd, Cornell University

Information Science — Social Computing & Computational Social Science

Travis Lloyd is a public interest technologist interested in social technologies for a healthy democracy. Prior to joining Cornell he spent almost a decade working as a software engineer for organizations ranging from large public companies to small legal aid startups. He holds a combined A.B.-Sc.B degree in Computer Science and Music from Brown University.

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Cade Lueker, CU Boulder

Computer Science — Programming Languages

I am a Computer Science student from Brooklyn NYC. I Moved to Colorado in 2022 to work as a software engineer before feeling that I should go back into academia. I researched various programs and found the CUPLV lab at CU Boulder. The research aligned perfectly with my interests and the professors were extremely welcoming and friendly. My current advisor told me about CSGrad4US and I applied just in time. I have been loving PhD life although it is hard and look forward to the future!

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Nina Lutz, University of Washington

Human Centered Design Engineering Department — CSCW, HCI, Sociotechnical Systems, Computational Photography, Mis/Disinfo, Online Harm, Visual Media

Nina Lutz is a PhD student at the University of Washington where she tries to use computers to make us think about other people. She does this using mixed methods approaches to examine how visual media reifies and exacerbates online and offline harms to intersectionally marginalized communities. Lutz believes that no one can escape visual culture and that it has uniquely powerful ways for malicious actors to enact harm within sociotechnical systems, particularly social media platforms. Along the way, she strives to create tools for other researchers to better collect, investigate, and analyze visual media in their own research agendas.

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Ian Magnusson, University of Washington

CSE — Language Models 

Hi, I’m Ian! I do AI research at University of Washington and the Allen Institute for AI with Noah Smith and Pang Wei Koh. I’m interested in the science of language modeling, especially advancing evaluation to better understand scaling behavior and robustness across textual domains.

Previously, I was a PYI at AI2. I got my MS in computer science from Northeastern University, and interned at AWS AI Labs and SIFT. I also hold a BA in cultural anthropology from Bard College.

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Evanjelin Mahmoodi, University of California Los Angeles

Computer Science — Computational Biology

Hi! My name is Evanjelin, and I am a computer science PhD student at UCLA. My research interests include applying Bayesian statistics and causal modeling to interesting biology/genomic problems.

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Jessica Medina, Drexel University

Information Science — HCI, online social support, online communities, health and wellbeing 

Jessica graduated with her B.S. in Computer Science from University of Central Florida. She worked as a full stack developer in the health industry and as a software engineer in the aviation industry. She started her Ph.D. in Information Science at Drexel University under the advisement of Dr. Afsaneh Razi. Her research interests include: HCI, health and wellbeing, online social support and communities for people living with epilepsy. Jessica is a dog mom to a working service dog named Shay and a non working dog named Chewie.

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Antero Mejr, Tufts University

Computer Science — Formal Methods

Prior to joining Tufts, I was a Senior Software Engineer in the medical imaging industry, designing software for CT, MRI, and surgical fluoroscopy devices. My primary interest was developing methods for building safe, effective, and concurrent scientific software. I also enjoy contributing to open-source projects, particularly those that involve functional programming. These interests led to my current research area, developing verification techniques to ensure the correctness of complex, real-world software systems. This year, I joined the TuPL (Tufts Programming Languages) research group, where I will begin my research with my advisor Jeff Foster. I hope that my work may advance the cause of making formal methods more accessible and prevalent in the systems we depend on for everyday life.

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Daniel Meyer, Columbia University

Computer Science — Computational Biology

Dan is a 2nd year Computer Science PhD student at Columbia University. He is a member of David Knowles’ lab where he uses machine learning tools and techniques to study gene regulation. Prior to joining the Knowles lab, Dan worked for 5 years as a Computational Associate at the Broad Institute in the lab of Steve McCarroll, where he contributed to a variety of projects, prominently around “stem cell villages”. Dan earned his undergraduate degree in Computer Science from Tufts University in 2018.

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Andres Meza, UC San Diego

Computer Science and Engineering — Hardware Security

Andres Meza is a PhD student at the University of California San Diego (UCSD), La Jolla, CA 92093 USA. His research interests include hardware security, optimization of machine learning models for hardware deployment, and computer vision. Meza received a B.S. in both computer science and cognitive science with a machine learning and neural computation specialization from UCSD.

