Organizers and Program Committee

Deepti is a Research Scientist at Facebook AI Applied Research (FAIAR). Her research interests lie at the intersection of Computer Vision, Image and Video Processing, and Machine Learning with specialization in perceptual image and video quality, large-scale video action recognition, and algorithmic fairness. 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."

Abhimanyu is a 4th-year Ph.D. student in the Human Dynamics group at MIT, advised by Professor Alex Pentland. His research interests are in robust and cooperative (federated) machine learning, including problems in multi-agent decision-making and differential privacy. Prior to this, he received a master's degree in Computer Science and bachelor's degree in Electrical Engineering at IIT Delhi, where I was advised by Professor Sumeet Agarwal. He has also spent time as a research intern at Facebook AI, and was a post-baccalaureate fellow at the Department of Economics at Harvard, under Professor Ed Glaeser. His research has been supported by a Snap Research Scholarship (2019) and an Emerging Worlds Fellowship (2017).

Angelina Wang is a 2nd year PhD student at Princeton advised by Professor Olga Russakovsky. She is interested in machine learning fairness and algorithmic bias. She completed her undergraduate degree in EECS from UC Berkeley.

Laurens is a Research Director at Facebook AI Research (FAIR), and leads FAIR’s New York site. 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.

Prof. Olga Russakovsky is an Assistant Professor in the Computer Science Department at Princeton University. Her research is in computer vision, closely integrated with the fields of machine learning, human-computer interaction and fairness, accountability and transparency. She has been awarded the AnitaB.org's Emerging Leader Abie Award in honor of Denice Denton in 2020, the CRA-WP Anita Borg Early Career Award in 2020, the MIT Technology Review's 35-under-35 Innovator award in 2017, the PAMI Everingham Prize in 2016 and Foreign Policy Magazine's 100 Leading Global Thinkers award in 2015. In addition to her research, she co-founded and continues to serve on the Board of Directors of the AI4ALL foundation dedicated to increasing diversity and inclusion in Artificial Intelligence (AI). She completed her PhD at Stanford University in 2015 and her postdoctoral fellowship at Carnegie Mellon University in 2017.

Prof. 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 NVIDIA female leader in computer vision award in 2020, AIMiner top 100 most influential scholars in Machine Learning (2020), MIT EECS Rising Star in 2015, and is a recipient of the NSF Graduate Fellowship. 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).

Dhruv Mahajan is a Research Scientist on the Computer Vision team at Facebook AI. He joined Facebook Research in January 2017. Prior to that, he worked at Microsoft, MSR India and Yahoo! Research. He obtained his Ph.D. titled “A Theoretical Analysis of Light Transport” at Columbia University in 2009. His research interests lie in extreme vision that entails understanding core vision problems like classification, detection, etc. in a weakly-supervised setting at the scale of billions of data points, high dimensional label space and large model capacity. He is also interested in the low-shot learning (other spectrum of extreme vision) to address the long tail issues in data. He has also worked in developing new and efficient distributed optimization algorithms respecting system constraints like communication and machine failures.

Han Zhao is a machine learning researcher at the D.E.Shaw group and an incoming assistant professor at the Department of Computer Science, the University of Illinois at Urbana-Champaign. He completed his Ph.D. from the Machine Learning Department, Carnegie Mellon University, where he was advised by Geoff Gordon. Before coming to CMU, he obtained his BEng degree in Computer Science from Tsinghua University (honored as a Distinguished Graduate) and MMath degree in mathematics from the University of Waterloo (honored with the Alumni Gold Medal Award). He has also spent time at Huawei Noah's Ark Lab, Baidu Research, Microsoft Research, and the D. E. Shaw Group. His research interests are broadly in machine learning, with a focus on representation learning, probabilistic reasoning, domain adaptation and algorithmic fairness.