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).
In her role as Executive Director of Strategic Research Initiatives, Erika leads a number of programs that help companies engage in research at Stanford, including the Stanford AI Lab (SAIL), the Center for AI Safety, the Stanford Data Science Initiative, the AI for Health Initiative, the SAIL-Toyota AI Research Center and the SAIL-JD Initiative. Erika has a PhD in Biomedical Informatics from Stanford University and a Masters of Science in Statistics from UC San Diego. With a background in statistics, machine learning, biology, medical device development, and regulatory affairs, Erika is particularly interested in interpretable machine learning for clinical applications and value-based design as well as the development of unbiased and fair AI algorithms for decision making.