People

Hi! I'm Divya. I am a graduate student majoring in Computer Science at NYU and am broadly interested in formalizing how humans and machines process language and navigate decision making. I study how humans update their beliefs by combining prior knowledge with new information, and the conditions under which this breaks down. With the help of Reinforcement Learning and Bayesian modeling, I aim to understand how agents learn from their environments. Currently, I am studying learner modeling and diagnostic inference, particularly how observers reason about the underlying cognitive processes that produced a given behavior.

I’m a Master’s student in Computer Science at NYU Courant. My research focuses on reasoning in large language models, particularly on improving hypothesis exploration, with inspiration from cognitive science.

I'm a graduate student researcher at UBC interested in synthesizing a more complete understanding of agency and consciousness, both individually and collectively, by combining ideas from computer science, mathematical modelling, and philosophy. Currently, I am particularly interested in how social engineering and hive-mind thinking can influence agents to make irrational decisions, especially with increasing autonomous AI agents and their integration into society. Inspired by this, my current work involves quantifying facets of social intelligence in LLMs to better understand how vigilant they are to, and how effectively they can produce, persuasive social influence, and using these results to advocate for AI safety awareness.

Sunny Yu is an undergraduate student studying Symbolic Systems and Computer Science at Stanford University, working with the Stanford NLP Group.

Andy is a first-year PhD student in computer science at NYU, advised by Claudio Silva and Pavel Izmailov. Before this, he worked in industry as an engineer; before that, he graduated from Pomona College in philosophy and computer science.

Kai Xu is a second-year PhD student at NYU advised by He He. Through his research, he hopes to contribute to a future where AI can safely and reliably advance human flourishing, especially in scientific domains. His current interests are in LLM safety training, benchmarking language/multimodal models, and AI for science.

Tiancheng Yang is a PhD student in the Department of Statistics and Actuarial Science at the University of Waterloo, supervised by Prof. Matthias Schonlau. His research interests include natural language processing, AI agents, and survey research.

Valerie is a Machine Learning PhD student at CMU. Her work bridges machine learning, natural language processing, and human-computer interaction to advance the design of collaborative AI systems. Her research has fostered close collaborations with major engineering and financial companies, with findings cited by leading model providers and deployed in industry products. Valerie has been recognized with the Rising Stars in Data Science award, CMU Presidential Fellowship, and the NSF Graduate Research Fellowship. Her research has also received various awards, including Best Paper at a NeurIPS workshop and Oral Presentations at ICLR and AAAI.

Elizabeth is a PhD candidate at Princeton Computer Science advised by Tom Griffiths and Natalia Vélez. She studies multi-agent teams (human, AI, and both) by developing formal frameworks to characterize optimal collaborative strategies and trade-offs, and testing these models with multiplayer human experiments, LLM teams, and multi-agent reinforcement learning.

Raja is a PhD candidate at Princeton Psychology working in the Griffiths Computational Cognitive Science Lab. In September 2026, he will be joining the Kempner Institute at Harvard as a Research Fellow. Prior to Princeton, Raja completed an MSc in Physics, and a joint BSc in Physics and Electrical Engineering at the Technion. Raja's work is broadly focused on the study of representations in natural and artificial minds using tools from cognitive science, AI, and physics.

Ryan Liu is a 3rd year PhD student at Princeton University Computer Science advised by Tom Griffiths. His research focuses on understanding the similarities and differences between language models and humans through the lens of cognitive science, and using these insights to shed light on core problems in LLMs and AI agents. In the past, he has worked on improving conference peer review advised by Nihar Shah at Carnegie Mellon.

Hi! I'm a Master's student in Data Science at NYU focused on AI safety evaluation and applied LLM research. My current work centers on decoupling safety behaviors from general problem-solving in language models. I also study how model scale and prompting strategy affect reliability, particularly how smaller models degrade on complex safety risk. Looking ahead, I'm excited about extending this to agentic systems — understanding how autonomous AI agents handle adversarial dynamics, social influence, and failure modes in multi-agent settings.

Allison is a 4th year computer science PhD student working with Dr. Olga Russakovsky at Princeton University. Broadly, she is interested in the intersections of computer science, psychology, and cognitive science. She works to both better understanding and analyzing limitations of current AI systems as well as studying human-computer interaction with AI systems specifically. In addition to research, she is passionate about communication and education about science and technology, seeking ways to make black-box AI technology more accessible to everyone to understand!

I am a PhD student in the Computation and Decision-Making Lab at NYU. I explore decision-theoretic models of human choices during pedagogy and learning. For instance, how do we choose between many possible representations for a difficult problem? How can we communicate these representations to impart knowledge or work better together?

Chenyi (she/they) is a PhD student in Psychology at Cornell University. Chenyi Li studies creativity and intelligence, especially in the emerging human-AI society. She is interested in how patterns of creativity arise and evolve when humans and AI work together. To study this, she uses large-scale social network experiments and computational modeling to examine the underlying processes and mechanisms. She is also interested in designing new paradigms and systems for human-AI collaboration that can benefit groups and communities.

I am an assistant professor in Computer Science and Data Science at New York University. Before I was at UT Austin, Google AI, UW, and Cornell. I enjoy studying real world language usages with simple, efficient and generalizable models.

Andreea Bobu is an Assistant Professor at MIT in AeroAstro and CSAIL. She leads the Collaborative Learning and Autonomy Research Lab (CLEAR Lab), where they develop autonomous agents that learn to do tasks for, with, and around people. Her goal is to ensure that these agents' behavior is consistent with human expectations, whether they interact with expert designers or novice users. She obtained her Ph.D. in Electrical Engineering and Computer Science at UC Berkeley with Anca Dragan in 2023. Prior to her Ph.D. she earned her Bachelor's degree in Computer Science and Engineering from MIT in 2017. She was the recipient of the Apple AI/ML Ph.D. fellowship, is a Rising Star in EECS and an R:SS and HRI Pioneer, and has won best paper award at HRI 2020 and the Emerging Research Award at the International Symposium on the Mathematics of Neuroscience 2023. Before MIT, she was also a Research Scientist at the AI Institute and an intern at NVIDIA in the Robotics Lab.

I started my journey to cognitive science from an undergraduate degree in philosophy (Tsinghua, China). That led me to do the Master of Logic at ILCC at the University of Amsterdam. Following a few years working as a data scientist, I did my PhD in a fantastic lab at the University of Edinburgh, and then spent some wonderful time at Princeton University for my postdoc. I am now leading my lab in computational cognitive science at the University of Edinburgh.