Rate My Professor K. Subramani

K. Subramani is a Professor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University, part of the Statler College of Engineering and Mineral Resources. He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2000), an M.S. in Computer Science from the same institution (2000), an M.Tech. in Computer Science and Engineering from the Indian Institute of Technology Bombay (1993), and a B.Tech. in Computer Science and Engineering from Regional Engineering College, Calicut (1991). Prior to his academic career, Subramani worked as a database designer at Larsen & Toubro from January to July 1993. He then served as an algorithm designer at Glaxo-Wellcome, focusing on graph isomorphism problems, followed by a position as a VLSI engineer at Cadence Design Systems.

Subramani's research centers on theoretical computer science, with interests in algorithms, scheduling, computational complexity, polyhedral combinatorics, and computational biology. His recent work includes dataless neural networks, which has received funding from DARPA. He has authored numerous publications, including 'A faster algorithm for the single source shortest path problem with few distinct positive lengths' (JB Orlin, K Madduri, K Subramani, M Williamson, 2010), 'Resource-optimal scheduling using priced timed automata' (JI Rasmussen, KG Larsen, K Subramani, 2004), 'On partial vertex cover and budgeted maximum coverage problems in bipartite graphs' (B Caskurlu, V Mkrtchyan, O Parekh, K Subramani, 2014), 'Optimal length resolution refutations of difference constraint systems' (K Subramani, 2009), 'A combinatorial certifying algorithm for linear feasibility in UTVPI constraints' (K Subramani, P Wojciechowski, 2017), and 'A combinatorial algorithm for Horn programs' (R Chandrasekaran, K Subramani, 2013). His scholarship has garnered over 1,285 citations according to Google Scholar. Subramani teaches courses in algorithms and automata theory and supervises Ph.D. students in related areas.