Hong Zhang, Postdoctoral Scholar
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
Daniel Gabric, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jeffrey Shallit
Sangho Suh, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Edith Law
Amirali Aghazadeh, Postdoctoral researcher
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
Ningning Xie, Research Associate
Department of Computer Science and Technology
University of Cambridge
Sangho Suh, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Edith Law
Sihang Liu
Department of Computer Science, University of Virginia
Ruosong Wang
School of Computer Science, Carnegie Mellon University
Krikamol Muandet
Research Group Leader, Empirical Inference Department
Max Planck Institute for Intelligent Systems
Society is made up of a set of diverse individuals, demographic groups, and institutions. Learning and deploying algorithmic models across these heterogeneous environments face a set of various trade-offs. In order to develop reliable machine learning algorithms that can interact successfully with the real world, it is necessary to deal with such heterogeneity.
Sushant Sachdeva, Assistant Professor
Department of Computer Science, University of Toronto
We give the first almost-linear time algorithm for computing exact maximum flows and minimum-cost flows on directed graphs. By well known reductions, this implies almost-linear time algorithms for several problems including bipartite matching, optimal transport, and undirected vertex connectivity.