My current research interests span the broader area of machine learning, ranging from Spatio-temporal Abstractions in Reinforcement Learning to social network analysis and Data/Text Mining. Much of the work in my group is directed toward understanding interactions and learning from them.

Several people have asked me what background is needed to start working in machine learning. Here is an attempt at building a list by our group. Wiki Page.

Reinforcement Learning

One of the main focus of research in our group is on building situated learning agents that can incrementally solve larger and larger problems using structures built from prior experience. We look at this both from a spatial and temporal perspective, with motivations drawn from cognitive theories of representation.

From a spatial, or a representation, perspective we want agents to be able to incorporate only those aspects of their enivornment that are crucial for the task at hand. We build on the notion of MDP homomorphisms proposed in my thesis and have explored various extensions to this basic framework [RB 2004, RBM 2007, NR 2007, NR 2008].

These architectures work with an already abstract representation of the world. Closer to the sensory level we are looking at the learning of visual routines that can tell an agent what aspects of its visual input to focus on. These visual routines then act as the feature extraction units that the higher levels in the architecture choose from. This work is part of a joint project with Profs. Jeremy Wyatt and Richard Dearden from University of Birmingham and Dr. Anurag Mittal and myself, from this Department, funded by the British Council under the UKIERI program.

From a temporal perspective we have addressed issues of representations and sub-tasks - the question of what is an adequate representation for the sub-task at hand [RB 2003b, RB 2003c, RBM 2007]. We are currently working on using cognitively motivated representation schemes for hierarchical task architectures. On the question of building temporal hierarchical architectures, we are exploring issues regarding incremental learning of the hierarchy, combining learning and planning; and looking at more cognitively motivated representations of tasks and sub-tasks.

We are also exploring the realizations of these algorithms on real-robot platforms. This requires us to address issues that are closer to the sensory and motor control level, including visual cognition, and other localization mechanisms [BRK 2009]. We are also looking at transfer of learning from one robot to another under several constrained settings as well as the problem of learning from instructions.

Text Mining

Our group has been looking at the marriage of both semantic and statistical approaches to text mining tasks. Much of the work in the group has been driven by specific problems. We have worked on the problem of text summarization [SRR2006a, 2008ARb, 2008ARa] categorization [SRR2008, SRR2006b], clustering [JDR2008, JDRS2007], etc. Some of the current projects are focused on (auto | financial | micro) blog analysis (part of the work is funded by General Motors, India Science Labs), resume processing (funded by Burning Glass Technologies), representative document set mining, question answering systems, etc.

Data Mining

My interest in data mining has been motivated partly by my desire to understand the use of statistical models in mining, partly by my drive for doing something of immediate relevance, and partly by the connections between my work on abstraction and some of the problems in mining. One of the ongoing projects is on opthalmic data mining with Sankara Nethralaya^ [RKR2008, CR2008]. We work closely with doctors from their Vision Research Foundation on building better screening tools, disease incidence prediction, risk factor analysis, etc.

We are also looking at telecom data analysis, funded by Ericsson R & D, India. The problems we focus on is trying to understand customer behavior better through various analytics tools, including social network analysis [KR2009, AR2010].


These are wonderful people from outside my Department that I am collaboarting with currently or have done so in the recent past, and hope to continue working with in the future.

  • Karthik V. Aadithya, University of California, Berkeley
  • Andrew G. Barto, University of Massachusetts, Amherst
  • Dipti Deodhare, Center for Artificial Intelligence and Robotics
  • K. Madhava Krishna, IIIT Hyderabad
  • Suril Shah, IIIT Hyderabad
  • Sriraam Natarajan, Indiana University, Bloomington, USA
  • Jeremy Wyatt, University of Birmingham, UK
  • Srinivasan Parthasarathy, Ohio State, USA
  • Koshy Verghese, CE, IIT Madras
  • V. Srinivasa Chakravarthy, BT, IIT Madras
  • Karthik Raman, BT, IIT Madras
  • Nandan Sudarsanam, DoMS, IIT Madras
  • Ramasuri Narayanam, IBM Research, Bengaluru

Awards and Service

  • Won a UKIERI funded project in 2009, jointly with Jeremey Wyatt, Richard Dearden from University of Birmingham and Anurag Mittal, IIT Madras
  • KLA Tencor Facutly Research Engagement Grant, 2014, 2015
  • Yahoo! Faculty Research and Engagement Gift, 2009, 2014
  • Rachit Arora, best student paper award, AND 2008
  • Sanjukta Roy (CMI), best student paper award, CoDS 2015