PhD Fellowships 2022

Denizalp Göktaş is a PhD candidate in the computer science department at Brown University working with Amy Greenwald.

Denizalp’s research lies at the intersection of computer science theory, optimization, and microeconomics.  He builds and analyzes decentralized multi-agent learning algorithms in games and markets with the ultimate goal of building decentralized welfare improving technology based on these algorithms.

Denizalp holds a BA from Columbia University in Computer Science and Statistics and a BA from the Paris Institute of Political Studies (Sciences Po) in Political Science and Economics.

Saurabh Garg is a third-year Ph.D. student in the Machine Learning Department at Carnegie Mellon University, advised by Zachary Lipton and Sivaraman Balakrishnan. 

Saurabh is interested in building robust and deployable machine learning systems. The primary focus of his research is to improve and evaluate deep learning models in the face of distribution shifts. Machine learning algorithms are typically developed and evaluated under simplistic assumptions that are often violated in practice. Saurabh is interested in understanding the behavior of machine learning models in real-world scenarios and building provable methods to make progress towards relaxing simplifying assumptions in order to make robust and trustworthy models. 

Before Saurabh started his Ph.D., he received his bachelor’s degree from the Indian Institute of Technology (IIT) Bombay, majoring in Computer Science and Engineering.

Yanyi Liu is a PhD student in the Computer Science Department at Cornell Tech, advised by Rafael Pass and Elaine Shi (now at CMU).

Yanyi's research aims at developing best-possible secure constructions of cryptographic primitives that protect the authenticity and confidentiality of communication on the Internet. He is currently focusing on the possibility of basing one-way functions on worst-case hardness assumptions through the notion of Kolmogorov complexity. He is also interested in secure computation protocols for real-life applications.

During his PhD, Yanyi's work received the best paper award at CRYPTO'21. Previously, He obtained his bachelor's degree in Computer Science from Tsinghua University.

Pranav is a PhD student in Machine Learning at Georgia Institute of Technology advised by Rampi Ramprasad and Chao Zhang.

Pranav works on developing natural language processing methods to extract structured information and insights from scientific literature, which has challenges unique to it. Data locked away in scientific text is multi-modal just like in financial documents, i.e., split across text, tables & figures and moreover, entity relationships can span long distances in text. Pranav’s research tackles these challenges, the goal being to build systems that can automatically extract data from scientific literature and train downstream machine learning models with it.

Pranav received a bachelor’s and master’s degree from the Indian Institute of Technology, Bombay. As an undergraduate, he was an active quizzer and won several national competitions. He is an avid traveler and has visited 17 countries to date.

Yuhang Song is a fourth-year DPhil (Ph.D.) student in Computer Science & Nuffield Clinical Neurosciences at the University of Oxford, working with Prof. Thomas Lukasiewicz and Prof. Rafal Bogacz at Intelligent Systems group and Models of Brain Decision Networks group.

Yuhang’s research focuses on deciphering and extracting the learning principles of biological neural systems, so as to reverse-engineer them as algorithms or even specialized hardware. Such a route of research would, on the one hand, bring us one step closer to true artificial intelligence that facilitates our daily life, and, on the other hand, improve our understanding of the most sophisticated part of our body, the brain, so that diseases related to learning, and broadly, to neural systems, can be better understood and treated.

Yuhang received his Bachelor of Science degree in Electronic Information Engineering from Beihang University. 

Zexuan Zhong is a PhD student in Computer Science at Princeton University, working with Prof. Danqi Chen. 

Zexuan is interested in natural language processing and machine learning. His long-term research goal is to build efficient and robust systems that can understand human language and interact with humans in natural language. His current research focuses on extracting factual knowledge from unstructured text and pre-trained language, and leveraging the extracted knowledge to solve real-world tasks.

Zexuan obtained his bachelor’s degree from Peking University and his master’s degree from University of Illinois at Urbana-Champaign.

Kuno Kim is a PhD student in the Department of Computer Science at Stanford University,  working with Stefano Ermon. 

Kuno is interested in developing robust and scalable algorithms for Imitation and Inverse Reinforcement Learning. Currently, he focuses on Inverse Reinforcement Learning in problem scenarios where generic reward priors can be used to improve data efficiency. 

Kuno obtained his bachelor's degree from Caltech, majoring in Computer Science, where he applied machine learning tools to applications in healthcare including signal processing for biomedical sensors and optimized physical therapy for patients with spinal cord injuries.

Sewon Min is a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Prof. Luke Zettlemoyer and Prof. Hannaneh Hajishirzi. Her research is in the area of natural language processing and machine learning. Her work specifically focuses on question answering, natural language understanding, knowledge representation and few-shot learning. She is a co-organizer of The 7th Workshop on Representation Learning (ACL 2022), The Workshop on Semiparametric Methods in NLP (ACL 2022), The 3rd Workshop on Machine Reading for Question Answering (EMNLP 2021), Competition on Efficient Open-domain Question Answering (NeurIPS 2020), and more. Prior to UW, she obtained a B.S. degree in Computer Science & Engineering from Seoul National University.

Orr is a Computer Science PhD student at UC Berkeley, co-advised by Shafi Goldwasser and Avishay Tal.

He is researching vulnerabilities in deep reinforcement learning and how to defend against them. His background is in computational complexity and proof systems.

Orr completed his MSc at the Weizmann Institute of Science, and BSc at the Hebrew University of Jerusalem. He enjoys playing bass and climbing.

Jay Wang is a machine learning PhD student at Georgia Tech, working with Professor Polo Chau.

Jay’s research focuses on making AI more accessible, interpretable, and
accountable, by designing and developing novel interactive interfaces for
people to easily and enjoyably interact with machine learning systems at scale.
Jay’s hybrid expertise in machine learning and human-computer interaction
enables him to harness AI’s potential to benefit everyone. His work has
resulted in open-source tools like CNN Explainer (viral with 100k+ visitors)
that transforms AI education and GAM Changer (best paper award at
Research2Clinics workshop at NeurIPS’21) that empowers users to edit AI models
to reflect their knowledge and values.

Jay received his BS in Computer Science, Statistics, and Mathematics from University of Wisconsin–Madison in 2019.