Short Bio

Dr. 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).

(updated Sept 2020)

Long Bio

Dr. Judy Hoffman is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology where she is also affiliated with the Machine Learning Center. She completed her PhD in Electrical Engineering and Computer Science at the University of California, Berkeley in August 2016 and her postdoctoral fellowship at Stanford University in 2017 and at the University of California, Berkeley in 2018. Her research lies at the intersection of computer vision and machine learning, with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness.

Her research focuses on tackling real-world variation and scale while minimizing human supervision. She develops learning algorithms which facilitate transfer of information through unsupervised and semi-supervised model adaptation and generalization. She is broadly interested in the sensitivities of deep learning models to dataset and model bias and has studied the affect due to natural and adversarial deviations, as well as the adverse impact of dataset bias on underrepresented populations. Her research has been covered in popular news outlets like Vox, Business Insider, The Guardian, and NBC News. She has published more than 30 peer-reviewed articles and her work has been cited more than 10,000 times.

In addition to her research, she has devoted her time to a number of diversity and outreach activities. She co-founded and continues to support Women in Computer Vision, an organization which provides mentorship and travel support for female identifying students and early-career colleagues (founded 2015). The organization routinely sponsors ~40 women to travel to the premier computer vision conferences and pairs each with a senior member of the community for mentorship. She served as co-President of the Women in Computer Science and Engineering at UC Berkeley where she spearheaded an undergraduate mentoring program and was given a leadership award for her efforts (2013). She organized a workshop at the Grace Hopper Conference to help undergraduates in applying to graduate school (2012) and serves as a mentor for the African Masters in Machine Intelligence (2020).

She has been awarded numerous honors for her research and leadership. She received the NVIDIA female leader in computer vision award in 2020, was recognized as one of the top 100 influential scholars in machine learning (AIMiner) in 2020, and the MIT EECS Rising Star award in 2015. Within the computer vision and machine learning communities she has served as an Associate Editor for IJCV (2020), an Area Chair for CVPR'19, ICCV'19, ICLR'19, CVPR'20, ICLR'20, ICML'20, and CVPR'21 and will be the tutorial chair for ICCV'23. She has organized multiple workshops and tutorials on learning with limited supervision, and has given more than 50 invited talks at conferences, workshops, companies, and universities.

(updated Sept 2020)