Distinguished Thought Leader Series

Devika Subramanian, Ph.D. is Professor of Computer Science and Electrical and Computer Engineering, Rice University. She focuses on statistical machine learning, artificial intelligence, computational biology, datamining, adaptive compilation, computational neuroscience, cognitive science, and mobile robotics.

Devika's research is aimed at the design and analysis of resource-bounded systems that adapt and learn from experience. With Stuart Russell of UC Berkeley, she wrote the first paper defining the area of bounded optimality, i.e., what it means for an agent to make the "best" use of scarce resources. Her work centers onseveral applications designed to push the science of adaptive systems.

Her current projects are in three main areas: computational biology, conflict forecasting, and adaptive compilers.

With scientists at the M.D. Anderson Cancer Center and Baylor College of Medicine, she is reverse-engineering metabolic networks from gene expression data in cancer cells, reconstructing signal transduction networks in granulocyte differentiation in AML and CML from flow cytometry data, and identifying multi-locus genetic markers from genotype-phenotype association data.

With support from an NSF ITR, and in collaboration with Dr. Richard Stoll of Rice University, she is developing a system for predicting outbreaks of conflict in the Middle East based on temporal analysis of newswire stories from the region.

With support from an NSF ITR and in collaboration with Professors Cooper and Torczon of Rice University's Scalar Compiler Group, she is designing learning algorithms that help compilers customize their optimization strategies to specific programs.

Her past projects include: designing an adaptive outdoor tour guide for the Rice campus (funded by Rice Engineering), reinforcement learning for non-stationary environments and applications to network routing (funded by Southwestern Bell), designing adaptive control systems for the Mars Bioplex (funded by NASA), designing experimentation strategies for protein crystallography (funded by NIH), adaptive compilers for power-sensitive applications (funded by Darpa and the Texas Advanced Technology Program), automating the conceptual design of opto-mechanical systems from specifications of behavior (funded by NSF), and dynamically learning models of humans acquiring a complex visualmotor task (funded by ONR).

Devika coauthored Computational methods for learning bayesian networks from high-throughput biological data, Predicting altered pathways using extendable scaffolds, Statistical methods for the objective design of screening procedures for macromolecular crystallization, The relevance of relevance, An overview of current research on knowledge compilation and speed-up learning, Representational issues in machine learning, Subjective Ontologies, Making Situation Calculus Indexical, A Comparison of Action Selection Learning Methods, and A multi-strategy learning scheme for knowledge assimilation in embedded agents.

Devika earned her B.Tech in Computer Science and Engineering at the Indian Institute of Technology, Kharagpur, India in 1982. She earned her M.S. in Computer Science at Stanford University in 1984, and she earned her Ph.D. in Computer Science at Stanford University in 1989 with the thesis A Theory of Justified Reformulations.