People

Principal investigator

Brenden Lake. Brenden is an Associate Professor of Computer Science and Psychology at Princeton University. He received his M.S. and B.S. in Symbolic Systems from Stanford University in 2009, and his Ph.D. in Cognitive Science from MIT in 2014. He was a postdoctoral Data Science Fellow at NYU from 2014-2017, and then an Assistant/Associate Professor at NYU from 2017-2025. Brenden’s research use advances in machine intelligence to better understand human intelligence, and use insights from human intelligence to develop more fruitful kinds of machine intelligence. [Website]

Postdoctoral researchers

Changho Shin. Changho is a postdoctoral research associate in the Department of Computer Science at Princeton. He is interested in data-centric approaches to learning, particularly how learning systems can exceed imperfect supervision by exploiting structure in data. His research focuses on understanding human inductive biases and using them to develop more capable learning systems through data-centric approaches. Prior to joining the lab, Changho obtained his Ph.D. in Computer Sciences from the University of Wisconsin–Madison, where he studied learning from imperfect and weak signals. He completed his B.A. in Psychology and B.S. in Computer Science at Seoul National University. [Website]

Manasi Malik. Manasi is a postdoctoral research associate in the Department of Computer Science at Princeton. She is interested in understanding the computational principles that give rise to human cognition, including what kinds of minimum structure and inductive biases are needed and also how these computations emerge and change over development. Prior to joining the lab, she obtained her Ph.D. in Cognitive Science from Johns Hopkins University, where she developed computational models to understand how humans extract social information from visual scenes. She completed her B.Tech at IIIT Delhi and worked on multisensory perception for her bachelor’s thesis at the National Brain Research Center, India. [Website]

Simon Schug. Simon is a postdoctoral research associate in the Department of Computer Science at Princeton. He is interested in understanding the principles that underlie intelligence and how large-scale neural networks can serve as a substrate for it. Prior to joining the lab, Simon obtained a Ph.D. in Computer Science from ETH Zurich in 2025, where he researched meta-learning and compositional generalization. During his doctorate he spent time as a researcher at Google DeepMind. He completed his Master’s thesis at the University of Cambridge and received an M.Sc. in Neural Systems & Computation from ETH Zurich and UZH in 2020, after having simultaneously pursued and obtained B.Sc. degrees in both Electrical Engineering and Psychology from RWTH Aachen in 2017. [Website]

Lab affiliates

Guangyuan Jiang. Guangyuan is a PhD student at the MIT Brain and Cognitive Sciences. He is interested in languages in and about the mind —– the dynamic interplay between language, culture, and thought. Guangyuan seeks to build models of human language to uncover the core cognitive and computational principles that shape language, and to develop human-like machines to reverse-engineer the mind through language. [Website]


Akshay K. Jagadish Akshay is a postdoctoral research fellow at the Princeton AI Lab. His research takes two complementary approaches to study natural and artificial minds: 1) Building scalable, sub-symbolic models of human (and machine) cognitive function using frameworks such as meta-learning, ecological adaptation, and resource-rationality. 2) Developing AI-driven methods to accelerate the discovery of interpretable symbolic programs of human (and machine) behavior and their internal representations. [Website]

Gabriel Sarch. Gabriel is a Postdoctoral Research Fellow in Princeton Language and Intelligence. His research develops methods for training multimodal AI models to reason, explore, and make decisions in complex environments, with an emphasis on visual reasoning and cognitively inspired artificial intelligence. He received his Ph.D. in Machine Learning and Neuroscience from Carnegie Mellon University. [Website]


Ph.D. students

Solim LeGris. Solim is a Ph.D student in the Psychology department at NYU and is co-advised by Todd Gureckis and Brenden Lake. Before coming to NYU, he received his BASc in Honours Cognitive Science from McGill University and worked on modelling categorical perception and categorization difficulty in neural networks. Solim is interested in computational cognitive science, more specifically in understanding what makes humans capable of learning concepts efficiently and using them flexibly across a broad range of situations. He is generally interested in building computational models that can both increase our understanding of cognition and bring machines closer to human-like learning and thought. [Website]

Wentao Wang. Wentao is a Ph.D. student at NYU’s Center for Data Science, working with Brenden Lake. Wentao received his Master’s degree in Computer Science at New York University Courant Institute of Mathematical Sciences in 2023. He received his BS in Computer Science from Peking University in 2020. He is broadly interested in the intersection of language language processing and cognitive science. In particular, he is interested in understanding what models have learned and building models that could generalize in human-like ways.

Phoebe Zeng. I am a Ph.D. student at Princeton’s Department of Computer Science. If math was erased from human memory, it seems likely we’d reinvent a similar mathematics. My research aims to interrogate the nature of this tight connection between human cognition and human mathematics, in hopes that it can teach us how to design artificial mathematicians. [Website]

Lab alumni