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Micah Morgan, University of Maryland

College of Information — HCI, Quantum Computation, and Health Information

Micah Morgan is a first year PhD student at the University of Maryland (UMD), where she is a CSGrad4US and MathQuantum Fellow. Her research considers whether quantum computation can improve the information landscape for clinicians and caregivers of children with rare disease. Prior to joining UMD, Morgan worked as the Head of Public Affairs for SeedAI. There, she worked alongside Congress to improve their understanding of artificial intelligence technologies toward more ethically sound policies.

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Sarah Mundy, Columbia University

Computer Science — Malware in Quantum Computing

Sarah Mundy is a first-year PhD student advised by Professor Salvatore Stolfo. Her research interests are in cybersecurity applied to quantum computing, specifically looking at potential malware attack vectors. Previously, Sarah has worked with NASA’s Office of the Chief Human Capital Officer in the workforce planning group, the Pentagon’s Office of the Undersecretary of Defense Research & Engineering under the Principal Director of AI, on DARPA’s Media Forensic program, and with various military and intelligence research groups focused in the AI and ML spaces. She graduated from the University of Nevada, Reno with her BS in electrical engineering in 2013. She has received the Echostar Spot Award for outstanding performance on a satellite networking project, NAVAIR’s Flight Test Excellence Award for her work planning Tomahawk missile software test flights, the UNR Outstanding Student Service Awards for both the College of Engineering and the Department of Electrical Engineering, 1st and 2nd place in the IEEE Region 6 paper and design competition, respectively, and is a Tau Beta Pi engineering honors society lifetime member. Her hobbies include running, lifting, hiking, reading science fiction and non-fiction, and taking care of her orchids and potted fruit tree.

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Matt Nicholson, University of Colorado Boulder

Information Science — Social Computing 

Matt Nicholson is a PhD student in the Department of Information Science at CU Boulder, working with Professor Bryan Semaan. His research examines how online communities coordinate and govern within themselves and between each other. Matt earned a BS in Computer Science, with a second major in Mathematical Methods in the Social Sciences, from Northwestern University and prior to CU, he spent several years working as a business analyst. In his free time, Matt enjoys watching baseball, the television show Survivor and hosting dinner parties where the meal centers around a bad pun.

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Juan Padilla, University of Puerto Rico

Computer Science and Engineering — Geolocation Management for AI and Autonomous Applications

Juan A. Padilla is a dedicated professional with a background in Computer Engineering from the University of Puerto Rico, Mayagüez Campus, and over 25 years of experience in technology and innovation. As Chief Strategy Officer and Lead Investigator, he has contributed to various impactful projects, including geolocation-enabled systems, disaster response applications, and addressing solutions for underserved communities. His work has been supported by grants from organizations such as the National Science Foundation and the Department of Defense.

Juan is passionate about using technology to solve real-world challenges and holds a U.S. patent for his Geolocation Name System, which offers a novel way to represent physical coordinates. Over his career, he has collaborated with diverse teams and organizations to develop technical solutions for industries ranging from pharmaceuticals to public services.

He is also a recipient of the NSF CSGrad4US fellowship, which supports his ongoing doctoral studies in Computer and Information Science and Engineering. Juan approaches his work with a focus on learning, collaboration, and practical impact, aiming to bridge innovation and community needs. He values teamwork and the opportunity to contribute to projects that improve lives and advance technology.

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Samyak Parajuli, The University of Texas at Austin

Electrical and Computer Engineering — AI

Samyak Parajuli is currently a PhD student at UT Austin advised by Amy Zhang. His research focuses on multimodal learning and self-supervised learning within decision-making contexts. Previously, he worked as a research engineer at Scale AI and Perplexity AI developing VLMs, LLMs, and RLHF approaches. He holds Bachelor’s and Master’s degrees from UC Berkeley.

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Jeff Pflueger, Northeastern University 

Computer Science — Robotics and AI 

Jeff Pflueger is a 1st year PhD student in the Khoury College of Computer Sciences at Northeastern University working on robotics. He is advised by Michael Everett. His research interests are in verifiable machine learning, planning and decision making, and cognitive robotics. Jeff earned a BS in Engineering from Olin College of Engineering in 2018. Before starting his PhD, he worked as a software engineer in the autonomous vehicles and consumer robotics industries. While working, he primarily focused on robotics systems, error-reporting and logging, and motion planning. In his spare time, he likes to bake, cook, and tackle all kinds of puzzles.

