PhD Fellowships 2021

Paul Gölz is a PhD student in the Computer Science Department at CMU and is advised by Ariel Procaccia (now at Harvard).

Paul’s research applies tools from AI, algorithms, and game theory to help society make better decisions. A specific interest of his are emerging forms of democratic participation and how these processes can be supported by axiomatic and algorithmic analysis. Recently, he has been working on sampling algorithms for citizens' assemblies, which combine demographic representation with probabilistic fairness guarantees to individuals.

Paul earned his Bachelor’s degree at Saarland University and was previously supported by the German Academic Scholarship Foundation.

Charvi Rastogi is a third year Ph.D. student in the Machine Learning Department at Carnegie Mellon University, advised by Nihar Shah and Sivaraman Balakrishnan. 

Charvi’s research centers on the interactions between AI and society. One of her focuses is designing tools for helping human decision-makers use AI-generated predictions. She uses insights from social psychology and cognitive science to devise strategies for optimal human-AI collaboration. She is also developing methods to crowdsource opinions on algorithmic behaviour to detect and flag biases in tasks such as web searches and image tagging. She hopes to create a self-sustaining feedback mechanism to improve AI performance for all.  

Charvi did her undergraduate studies at IIT Bombay, where she received a B.Tech+M.Tech in Electrical Engineering. She is passionate about questioning and shaping the role of algorithms in our society.

Bijeeta Pal is currently pursuing a PhD at Cornell University, advised by Professor Thomas Ristenpart.

Bijeeta's research focuses on understanding the interplay of security, privacy, and deep learning, exploring the new classes of attacks emerging in this domain along with developing defenses to counter them. Her work uses a combination of cryptography and empirical analysis as tools to build systems resilient against these attacks.

Bijeeta received a bachelor's and master's degree in Computer Science from the Indian Institute of Technology (BHU) and Purdue University respectively. She is also professionally trained in Kathak, an Indian classical dance.

Haekyu Park is a Computer Science PhD student at Georgia Institute of Technology, working with Prof. Polo Chau.

Haekyu’s thesis research designs and develops interactive scalable interfaces for trustworthy and easy-to-use AI. Her human-centered approach creates novel tools that broaden the access of AI technologies, helping people more easily interpret complex models, gain trust in well-performing ones, and fix those that malfunction. Haekyu’s cross-disciplinary research straddles visualization, machine learning, and scalable graph analytics. Her work has resulted in a first-of-its-kind GPU-accelerated data science curriculum, pioneering open-sourced tools like CNN Explainer that transforms AI education, and the ARGO Lite graph visualization used by thousands of students.

Haekyu received her Bachelor's degree in Computer Science and Engineering from Seoul National University. During her PhD, she interned at NVIDIA.

Neema Kotonya is a third-year PhD student in the Computing Department at Imperial College London, where she is advised by Prof. Francesca Toni.

Neema's research interests lie at the intersection of explainable AI and natural language processing. More specifically, her research focuses on extending the pipeline for automated fact verification. Her research looks at extracting explanations for veracity predictions and evaluating the usefulness of these explanations. 

Neema received an MEng in Computer Science from University College London. She is committed to STEM outreach, and serves as a governor at a London primary school. Neema also sits on the committee for the United Kingdom Linguistics Olympiad. In her free time, she enjoys reading and exploring the outdoors. 

Francesca Mosca is a final year PhD candidate in Artificial Intelligence at the Department of Informatics, King’s College London, where she is working under the supervision of Dr. Jose M. Such and Prof. Peter McBurney.

Her research interests lie at the intersection of Agent-based Modelling, Explainable AI, Human-Computer Interaction, Computational Social Choice, and Privacy. As an AI researcher, Francesca aims to develop ethical and sustainable AI applications which can assist humans in everyday challenges.

Francesca holds a MSc in Mathematical Engineering from Politecnico di Torino, Italy, and a MSc in Intelligent Systems from King’s College London.

Alexander Amini is a PhD student at the Massachusetts Institute of Technology, working with Daniela Rus.

Alexander's research focuses on building reliable machine learning algorithms for end-to-end control of autonomous systems and formulating robustness measures for these methods. His work has spanned learning control for autonomous robots, formulating confidence of deep neural networks, mathematical modeling of human mobility, as well as building complex inertial refinement systems.

Alexander completed his BSc in Electrical Engineering and Computer Science from MIT in 2017.

Dat Huynh is a Ph.D. candidate in the Khoury College of Computer Sciences at Northeastern University, advised by Prof. Ehsan Elhamifar. He received his Bachelor's degree from University of Sciences (Viet Nam), where he studied the Advanced Program in Computer Science.

