Organizers

Deepti is a Research Scientist at Facebook AI Research (FAIR) where she works on Computer Vision, Image and Video Processing, and Machine Learning. Specifically, she focuses on problems such as building fair and inclusive computer vision models, ML explainability, and perceptual image and video quality. Prior to joining Facebook AI, Deepti obtained her Masters and PhD at the University of Texas at Austin in 2017 where she worked with Prof. Alan Bovik on her PhD thesis titled “Perceptual quality assessment of real-world images and videos."

Aaron Adcock is a Research Engineering Manager at Facebook AI Research (FAIR) where he supports the New York Computer Vision engineering team. His work focuses on building the tools and infrastructure needed to responsibly accelerate, scale, and productionize computer vision research. Before joining FAIR, he worked on applications of machine learning in the search, ranking, and recommendation of places across Facebook products. He also completed a Ph.D. in Electrical Engineering at Stanford University in 2014.

Angelina Wang is a PhD student at Princeton University advised by Professor Olga Russakovsky. She works on machine learning fairness and algorithmic bias and is supported by the National Science Foundation Graduate Student Fellowship. Previously she interned with Microsoft’s FATE (Fairness, Accountability, Transparency, and Ethics in AI) and Cognitive Services teams, and completed her undergraduate degree in EECS at UC Berkeley.

Laurens van der Maaten is a Research Director at Facebook AI Research (FAIR). His research focuses on machine learning and computer vision. Before, he worked as an Assistant Professor (with tenure) at Delft University of Technology, as a post-doctoral researcher at UC San Diego, and as a Ph.D. student at Tilburg University. He is interested in a variety of topics in machine learning and computer vision, including privacy and developing inclusive AI systems.

Vicente Ordóñez-Román is Associate Professor in the Department of Computer Science at Rice University and Amazon Visiting Academic at Alexa AI. His research interests lie at the intersection of computer vision, natural language processing and machine learning. His focus is on building efficient visual recognition models that can perform tasks that leverage both images and text. He has received best paper awards at EMNLP 2017 and ICCV 2013. He has also been the recipient of an NSF CAREER Award and multiple industry faculty awards. Vicente obtained his PhD in Computer Science at the University of North Carolina at Chapel Hill in 2015, an MS at Stony Brook University, and an engineering degree at the Escuela Superior Politécnica del Litoral in Ecuador. In the past, he has also been a visiting researcher at the Allen Institute for Artificial Intelligence and a visiting professor at Adobe Research.

Judy Hoffman is an Assistant Professor in the School of Interactive Computing at Georgia Tech and a member of the Machine Learning Center. Her research lies at the intersection of computer vision and machine learning with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness. She has been awarded the Samsung AI Researcher of the Year (2021), Google Scholar Faculty Award (2022), NVIDIA female leader in computer vision award (2020), AIMiner top 100 most influential scholars in Machine Learning (2020) and MIT EECS Rising Star in 2015. In addition to her research, she co-founded and continues to advise for Women in Computer Vision, an organization which provides mentorship and travel support for early-career women in the computer vision community. Prior to joining Georgia Tech, she was a Research Scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016 after which she completed Postdocs at Stanford University (2017) and UC Berkeley (2018).

Reza Shokri is a NUS Presidential Young Professor of Computer Science. His research focuses on data privacy and trustworthy machine learning. He is a recipient of the IEEE Security and Privacy (S&P) Test-of-Time Award 2021, for his paper on quantifying location privacy. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models. He received the NUS Early Career Research Award 2019, VMWare Early Career Faculty Award 2021, and Intel Faculty Research Award (Private AI Collaborative Research Institute) 2021-2022. He obtained his PhD from EPFL.

Been Kim is a staff research scientist at Google Brain. Her research focuses on improving interpretability in machine learning: not only by building interpretability methods but also challenging them for their validity. She gave a talk at the G20 meeting in Argentina in 2019. Her work TCAV received UNESCO Netexplo award, was featured at Google I/O 19' and in Brian Christian's book on "The Alignment Problem". Been gave keynote at ECML 2020, tutorials on interpretability at ICML, University of Toronto, CVPR and at Lawrence Berkeley National Laboratory. She was a co-workshop Chair ICLR 2019, and has been an (senior) area chair at conferences including NeurIPS, ICML, ICLR, and AISTATS. She received her PhD from MIT.

Cristian Canton is a research manager at Facebook where he currently leads the AI Red Team for the company, focused on prevention of AI weaponization. In the past, he managed the computer vision team within the objectionable and harmful content domain (i.e. detect and remove all the bad visual content in Facebook). From 2012-16, he was at Microsoft Research in Redmond (USA) and Cambridge (UK) where he worked on large scale Computer Vision and machine learning problems. From 2009-2012, he was the lead engineer at Vicon (Oxford), bringing CV to produce visual effects for the cinema industry.