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Grace Quaintance, Cleveland State University

Electrical & Computer Engineering — Computer Engineering – Open-RAN/NextG and Satellite Communications, Controls

I’m a first year PhD student at Cleveland State University. Before returning to school, I worked for a Non-Profit hospital in the Buildings+Design department as a Facility Engineer. There, I coordinated building projects in tandem with a project team to engage architecture and/or engineering firms. Prior to that, I worked as a 911 dispatcher for 13 years. I have two undergrad degrees, a Bachelor of Electrical Engineering from Cleveland State and a Bachelor of Architecture from Florida A&M University. When I’m not learning more new things, I a busy wife and mom to two rambunctious kids.

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Gabriella “Nikki” Ramirez, Virginia Tech

Computer Science — Dark Patterns and User Security and Privacy in Virtual Reality

Nikki Ramirez is a second-year PhD student focusing her research on dark patterns as well as user security and privacy in virtual reality. She is advised by Dr. Brendan David-John. Previous projects included virtual monitors in augmented reality, subtle gaze guidance in a 360° video, and visceral notices for eye-tracking. Currently, she is exploring user privacy concerns and needs in regards to potential risks and dark patterns in extended reality (virtual reality, augmented reality, mixed reality). G. Nikki Ramirez graduated from University of Colorado Boulder in 2019 with a BA in Computer Science and Minors in History and English Creative Writing. She previously worked at University of Colorado Denver’s Physics Department with the Super Cryogenic Dark Matter Search (SCDMS) collaboration as a Staff Scientist, as well as at Micro Focus as a Software Engineer II. In her free time, Nikki writes science fiction/fantasy novels, plays video games, and runs table-top RPG campaigns.

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Harine Ravichandiran, University of Washington

Computer Science — Robotics

Harine is a PhD student at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where she is advised by Prof. Byron Boots. Her research lies at the intersection of robot learning and perception, aiming to develop collaborative robotic systems capable of performing complex tasks in unstructured environments. Before joining UW, Harine worked on autonomous vehicles at Overland AI and Waymo. She earned her Master’s and Bachelor’s degrees from Stanford University.

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Jamie Rosas-Smith, Johns Hopkins University 

Computer Science — NLP and Speech Processing

Jamie Rosas-Smith is a first-year PhD student in the Center for Language and Speech Processing (CLSP) at Johns Hopkins University, where he is advised by Prof. Anjalie Field. His research interests are primarily focused on natural language and speech processing for social applications. Prior to joining CLSP, Jamie completed his bachelor’s degree in computer science at Wellesley College, conducted research in the areas of prosody, acoustics, and algorithms for detecting speech irregularities at MIT’s Research Laboratory of Electronics, and worked at Chase as a software engineer. He is a proud recipient of both the CSGrad4US Fellowship and the Percy Pierre Doctoral Fellowship.

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Ryan Rubel, University of Southern California

Thomas Lord Department of Computer Science — Robotics, Machine Vision

Ryan Rubel is a Ph.D. student at the University of Southern California, where he researches perception algorithms for safety-critical robotics under Professors Lars Lindemann and Jyo Deshmukh. Ryan’s work is motivated by the development of autonomous robotic systems for space exploration, which he believes will be essential to studying many of the Solar System’s more distant bodies. Prior to joining USC, Ryan spent two years researching multimodal machine vision algorithms at Noblis. He holds a B.S. in Computer Science from Johns Hopkins University with minors in Robotics, Mathematics, and Applied Mathematics and Statistics. In his spare time, Ryan leads the pathfinding team of the USC Field Robotics Lab and enjoys playing video games.

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Daniel Sainati, University of Pennsylvania

Computer and Information Science — Programming Language

Daniel is a PhD student in the PLClub at the University of Pennsylvania, where he studies type systems and language design, with an eye towards leveraging types to guarantee program correctness. Before starting his PhD, Daniel worked as a software engineer at Meta, on the Flow type checker for JavaScript, and at the Flow Foundation, on the Cadence language. Daniel holds a Bachelor of Science and a Master of Engineering degree from Cornell University.

