Raghuram Ramanujan

Home * People * Raghuram Ramanujan



Raghuram Ramanujan, an Indian American electrical engineer, computer scientist and assistant professor at Davidson College, Davidson, North Carolina. He holds a degree in electrical and computer engineering from Purdue University, and a M.Sc. and Ph.D. in CS, 2012 from Cornell University. His research interests span multiple areas of artificial intelligence, including automated planning, combinatorial search, and machine learning.

=UCT= Along with Ashish Sabharwal, and Bart Selman, Raghuram Ramanujan researched on Upper Confidence bounds applied to Trees (UCT). Quote from their abstract On Adversarial Search Spaces and Sampling-Based Planning : UCT has been shown to outperform traditional minimax based approaches in several challenging domains such as Go and Kriegspiel, although minimax search still prevails in other domains such as Chess. This work provides insights into the properties of adversarial search spaces that play a key role in the success or failure of UCT and similar sampling-based approaches. We show that certain "early loss" or "shallow trap" configurations, while unlikely in Go, occur surprisingly often in games like Chess (even in grandmaster games). We provide evidence that UCT, unlike minimax search, is unable to identify such traps in Chess and spends a great deal of time exploring much deeper game play than needed.

=Selected Publications=
 * Raghuram Ramanujan, Ashish Sabharwal, Bart Selman (2010). On Adversarial Search Spaces and Sampling-Based Planning. ICAPS 2010
 * Raghuram Ramanujan, Bart Selman (2011). Trade-Offs in Sampling-Based Adversarial Planning. ICAPS 2011, best paper, VideoLecture
 * Raghuram Ramanujan, Ashish Sabharwal, Bart Selman (2012). Understanding Sampling Style Adversarial Search Methods. arXiv:1203.4011
 * Erol Cromwell, Jonah Galeota-Sprung, Raghuram Ramanujan (2015). Computational Creativity in the Culinary Arts. 28th FLAIRS Conference

=External Links=
 * Raghuram Ramanujan - Davidson College
 * Trade-Offs in Sampling-Based Adversarial Planning - VideoLectures.NET

=References=

Up one level