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. My research interests lie at the intersection of machine learning, psychology, and neuroscience. I am interested in understanding the mechanistic underpinnings of human intelligence: what are the computational principles and circuit-level mechanisms that underlie our remarkable cognitive and perceptual capabilities? Recent progress in machine learning opened up exciting new possibilities for addressing some of the most fundamental questions at the intersection of cognitive science and machine learning: questions such as the amount and type of data, and the inductive biases necessary to learn sophisticated world models, robust, general-purpose perceptual, and semantic representations. We can now routinely train very large models on large, complex datasets and probe the capabilities of the resulting systems in fine detail. This gives us a vastly improved capacity to grapple with the full scale and richness of the perceptual and cognitive problems faced by humans in the real world. I utilize this new capacity in my current work. I like working on general algorithms and models that can scale to large (ideally, unlimited) real-world data. [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 Ph.D. student at the Center for Neural Science and a Google Ph.D. Fellow in Computational Neuroscience. He received his Sc.B. in Applied Mathematics from Brown University in 2015 after completing a thesis under Thomas Serre and Stuart Geman. Reuben’s Ph.D. research focuses on the symbolic and sub-symbolic characteristics of human concept representations, emphasizing everyday concepts like animals, vehicles and handwritten characters. Using a synthesis of ingredients from deep neural networks and structured Bayesian models, he aims to provide a comprehensive computational account for the flexibility of human concepts. [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 Ph.D. student at the Center for Data Science, working with Professors Brenden Lake and Todd Gureckis. Guy is interested in 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 work focuses on object-based and relational approaches to reinforcement learning 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 enjoys pour-over coffee and fermenting foods. [Website]

Assistant research scientists

Laura Ruis. Laura is an assistant research scientist at the CDS working on understanding generalization of machine learning methods. She finished her Master’s degree in Artificial Intelligence at the University of Amsterdam, where she focused on machine learning for natural language processing. During this time, she worked on things like probabilistical graphical models for dependency parsing and non-autoregressive transformers for text generation, among other things, and developed an interest in the generalization capabilities of deep learning methods. Her current research focuses on improving the robustness of contemporary methods to distributional shift and endowing methods with compositional generalization capabilities with techniques like meta learning. [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