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Hrishee Shastri, University of Chicago

Computer Science – Data, Data Science, Data Management

I am currently a PhD student at the University of Chicago. I originally did my B.A in math-computer science at Reed College in Portland, Oregon, graduating in 2021. Following that, I spent 3 years working in the tech industry in Seattle, Washington. I am from Seattle, Washington. Outside of work, I like to play soccer, swim, watch squirrels frolic, and seek out cozy neighborhood bookstores, coffee shops, and grocery stores.

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Eden Shaveet, Cornell University

Information Science — Multimodal Information

Eden Shaveet is a Ph.D. student at Cornell University in the Department of Information Science advised by Tanzeem Choudhury (People-Aware Computing Lab). Her work centers multimodal information extraction and interpretation methods for heath inferencing. Eden holds an MS in Health Informatics and Analytics from Tufts University School of Medicine and completed post-baccalaureate training as a Bridge to Ph.D. Scholar at Columbia University in the Department of Computer Science.

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Filipp Shelobolin, Columbia University

Computer Science — Fairness & Explainability

Filipp Shelobolin is a PhD student at Columbia working with Professors Augustin Chaintreau and Jeanette Wing on the fairness and explainability of complex machine learning systems.

After receiving a BS in Statistics and Machine Learning from Carnegie Mellon in 2021, Filipp worked as a Research Scientist at Upstart for three years. Filipp enjoys writing, performing improv comedy, playing pool, and reading almost anything.

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Di Yang (Steven) Shi, The University of Texas at Austin

Computer Science — Robotics

Steven graduated from Brown University with a Sc.B. in Computer Science in 2018. Prior to beginning his Ph.D. program, he worked as a software engineer at SingleStore and Google. He is currently a third-year Ph.D. student in Computer Science at The University of Texas at Austin. He is generously funded by the NSF CSGrad4US program as one of its inaugural fellows and is currently researching high performance simulation for multi-agent reinforcement learning under the supervision of Dr. Peter Stone. Steven is proud to be a first-generation immigrant and college student. Outside of academia, Steven enjoys playing board games and soccer.

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Ashley Shin, University of  California, San Diego

Computer Science and Engineering — Machine Learning

Ashley is a first year PhD student at UC San Diego, advised by Julian McAuley. Her research interests include retrieval, recommender systems, NLP, and ML. Overall, she is interested in helping people find what they want. She is currently working on projects in generative sequential recommendation. Before UCSD, Ashley worked on biomedical text retrieval at the National Institutes of Health (NIH). She studied philosophy in undergrad. She likes to write prose, play guitar, and make music.

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Kaleen Shrestha, University of Southern California

Computer Science — Human-Robot/Computer Interaction

Kaleen is a second year Ph.D. student in the Computer Science department at the University of Southern California working at the Interaction Lab with Professor Maja Matarić. She is broadly interested in conflict resolution and is investigating how socially assistive robots can help children learn to resolve conflict. Prior to USC, she worked as a Research Analyst on projects related to automatic speech recognition for low-resource languages and analyzing dehumanization in social media at the University of California, Santa Cruz in the Natural Language Processing department. She received a B.S. in Computer Science and an M.S. in Natural Language Processing from the University of California, Santa Cruz.

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Shani Spivak, Northeastern University

Khoury College of Computer Science — AI ethics and governance, Vis x ML

Shani Spivak is a doctoral student in the Khoury College of Computer Sciences at Northeastern University, and is a Khoury Distinguished Fellow and NSF CSGrad4US Fellow. Prior to the PhD program, she worked as a Director for Emerging Technology and International Collaboration at the National Security Council (NSC) focusing on the development and coordination of national artificial intelligence, digital assets, and digital identity policy. Before the NSC, she served as the FBI’s Senior National Intelligence Officer for Technology, and as an adjunct professor at the National Intelligence University. While working at the FBI, Shani founded a Women in Tech advocacy group and helped drive AI ethics and governance work. She has a B.S. in electrical engineering from Boston University, an MBA from NYU, and is US Navy veteran, having served in the Seabees as part of the Navy Civil Engineer Corps.

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Shalini Sushri, Georgia Tech 

Computer Science — AI/ML

I am a CS PhD student at Georgia Tech and a CSGrad4US Fellow. My research focuses on advancing the field of artificial intelligence by developing AI systems that can reason, and explain their decisions.

