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Dr. 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 completed her PhD at Stanford University and her postdoctoral fellowship at Carnegie Mellon University. She has served as a Senior Program Committee member for WACV’16, CVPR’18, CVPR’19, NeurIPS'19 and CVPR'20, has organized 9 workshops and tutorials on large-scale recognition, and has given more than 50 invited talks at universities, companies, workshops and conferences. She was awarded the PAMI Everingham Prize in 2016 as one of the leaders of the ImageNet Large Scale Visual Recognition Challenge, the MIT Technology Review's 35-under-35 Innovator award in 2017 and was named one of Foreign Policy Magazine's 100 Leading Global Thinkers 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 AI. She co-founded the Stanford AI4ALL camp teaching AI for social good to high school girls and the Princeton AI4ALL camp teaching AI technology and policy to high school students from underrepresented racial groups.
- "End-to-End Learning of Action Detection from Frame Glimpses in Videos" by Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei in CVPR 2016
- "Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation" by Zeyu Wang et al. in CVPR 2020
- "What's the Point: Semantic Segmentation with Point Supervision" by Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei in ECCV 2016
- "Human Uncertainty Makes Classification More Robust" by Joshua Peterson, Ruairidh Battleday, Thomas Griffiths, Olga Russakovsky in ICCV 2019
- "What's in a Question: Using Visual Questions as a Form of Supervision" by Siddha Ganju, Olga Russakovsky, Abhinav Gupta in CVPR 2017
- "Towards Fairer datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy" by Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng and Olga Russakovsky in FAT* 2020
- "ImageNet Large Scale Visual Recognition Challenge" by Olga Russakovsky et al. in IJCV 2015