Program Overview

Main Conference 2021 June 21 - 24 (Monday - Thursday)
Workshops and Tutorials 2021 June 19, 20 and 25 (Saturday, Sunday & Friday)

Conference Schedule in 24 hour format - PDF

Workshop Schedule in 24 hour format - Day 1, Day 2, Day 3

Tutorial Schedule in 24 hour format - Day 1, Day 2


CVPR 2021 Invited Speakers and Discussion Panels

Panel One                                                                   Monday, June 21               6:00 PM – 7:30 PM  EDT

Moderated by Emily Denton,  Google's AI Ethical AI and Serge Belongie, Cornell Tech & Cornell University               


John Quinn, Makerere University (Uganda) / Google Ghana
Pablo Arbelaez, Universidad de los Andes
Catherine D’Ignazio, MIT
Meredith Whittaker, AI Now/NYU
Padmanabhan Anandan, Wadhwani Institute for AI
Constantinos Daskalakis, MIT


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Dr. John Quinn worked in Uganda since 2007, first for Makerere University as faculty in the Department of Computer Science, then for United Nations Global Pulse as technical lead of Pulse Lab Kampala, and now Sunbird AI. In 2018, Dr. Quinn concurrently joined the research team at Google Ghana. He has also spent time working at Tokyo Institute of Technology, Google Switzerland, Medical Emergency Relief International, and the Neonatal Unit at Edinburgh Royal Infirmary. Dr. Quinn received a BA in computer science from the University of Cambridge (2000), and my PhD in machine learning from the University of Edinburgh (2007). Dr. Quinn originally from Inverness in the North of Scotland.   Pablo Arbeláez received the PhD (with Hons.) degree in applied mathematics from the Université Paris-Dauphine, in 2005. He was a research scientist in the Computer Vision Group, UC Berkeley from 2007 to 2014. He currently holds a faculty position with the Universidad de los Andes in Colombia. His research interests include computer vision, where he has worked on a number of problems, including perceptual grouping, object recognition and the analysis of biomedical images.
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Catherine D’Ignazio is a hacker mama, scholar, and artist/designer who focuses on feminist technology, data literacy and civic engagement. She has run women’s health hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea level rise. Her 2020 book from MIT Press, Data Feminism, co-authored with Lauren Klein, charts a course for more ethical and empowering data science practices. D’Ignazio is an assistant professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT where she is the Director of the Data + Feminism Lab.   Meredith Whittaker is the Minderoo Research Professor at New York University and the founder of Google’s Open Research group and co-founder of the AI Now Institute.  Her research and advocacy focus on the social implications of artificial intelligence and the tech industry responsible for it, with a particular emphasis on power and the political economy driving the commercialization of computational technology. 
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Padmanabhan Anandan is the CEO of Wadhwani Institute for Artificial Intelligence, an independent not-for-profit Research institute focused on developing artificial intelligence based applications for social good. He was formerly vice president for research at Adobe Systems and prior to that a distinguished scientist and managing director of Microsoft Research. He was managing director at Microsoft Research India, which he founded in January 2005 in Bangalore. He joined Microsoft Research in Redmond, Washington in 1997, where he founded and built the Interactive Visual Media group.    Constantinos (aka "Costis" with an accent on 'i') Daskalakis is a Professor of Electrical Engineering and Computer Science at MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, and a PhD in Electrical Engineering and Computer Science from UC Berkeley. He works on Computation Theory and its interface with Game Theory, Economics, Probability Theory, Machine Learning and Statistics. He has resolved long-standing open problems about the computational complexity of Nash equilibrium, and the mathematical structure and computational complexity of multi-item auctions. His current work focuses on high-dimensional statistics and learning from biased, dependent, or strategic data. He has been honored with the ACM Doctoral Dissertation Award, the Kalai Prize from the Game Theory Society, the Sloan Fellowship in Computer Science, the SIAM Outstanding Paper Prize, the Microsoft Research Faculty Fellowship, the Simons Investigator Award, the Rolf Nevanlinna Prize from the International Mathematical Union, the ACM Grace Murray Hopper Award, and the Bodossaki Foundation Distinguished Young Scientists Award.





Panel Two                                                                   Wednesday June 23               4:00 AM – 5:30 AM  EDT

Moderated by Laura Sevilla Lara, University of Edinburgh and Andrea Vedaldi, University of Oxford.