Dat's research interests lie in significantly reducing the amount of annotations needed to train deep learning systems for visual recognition, detection and segmentation tasks. Specifically, he designs methods that decompose complex concepts into primitive components that can be combined to enable learning with few or zero training samples, with missing annotations and with weak supervision.

Chenshuo Sun is a Ph.D. candidate in Information Systems at New York University Leonard N. Stern School of Business where he is advised by Professor Anindya Ghose (Heinz Riehl Chair Professor of Business).

As a computational social scientist, Chenshuo has been conducting research combining machine learning and economic methodologies to solve important real-world problems. His current research interest is in the area of digital economy, including consumer journey analytics, AI chatbot, omnichannel marketing, privacy concerns around digital surveillance, and recently, economics of 5G technologies.

During his Ph.D., Chenshuo has received research awards and grants from Marketing Science Institutes twice and many other scholarships. Previously, he received his bachelor’s degree (Hons) from Sichuan University, China and his master's degree from Tsinghua University, China. Chenshuo enjoys being a competitive medalist swimmer with a butterfly focus since high school.

Neha Gupta is a PhD student in the Computer Science Department at Stanford University, co-advised by Professors Moses Charikar and Gregory Valiant.

Neha is broadly interested in problems at the intersection of machine learning and theoretical computer science. Currently, she is focusing on designing algorithms for learning problems which are robust to natural distribution shifts arising in practice and understanding their theoretical guarantees and limitations. 

Neha obtained a bachelor’s degree in Computer Science from Indian Institute of Technology (IIT) Delhi and a master’s degree in Computer Science from Stanford University.

Jiaxuan You is a fourth-year PhD student in Computer Science at Stanford University, working with Prof. Jure Leskovec at Stanford AI Lab.

Jiaxuan’s research focuses on empowering deep learning with graphs, where he believes that the ability to understand and reason with relational data is central for the next generation of AI. Specifically, Jiaxuan's research advances the direction of: 1) learning from graph structures, 2) generating and optimizing graph structures, 3) graph structures as priors for deep learning, and 4) large-scale applications of graph learning techniques.

Jiaxuan received his Bachelor of Engineering degree in Automation and Bachelor of Science degree in Economics from Tsinghua University. He enjoys photography and flying drones in his leisure time.

Yuqian Jiang is a PhD student in the Computer Science department at the University of Texas at Austin, where she is advised by Prof. Peter Stone.

Yuqian's research interests are in the intersection of planning, learning, and robotics. She aims to close the gap between specialized robot skills that work in constrained conditions, and integrated robot systems that act robustly in real environments. Her current work focuses on combining classical AI planning and machine learning methods for creating general-purpose service robots. 

Yuqian received her bachelor's degree in Computer Science and Mathematics from UT Austin. In her free time, she enjoys playing the piano and running.

Zeyu Zheng is a PhD candidate at the University of Michigan working with Prof. Satinder Singh.

Zeyu is interested in reinforcement learning. His research has been focused on learning various forms of knowledge from the environment such as intrinsic rewards, temporal credit assignment mechanisms, and state representations. His long-term research goal is to build adaptive agents that learn effectively and efficiently from their experience in real-world environments.

Zeyu received his BS in Computer Science from Peking University in 2017. During his undergraduate study, Zeyu worked on parallel and distributed computing. His work won the best paper award at ACM SIGMOD in 2017.

Angelos Filos is a PhD student in the Department of Computer Science at the University of Oxford, working with Yarin Gal. 

Angelos is interested in the mathematical foundations and applications of reinforcement learning for real-world problems. Currently, he focuses on enabling robots to adapt autonomously and efficiently to non-stationary environments. 

Angelos obtained his bachelor's and master's degree from Imperial College London, majoring in Electrical and Electronic Engineering, where he researched "Reinforcement Learning for Portfolio Management".

Kuo-Hao Zeng is a Ph.D. student in the Department of Computer Science & Engineering at the University of Washington, working with Prof. Ali Farhadi and Prof. Roozbeh Mottaghi.

Kuo-Hao’s primary research interests include Computer Vision and Machine Learning, especially in applications to Robotics. More specifically, Kuo-Hao’s current research direction is utilizing Visual Forecasting/Reasoning for planning and policy learning. He is also interested in equipping embodied agents with visual understanding capacity by Learning through Interaction. 

Kuo-Hao received his BS from National Sun Yat-sen University in Mechanical and Electromechanical Engineering, and MS from National Tsing Hua University in Electrical Engineering.