Specifically, I explore two key areas:

Reasoning and Problem-Solving: How can we enhance AI systems’ capacity to reason logically, identify relevant information, and generate effective solutions?
Explainable AI: How can we enable AI systems to provide clear and concise explanations for their decisions, thereby increasing user trust and accountability?
Through my research, I aim to develop AI systems that are not only intelligent but also transparent and trustworthy. By addressing these fundamental challenges, we can unlock the full potential of AI and create a future where AI benefits society as a whole.

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Arghamitra Talukder, Columbia University

Computer Science — Computational Biology

I am currently enrolled in the Computer Science PhD program at Columbia University, specializing in the field of single-cell cancer genomics. My primary area of concentration centers on the development of probabilistic computational techniques, utilizing machine learning, to investigate and analyze various biological phenomena such as diseases and biomolecular interactions. My academic journey has been quite diverse, encompassing backgrounds in Electrical Engineering, Computer Science, and Biochemistry. I also spent two years working in the semiconductor industry. In my leisure time, I enjoy watching films and engaging in the pursuit of learning new languages.

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Ian Michael Terry, UC Santa Cruz

Computer Science and Engineering — Computational Ecology

I was born in Miami, FL and studied computer science at Florida State University. When I was graduating, I had aspirations of continuing my education, but I also wanted to try working a job with my newfound degree. After 5 years of working as a software engineer, the possibility of a return to academia felt entirely out of reach, until I one day received an email from the NSF. Since then, CSGrad4US has helped me navigate the path back to academia; I am now a new PhD student pursuing interdisciplinary research in computer science! It’s a new world for me, and I am very excited for what’s to come. 

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Patrick Jose Toral, University of Southern California

Viterbi School of Engineering — Machine Learning for Healthcare 

As a Computer Science PhD student at the University of Southern California since September 2022, I specialize in machine learning applications in Neuroimaging and secure federated learning. My work includes adapting advanced convolutional neural networks for 3D Brain MRI analysis for applications to Alzheimer’s and Autism research. My academic and professional journey is driven by a passion for impactful research in healthcare technology and a deep interest in the societal implications of AI. As a NSF CSGrad4US PhD Fellow and active member of the academic community, I am committed to advancing both my technical skills and understanding of AI’s broader impact. 

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David M Torres-Mendoza, UC: Santa Cruz

Computer Science & Engineering — Data Science 

David Manuel Torres-Mendoza is a committed computer scientist and data scientist, currently pursuing his Ph.D. in Computer Science and Engineering at the University of California, Santa Cruz. Passionate about enhancing equity and accessibility in computing and data science, he strives to make data communication clear, engaging, and fun. During his undergraduate studies at UCSC, David was deeply involved in an infant development lab, refining his skills in experimental design and data analysis. This experience shaped his professional path and led to his role at a non-profit focused on computer science education research. Here, he excelled in data analysis and management, developing a passion for broadening computing participation. His work significantly contributed to making computing more inclusive. David’s technical proficiency includes tools like Excel, SPSS, Dedoose, Qualtrics, SurveyGizmo, and programming languages such as Python and R. His expertise enables effective management of complex data science concepts across diverse contexts. David stands out as a skilled technical practitioner and a creative force for educational engagement and transformative change in data science.

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Bernardo Trindade, George Washington University

Electrical and Computer Engineering — Cybersecurity

Bernardo is currently enrolled as a PhD student at the George Washington University as a member of the GraphLab. Bernardo received his undergraduate degree in Computer Science with an additional focus on Mechanical Engineering from the Colorado School of Mines in 2016. His research focus is on applications of graph theory and graph neural networks to investigating cybersecurity incidents at the enterprise level.

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Bryan Wallace, Georgia Institute of Technology

Human Centered Computing — CS and AI Education Innovation and Equity , Broadening Participation In Computing

Bryan Wallace, a proud graduate of Morehouse College and current Ph.D. student at Georgia Tech, is passionate about advancing education through the intersection of technology, creativity, and accessibility. With a background in software engineering and over a decade of experience in K-12 education, he has dedicated his career to making computer science more inclusive and impactful.

