Soham’s research interests broadly include topics in machine learning and game theory. In machine learning, his interests span optimization methods for machine learning problems, distributed algorithms and online learning. Soham is co-advised by Dana Nau, and also collaborates with Michele Gelfand for his work on game theoretic models of human behavior and cultural bias. Soham has interned at IBM Almaden Research Center and DeepMind.
Zheng studies optimization and machine learning. His work is focused on automated and distributing optimization routines for model fitting and data science. Previously, he was a Project Officer in the Visual Computing Research Group at Nanyang Technological University in Singapore, where worked on domain adaptation for computer vision. Zheng has worked extensively with a number if industry collaborators, including Microsoft Research Asia (MSRA), Amazon Research, Adobe, Rolls-Royce, and Honda Corporation.
Sohil is focused on solving difficult computer vision problems using large-scale optimization, deep learning, and graphical models. Sohil was previously a systems design engineer at Broadcom Corporation, and works with Intel Research on deep learning and clustering.
Hao’s research interests lie at the intersection of machine learning and systems. Specifically, he is interested in designing efficient and scalable machine learning algorithms for high-performance and resource-constrained systems. Hao is co-advised by Hanan Samet. Before joining UMD, he obtained his master degree from the Chinese Academy of Sciences under the supervision of Prof. Shiguang Shan. He has worked for Nervana Systems, NEC Labs America, and Microsoft Research Asia.