Devika Subramanian (Ph.D. ’89, Stanford) explores a wide range of research interests, including artificial intelligence and machine learning and their applications in computational systems biology, neuroscience of human learning, assessments of hurricane risks, network analysis of power grids, mortality prediction in cardiology, conflict forecasting and analysis of terrorist networks, and analysis of unstructured text data.
Some of 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).