Zhi-hua Zhou, Nanjing University
Miguel Otaduy, Universidad Rey Juan Carlos
Max Welling, University of Amsterdam

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Zhi-Hua Zhou received his B.Sc., M.Sc. and Ph.D. degrees in computer science from Nanjing University, China, in 1996, 1998 and 2000, respectively, all with the highest honor. He joined the Department of Computer Science & Technology of Nanjing University as an Assistant Professor in 2001, and at present he is a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, Standing Deputy Director of the National Key Lab for Novel Software Technology, and Founding Director of  LAMDA (the Institute of Machine Learning and Data Mining) at Nanjing University.   Dr. Otaday is the director of the Multimodal Simulation Lab, and professor of computer science at Universidad Rey Juan Carlos since 2008. He holds a BS (2000) in Electrical Engineering from Mondragon University, and MS (2003) and PhD (2004) in Computer Science from the University of North Carolina at Chapel Hill. From 2005 to 2008, he was a senior researcher at the Computer Graphics Laboratory at ETH Zurich.
Dr. Otaduy designs computer models of biomechanics, the objects around us, and our interaction. His team creates simulations for computer animation or virtual reality, but also creates innovative solutions for design in real-world applications as diverse as fashion, virtual touch, medicine, or robotics.
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Prof. Dr. Max Welling  is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. He has a secondary appointment as a senior fellow at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep learning which got acquired by Qualcomm in summer 2017. In the past he held postdoctoral positions at Caltech (’98-’00), UCL (’00-’01) and the U. Toronto (’01-’03). He received his PhD in ’98 under supervision of Nobel laureate Prof. G. ‘t Hooft.     


Panel Three                                                                   Thursday June 24               1:30 PM – 3:00 PM  EDT

Moderated by Aude Oliva, MIT-IBM Watson AI Lab, Camillo Jose Taylor, University of Pennsylvania


Katie Bouman, Caltech
Pieter Abbeel, UC Berkeley
Su-hua Wang, UC Santa Cruz
Moustapha Cisse, Google Ghana
Matthias Bethge, University of Tuebingen
Noah Smith, University of Washington/Allens Insitute
Liang Huang, Baidu/Oregon State


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Dr. Bouman is a Rosenberg Scholar and Assistant Professor of Computing and Mathematical Sciences (CMS) and by courtesy in Electrical Engineering and Astronomy at Caltech in Pasadena, California. Her research focuses on computational imaging: designing systems that tightly integrate algorithm and sensor design, making it possible to observe phenomena previously difficult or impossible to measure with traditional approaches. Dr. Bouman's group at Caltech combines ideas from signal processing, computer vision, machine learning, and physics to find and exploit hidden signals for both scientific discovery and technological innovation.


Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). 

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Su-hua Wang’s research investigates cognitive development situated in sociocultural contexts, including technology, parent-child interaction, and children’s play.  Her research group has been working on multiple projects, to understand how learning occurs in the first five years as babies and young children interact with parents in everyday activities that involve books, toys, and technology. Her group is particularly interested in cultural ways of learning that are supported by families from different backgrounds


Dr. Moustapha Cisse is a research scientist at Google and head of the Google AI center in Accra, Ghana, where he leads research efforts in foundational machine learning and its applications to solving complex societal challenges. Moustapha is also a professor of machine learning at the African Institute of Mathematical Sciences, where where he is thefounder and director of the African Master’s of Machine Intelligence program. He was previously a research scientist at Facebook AI Research. Before that, he completed his PhD at University Pierre and Marie Curie in France.

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Dr. Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy. He is also an Amazon scholar and co-founder of Deepart UG, and Layer7 AI GmbH, and co-initiator of the European ELLIS initiative. His main research focus is on robust vision and neural decision making with the goal to advance internal model learning with neural networks. He received the first Bernstein Prize for Computational Neuroscience in 2006 and later became director of the Bernstein Center Tübingen and vice chair of the German Bernstein network.


Dr. Noah Smith is Professor of Computer Science & Engineering at the University of Washington, Adjunct in Linguistics, Affiliate of the Center for Statistics and the Social Sciences, and Senior Data Science Fellow at the eScience Institute. He is also Senior Research Manager for the AllenNLP team at the Allen Institute for Artificial Intelligence. Previously, he was Finmeccanica Associate Professor in the School of Computer Science at Carnegie Mellon University, completed his Ph.D. as a Hertz Foundation Fellow at Johns Hopkins University, and studied at the Universities of Maryland and Edinburgh and Western Maryland College. His undergraduate, graduate, and postdoctoral advisees have earned positions at leading organizations all over the world, where they make wide-ranging and high-impact research contributions.


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Dr. Liang Huang is currently an Associate Professor of EECS at Oregon State University and Distinguished Scientist (part-time) at Baidu Research USA. Before that he was Assistant Professor for three years at the City University of New York (CUNY) and a part-time Research Scientist with IBM's Watson Group. He graduated in 2008 from Penn and has worked as a Research Scientist at Google and a Research Assistant Professor at USC/ISI. Most of his work develops fast algorithms and provable theory to speedup large-scale natural language processing, structured machine learning, and computational structural biology. His recognitions include ACL 2019 Keynote Speech, ACL 2008 Best Paper Award (sole author), EMNLP 2016 Best Paper Honorable Mention, several best paper nominations (ACL 2007, EMNLP 2008, ACL 2010, SIGMOD 2018), two Google Faculty Research Awards (2010 and 2013), and a University Teaching Prize at Penn (2005). He also co-authored a best-selling textbook in China on algorithms for programming contests. His work on simultaneous translation has been covered by numerous media reports.