USA - 2021
For his dissertation, "Learning Through the Lens of Robustness," nominated by the Massachusetts Institute of TechnologyPress Release
2021 ACM Doctoral Dissertation Award
Manish Raghavan is the recipient of the 2021 ACM Doctoral Dissertation Award for his dissertation "The Societal Impacts of Algorithmic Decision-Making." Raghavan’s dissertation makes significant contributions to the understanding of algorithmic decision making and its societal implications, including foundational results on issues of algorithmic bias and fairness.
Algorithmic fairness is an area within AI that has generated a great deal of public and media interest. Despite being at a very early stage of his career, Raghavan has been one of the leading figures shaping the direction and focus of this line of research.
Raghavan is a Postdoctoral Fellow at the Harvard Center for Research on Computation and Society. His primary interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. Raghavan received a BS degree in Electrical Engineering and Computer Science from the University of California, Berkeley, and MS and PhD degrees in Computer Science from Cornell University.
Dimitris Tsipras’ dissertation, “Learning Through the Lens of Robustness,” was recognized for foundational contributions to the study of adversarially robust machine learning (ML) and building effective tools for training reliable machine learning models. Tsipras made several pathbreaking contributions to one of the biggest challenges in ML today: making ML truly ready for real-world deployment.
Tsipras is a Postdoctoral Scholar at Stanford University. His research is focused on understanding and improving the reliability of machine learning systems when faced with the real world. Tsipras received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, as well as SM and PhD degrees in computer science from the Massachusetts Institute of Technology (MIT).
Pratul Srinivasan and Benjamin Mildenhall are awarded Honorable Mentions for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem. Srinivasan’s dissertation, "Scene Representations for View Synthesis with Deep Learning," and Mildenhall’s dissertation, “Neural Scene Representations for View Synthesis,” addressed a long-standing open problem in computer vision and computer graphics. That problem, called “view synthesis” in vision and “unstructured light field rendering” in graphics, involves taking just a handful of photographs of a scene and predicting new images from any intermediate viewpoint. NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication.
Srinivasan is a Research Scientist at Google Research, where he focuses on problems at the intersection of computer vision, computer graphics, and machine learning. He received a BSE degree in Biomedical Engineering and BA in Computer Science from Duke University and a PhD in Computer Science from the University of California, Berkeley.
Mildenhall is a Research Scientist at Google Research, where he works on problems in computer vision and graphics. He received a BS degree in Computer Science and Mathematics from Stanford University and a PhD in Computer Science from the University of California, Berkeley.