Tengyu's work brings together techniques from theoretical computer science, applied mathematics, statistics, probability, and information theory to answer the twin questions of how to design successful nonlinear models and efficiently optimize nonconvex training functions for those models. Several of his publications develop mathematical tools to characterize the optimization landscape of various machine learning problems including dictionary learning, matrix completion, tensor decomposition, and linearized (recurrent) neural nets; some of these results have been published in Transactions of the Association for Computational Linguistics and the Journal of Machine Learning Research. Tengyu has also worked on sum-of-squares algorithms and statistical and communication trade-offs in machine learning, both areas having technical and conceptual open problems that he intends to continue investigating.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. Her research focuses on perception for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning.
Oussama Khatib is Professor of Computer Science at Stanford University. His work on advanced robotics focuses on methodologies and technologies in human-centered robotics including humanoid control architectures, human motion synthesis, interactive dynamic simulation, haptics, and human- friendly robot design.
Jure Leskovec is an Associate Professor of Computer Science at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the web and on-line media.
Fei-Fei Li is an Associate Professor at the Computer Science Department at Stanford University. She is the Director of the Stanford Vision Lab, where she works with the most brilliant students and colleagues worldwide to build smart algorithms that enable computers and robots to see and think, as well as to conduct cognitive and neuroimaging experiments to discover how brains see and think.
Christopher Manning is the inaugral Thomas M. Siebel Professor in Machine Learning in the Departments of Computer Science and Linguistics at Stanford University. His research goal is computers that can intelligently process, understand, and generate human language material. Manning is a leader in applying Deep Learning to Natural Language Processing, with well-known research on Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding.
Juan Carlos Niebles received an Engineering degree in Electronics from Universidad del Norte (Colombia) in 2002, an M.Sc. degree in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in 2007, and a Ph.D. degree in Electrical Engineering from Princeton University in 2011. He is a Senior Research Scientist at the Stanford AI Lab and Associate Director of Research at the Stanford-Toyota Center for AI Research since 2015. He is also an Associate Professor of Electrical and Electronic Engineering in Universidad del Norte (Colombia) since 2011. His research interests are in computer vision and machine learning, with a focus on visual recognition and understanding of human actions and activities, objects, scenes, and events. He is a recipient of a Google Faculty Research award (2015), the Microsoft Research Faculty Fellowship (2012), a Google Research award (2011) and a Fulbright Fellowship (2005).
Dorsa Sadigh is an Assistant Professor in the Computer Science Department and Electrical Engineering Department at Stanford University. Her work is focused on the design of algorithms for autonomous systems that safely and reliably interact with people.
SAIL-JD Supported Projects:
Silvio Savarese is an Associate Professor of Computer Science at Stanford University, Director of the SAIL-Toyota Center for AI Research at Stanford and steering committee member for the SAIL-JD AI Research Initiative. His research interests include computer vision, robotic perception and machine learning.
SAIL-JD Supported Projects: