People

Principal investigator


Brenden Lake. Brenden is an Assistant Professor of Psychology and Data Science at New York University, and a Research Scientist at Facebook AI Research. 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. Brenden’s research focuses on computational problems that are easier for people than they are for machines, such as learning new concepts, compositional generalization, and question asking. [Website]

Postdoctoral researchers


Emin Orhan. I obtained my PhD in Brain & Cognitive Sciences from the University of Rochester. I was previously a postdoc at the Center for Neural Science at NYU and then jointly at Rice University & Baylor College of Medicine. My research interests lie at the intersection of deep learning, cognitive science, and computational neuroscience. Broadly speaking, my research has three main goals: 1) Understanding how current deep learning models work, as well as characterizing their failure modes. 2) Comparing the behavior of deep learning models with qualitative and quantitative data from cognitive science and experimental neuroscience to better understand the mechanistic underpinnings of natural intelligence and to point out ways in which these models can be improved. 3) Based on the insights gleaned from the first two goals, improving the current generation of deep learning models. [Website]

Wai Keen Vong. I’m a postdoctoral researcher at the Center for Data Science at New York University,. Previously, I was a postdoctoral researcher at the Cognitive and Data Science Lab at Rutgers University — Newark, and I completed my Ph.D. in Psychology from the Computational Cognitive Science Lab at the University of Adelaide. My research interests are in cognitive science and artificial intelligence, with an eye towards building computational models that can learn in human-like ways. In particular, my research focuses on (1) How people learn concepts and categories from different kinds of labeled information (2) How people acquire this knowledge socially, from teaching, question asking to dialogs, and (3) How these models can be scaled up to deal with the complexities of real-world settings. My research is conducted using a combination of behavioural experiments, Bayesian modeling and deep learning. [Website]

Ph.D. students


Reuben Feinman. Reuben is a third-year Ph.D. student in Neural Science at New York University and a Google Ph.D. Fellow in Computational Neuroscience. He received his Sc.B. in Applied Mathematics from Brown University in 2015, capping years of coursework in pattern theory and related disciplines. Reuben’s research focuses on the neural mechanisms of efficient concept learning. Combining techniques from neural networks, Bayesian modeling and approximate inference, his work aims to develop statistical models of human perception and learning that capture critical ingredients from cognitive science and that help tie the gap between symbolic and sub-symbolic theories of cognition. [Website]

Yanli Zhou. Yanli is a Ph.D. student at the NYU Center for Data Science. Previously at NYU, she received her BA in Mathematics and Psychology in 2016 and an MS in Data Science in 2018. Before joining the lab, she worked as a research assistant under the supervision of Dr. Wei Ji Ma at the Center for Neural Science and Department of Psychology where she built probabilistic models of visual decision-making tasks. She is broadly interested in incorporating insights from cognitive science into building AI systems that can efficiently and flexibly learn.

Guy Davidson. Guy is a first-year Ph.D. student at the Center for Data Science, working with Professors Brenden Lake and Todd Gureckis. Guy’s interests center around the intersection between human cognition and machine learning, and particularly, what can we learn from studying humans to allow us to design wiser machine learning systems. His current research focus on inserting object-based and relational reasoning to reinforcement learning algorithms, and on the flexible ways humans generate goals in novel environments. Before coming to NYU, Guy served six years in the Israel Defense Forces, and then graduated (summa cum laude) with a B.Sc. in Computational Sciences from the Minerva Schools. He previously collaborated with Professors Yael Niv (Princeton) and Michael C. Mozer (Google Research and CU Boulder). Outside of his studies, Guy loves to play ultimate frisbee and is developing an appreciation for pour-over coffee and fermenting foods. [Website]

Assistant research scientists


Kanishk Gandhi. I am a first year Master’s student at New York University and a Morse Fellow at the Department of Electrical Engineering. My current research with Prof. Lake focuses on designing Reinforcement Learning agents to think and learn more like humans do. I am broadly interested in utilizing concepts in the cognitive sciences to improve contemporary deep learning algorithms. I have completed my undergraduate from IIT Kanpur majoring in Electrical Engineering with a minor in AI. In the past I have worked on natural language generation and predicting video watching patterns of people. I have also been part of a couple of interesting startup projects including LucidLaw (A legal research startup). [Website]

Vaibhav Gupta. Vaibhav recently completed his Masters in Computer Science from the Courant Institute, NYU. His work with the lab is broadly aimed at using deep learning techniques to model how learning takes place in humans. He is especially interested in exploring the use of self-supervised deep learning for vision based tasks. Prior to NYU, Vaibhav worked with Amazon for a few years. [Website]

M.S. and undergraduate students


Alexa Tartaglini. I am a third-year student in computer science and mathematics at the New York University Courant Institute of Mathematical Sciences. I am interested in comparing the mechanisms of human and machine intelligence, especially with regards to cognitive tasks that are difficult for computers to perform such as category and concept learning, object recognition, and commonsense reasoning. I am particularly interested in the role of shape in human category representation and the relationship between learning and reasoning.

Lab alumni