Invited Speakers

Jitendra Malik is the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley. He received the 2013 IEEE PAMI-TC Distinguished Researcher in Computer Vision Award, the 2014 K.S. Fu Prize from the International Association of Pattern Recognition, the 2016 ACM-AAAI Allen Newell Award, the 2018 IJCAI Award for Research Excellence in AI, and the 2019 IEEE Computer Society Computer Pioneer Award. He is a fellow of the IEEE and the ACM. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.

Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin and a Research Director in Facebook AI Research (FAIR). Her research in computer vision and machine learning focuses on video, visual recognition, and action for perception or embodied AI. She is an IEEE Fellow, AAAI Fellow, Sloan Fellow, a Microsoft Research New Faculty Fellow, and a recipient of NSF CAREER and ONR Young Investigator awards, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial Intelligence (IJCAI), the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013.

Devin Guillory is a PhD student in computer science at UC Berkeley where he works in the fields of computer vision and machine learning. Prior to Berkeley, he obtained bachelor’s and master’s degrees in electrical engineering from Stanford and went on to serve as a Staff Data Scientist and technical lead of Search Ranking and Computational Advertising teams at Etsy. A founding engineer of Blackbird Technologies, Devin joined Etsy by way of acquisition. Throughout his career, he’s worked on a variety of machine learning problems in industrial and academic settings (e.g., computer vision, robotics, natural language processing, information retrieval, computational advertising, etc.) and has grown passionate about exploring areas where the theory and practice of machine learning systems diverge.