CogSci Gala

Joshua Tenenbaum (MIT) studies the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, he tries to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future. He approaches these topics with a range of empirical methods — primarily, behavioral testing of adults, children, and machines — and formal tools — drawn chiefly from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra. His work is driven by the complementary goals of trying to achieve a better understanding of human learning in computational terms and trying to build computational systems that come closer to the capacities of human learners.

Dr. Molly Crockett (Yale) is Director of the Crockett Lab at Yale. The Crockett Lab seeks to understand this paradox by investigating the psychological and neural mechanisms of social decision-making and impression formation. Their approach integrates social psychology, behavioral economics, neuroscience and philosophy. They use a range of methods including behavioral experiments, computational modeling, brain imaging, and pharmacology. Dr. Crockett is also an Assistant Professor of Psychology at Yale University and a Distinguished Research Fellow at the Oxford Centre for Neuroethics. Prior to joining Yale, Dr. Crockett was a faculty member at the University of Oxford's Department of Experimental Psychology and a Fellow of Jesus College. She holds a BSc in Neuroscience from UCLA and a PhD in Experimental Psychology from the University of Cambridge, and completed a Wellcome Trust Postdoctoral Fellowship with economists and neuroscientists at the University of Zürich and University College London.

Lillian Lee (Cornell) is most interested in connections between natural language processing and social interaction. More and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format; there are thus tremendous opportunities for natural-language processing to contribute to the analysis and facilitation of socially embedded processes.
A.B. Cornell 1993, math and computer science; Ph.D. Harvard 1997, computer science. 

Nikolaus Kriegeskorte (Columbia) studies computer vision. When we open our eyes, we have an immediate sense of the scene we’re in, the objects around us and how they might help us accomplish our goals. But, under the hood, billions of neurons burn a lot of energy to give us this instant sense of our surroundings. How they accomplish this is still a computational mystery. To solve this computational mystery, Dr. Kriegeskorte is seeking help from new machines inspired by the brain: algorithms called deep neural network models. A form of artificial intelligence (AI), neural network models are composed of many small computing elements: highly simplified, artificial ‘neurons’ that pass information from one layer in a hierarchy to the next. Their deep hierarchy mirrors the brain’s own organization in the visual system, with layers corresponding to areas whose neurons represent and interpret the image at ever higher levels of abstraction.

Aaron Courville (University of Montreal) is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of Mila – Quebec Artificial Intelligence Institute. His current research interests focus on the development of deep learning models and methods. He is particularly interested in developing probabilistic models and novel inference methods. While he has mainly focused on applications to computer vision, He is also interested in other domains such as natural language processing, audio signal processing, speech understanding and just about any other artificial-intelligence-related task.

Rahul Gupta (Amazon) is a Senior Applied Scientist at the Spoken Language Understanding Innovations (SLU-Innovations) team in Cambridge, Massachusetts. Since joining the Alexa organization, he has focused on designing NLU models for scalability and speed. More recently, his research has focused on designing trustworthy NLU systems that provide private and bias-free experience. He received his PhD from the University of Southern California in 2016 on interpreting non-verbal communications in human interaction. He has published over forty papers in avenues such as IEEE-Transactions of affective computing, IEEE-Spoken language Understanding workshop, ICASSP, Interspeech and Elselvier computer speech and language journal. He is also co-inventor on five patent pending technologies at Amazon.

Robert Glushko (Veo Systems, Commerce One, UC-Berkeley) is an Adjunct Full Professor at the University of California at Berkeley in the School of Information, where he has been since 2002.

He has over thirty years of R&D, consulting, and entrepreneurial experience in information systems and service design, content management, electronic publishing, Internet commerce, and human factors in computing systems. He founded or co-founded four companies, including Veo Systems in 1997, which pioneered the use of XML for electronic business before its 1999 acquisition by Commerce One. Veo's innovations included the Common Business Library (CBL), the first native XML vocabulary for business-to-business transactions, and the Schema for Object-Oriented XML (SOX), the first object-oriented XML schema language. From 1999-2002 he headed Commerce One's XML architecture and technical standards activities and was named an "Engineering Fellow" in 2000. In 2008 he co-founded and serves as a Director for Document Engineering Services, an international consortium of expert consultants in standards for electronic business.