In his professional journey, he has developed culturally relevant computer science programs, designed immersive curricula, and led transformative initiatives that expand access to technology education. His Ph.D. research focuses on Computing in Education, Human-Centered Computing, and creating tools that make CS accessible to underserved communities.

Beyond his professional pursuits, Bryan’s energy and curiosity extend to other passions. An avid cook, music enthusiast, and trivia aficionado, he balances his work with training for his first triathlon. Whether designing inclusive STEM initiatives or exploring new ways to solve complex problems, Bryan is on a mission to empower the next generation of innovators and ensure that computer science education is a tool for equity and opportunity for all.

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Kevin A. Wang, Brown University

Computer Science — Computational Game Theory (AI / ML / Game Theory)

I research computational game theory: the intersection of computer science and game theory. Specifically, I am interested in building agents that can play imperfect-information 2-player zero-sum games such as poker and beyond. In theory, this means developing agents that converge to a Nash equilibrium. In practice, this means developing agents that beat the best humans. In particular, I focus on building agents that use “lookahead search”. I also have a broader interest in the general topic of building agents that use “system 2 thinking” / model-based planning (of which lookahead search in games is one example). I grew up in California and studied computer science at UC Irvine. After graduating in 2018, I software engineered at a startup. At the end of 2020, I quit my job and pursued research as a volunteer research assistant with Stephen McAleer and Roy Fox. In 2022-23, I was an AI Resident with Noam Brown and Gabriele Farina at Meta AI (FAIR). I am now a PhD student at Brown University advised by Amy Greenwald. In my spare time I enjoy being in NYC, watching the LA Lakers, and losing at Dota 2.

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Matthew West, University of Chicago

Computer Science — Systems 

Matthew West is from South Jersey, right outside of Philadelphia. He graduated from Lehigh University with a bachelor’s in computer science and biology in 2020 and then a master’s in computer science in 2021. He worked for AVEVA (formerly OSISoft) as a software engineer for 2 years after graduating. He currently lives in Chicago where he is pursuing a PhD in computer science at the University of Chicago. He is interested in improving computer systems for data science and machine learning. In his free time, he enjoys playing videos games, spending time with friends, and reading the occasional book.

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Anthony Wise, University of Washington

Computer Science and Engineering — Security and Privacy/Visualization

Anthony attended Virginia Tech and earned his Bachleor of Science in Computational Modeling and Data Analytics with a minor in Applied Mathematics and Physics. Immediately after undergrad, Anthony went on to do research and development work for the Department of Defense in the Washington D.C area. Having seen how research projects are ran, Anthony wanted to influence policy making in the DoD sector and he also aspires to become a professor. Being proactive, he applied for the NSF CsGrad4Us and is now a 1st year graduate student in Computer Science and Engineering at the University of Washington. His research area is in Security and Privacy and Visualization.

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Claire Yang, University of Washington

Computer Science and Engineering — Human-Robot Interaction, Computational Cognitive Science

I am a PhD student at the University of Washington’s Paul G. Allen School of Computer Science & Engineering. I am interested in human-robot interaction and computational cognitive science–I believe that a greater understanding of people can lead to better technology design, especially in assistive robotics. I graduated from University of Minnesota in 2021 and worked as a backend software engineer at HubSpot for a few years before starting graduate school. Outside of work, I enjoy yoga, rock climbing, and learning new things via different side projects.

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Jina Yoon, University of Washington

Computer Science & Engineering — Human-Computer Interaction

Jina Yoon is a PhD student of Computer Science & Engineering at the University of Washington, advised by Professor Amy X. Zhang. As a social computing researcher, her work explores the tension between user agency and platform design in online community moderation, especially for youth and neurodivergent populations. She advocates for online safety approaches that empower teens and augment their social strengths. Her research also examines how social media platforms can both help and harm the neurodiversity movement, especially for intersectional identities. Before starting her doctoral degree, she worked at Riot Games on the League of Legends Global Player Community team, as well as at Microsoft on device management and security. She earned her BAs in Computer Science and Modern Culture & Media from Brown University.

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Lirong Yuan, CMU

S3D — Software Engineering, Programming Languages, Systems, and Security

I am co-advised by Prof. Rohan Padhye and Prof. Christian Kästner. I plan to conduct research on topics including static and dynamic analysis of software dependency graphs, software bill of materials (SBOMs), sandboxing, and related areas.