معرفی
Brenden Lake is an Associate Professor of Psychology and Data Science at New York University (NYU), affiliated with the College of Arts & Science. His research focuses on the intersection of human and machine intelligence, aiming to understand the cognitive ingredients enabling flexible learning. He leads the Human & Machine Learning Lab at NYU’s Center for Data Science, collaborating with the CILVR Lab and NYU’s Computational Cognitive Science community.
Lake holds a Ph.D. in Cognitive Science from MIT (2014), an M.S., and B.S. in Symbolic Systems from Stanford University (2009). He previously served as a Moore-Sloan Data Science Fellow (2014–2017) before becoming an Assistant Professor in 2017. His work bridges cognitive science and AI, emphasizing neuro-symbolic models and learning from developmentally realistic data.
Key research interests include concept learning, compositional generalization, and abstract reasoning. His lab’s recent breakthroughs include modeling language acquisition through child-centric datasets and developing neural networks achieving human-like systematic generalization. Notable publications include work in Nature, Science, and top machine learning conferences. He teaches courses on computational cognitive modeling and AI theory, emphasizing interdisciplinary approaches.
Scientific contributions include the Omniglot benchmark and foundational papers on probabilistic program induction. His work has been featured in The New York Times, Scientific American, and Nature News.
