New York University Human & machine learning lab

Principal Investigator Brenden Lake

Brenden is an Assistant Professor of Psychology and Data Science at New York 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. Brenden is a recipient of the Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science, he is a MIT Technology Review Innovator Under 35, and his research was selected by Scientific American as one of the most important advances of 2016. Brenden's research focuses on computational problems that are easier for people than they are for machines, such as learning new concepts, creating new concepts, learning-to-learn, and asking questions.
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Postdoc 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.
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Postdoc 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.
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Ph.D. Student Anselm Rothe

I am a 5th year PhD student at New York University. Advised by Todd Gureckis and Brenden Lake, I am studying how people and machines ask questions. Asking questions is one of the human mind's greatest tools to learn about the world. I am fascinated by the comparison of human learning (cognitive science) and machine learning (artificial intelligence), and how insights in one domain can inform the other. From this perspective, I have also worked on other research projects, for example investigating how people infer an underlying causal structure based on a few observations.
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Ph.D. Student Reuben Feinman

Reuben is a second-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.
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Ph.D. Student 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.

M.S. Student 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).

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M.S. Student Ziyun Wang

Ziyun is a first-year MS student at the Computer Science Department of NYU. He received his Bachelor’s degree in Computer Science and Technology at Tsinghua University, China, where he had worked on research projects about language generation and information extraction for two years. Ziyun is broadly interested in discovering the potentials and limitations of state-of-the-art deep neural models, and advancing language utilization ability of machines. In the belief that the key for better AI lies in the field of cognitive science, he is especially interested in taking inspirations from how humans utilize natural languages, and developing more powerful machine learning models.

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