Ruslan Salakhutdinov (Carnegie Mellon) is a UPMC professor of Computer Science in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. He works in the field of statistical machine learning. His research interests include Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.

Dan Yamins (Stanford) is a computational neuroscientist at Stanford University, where I'm an assistant professor of Psychology and Computer Science, and a faculty scholar at the Wu Tsai Neurosciences Institute. He works on science and technology challenges at the intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. The brain is the embodiment of the most beautiful algorithms ever written. His research group, the Stanford NeuroAILab, seeks to "reverse engineer" these algorithms, both to learn both about how our minds work and build more effective artificial intelligence systems.   

Talia Konkle (Harvard) has been an assistant professor at Harvard since 2015. She received her Ph.D. from MIT in Cognitive Science, and did her undergraduate work at UC Berkeley in Cognitive Science and Applied Math. Her research focuses on the cognitive and neural organization of high-level visual experience: how do we see and understand the visual world around us? She employs a combination of behavioral techniques, human functional neuroimaging and computational modeling approaches to characterize representational spaces of the mind and discover how they are mapped onto the surface of the brain.

Sam Gershman (Harvard) received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, where I worked with Ken Norman and Yael Niv. From 2013-2015, he was a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT, working with Josh Tenenbaum and Nancy Kanwisher. He is currently an Associate Professor in the Department of Psychology and Center for Brain Science at Harvard. His research focuses on computational cognitive neuroscience approaches to learning, memory, and decision making.

Ilker Yildirim (Yale) an Assistant Professor of Psychology with a secondary appointment in the Department of Statistics and Data Science. His research aims to elucidate the biological computations underlying how we see, reason about, and interact with our physical environment. How does perception transform raw sensory signals arriving at our sensory organs into things like objects and people, into things that we can think about? This is the key question that drives our research, which we tackle primarily with computational modeling that brings together a diverse range of approaches including probabilistic modeling, simulation engines (including graphics and physics engines), and advanced approximate Bayesian inference (including deep neural networks, sequential importance samplers, approximate bayesian computation methods, and their hybrids). His team tests these models empirically in behavioral and neural experiments to give a unified account of neural function, cognitive processes, and behavior.

Steven Pinker (Harvard) is an experimental psychologist who conducts research in visual cognition, psycholinguistics, and social relations. He grew up in Montreal and earned his BA from McGill and his PhD from Harvard. Currently Johnstone Professor of Psychology at Harvard, he has also taught at Stanford and MIT. He has won numerous prizes for his research, his teaching, and his nine books, including The Language Instinct, How the Mind Works, The Blank Slate, The Better Angels of Our Nature, and The Sense of Style. He is an elected member of the National Academy of Sciences, a two-time Pulitzer Prize finalist, a Humanist of the Year, a recipient of nine honorary doctorates, and one of Foreign Policy’s “World’s Top 100 Public Intellectuals” and Time’s “100 Most Influential People in the World Today.” He is Chair of the Usage Panel of the American Heritage Dictionary, and writes frequently for The New York Times, The Guardian, and other publications. His tenth book, to be published in February 2018, is called Enlightenment Now: The Case for Reason, Science, Humanism, and Progress.

Stefanie Tellex (Brown) is an assistant professor in the Computer Science Department at Brown University. The aim of her research program is to construct robots that seamlessly use natural language to communicate with humans. In twenty years, every home will have a personal robot which can perform tasks such as clearing the dinner table, doing laundry, and preparing dinner. As these machines become more powerful and more autonomous, it is critical to develop methods for enabling people to tell them what to do. Robots that can communicate with people using language can respond appropriately to commands given by humans, ask questions when they are confused, and request help when they get stuck. Stefanie’s team applies probabilistic methods, corpus-based training, and decision theory to develop interactive robotic systems that can understand and generate natural language. She completed my Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs. Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language. She has published at SIGIR, HRI, RSS, AAAI, IROS, and ICMI, winning Best Student Paper at SIGIR and ICMI. I was named one of IEEE Spectrum’s AI’s 10 to Watch and won the Richard B. Salomon Faculty Research Award at Brown University.