Professor of Computing and Mathematical Sciences; Director, Information Science and Technology
Adam Wierman's research interests center around resource allocation and scheduling decisions in computer systems and services. More specifically, his work focuses both on developing analytic techniques in stochastic modeling, optimization, machine learning, and game theory, and applying these techniques to application domains such as energy-efficient computing, the cloud, the smart grid, and social networks.
Professor of Computer Science, Computation and Neural Systems, and Bioengineering
Professor Winfree's research involves theoretical and experimental aspects of molecular programming. Models of computation are developed that incorporate essential features of molecular folding, molecular self-assembly, biochemical circuits, and molecular robotics. These models are studied to determine their expressiveness for programming molecular-level tasks including decision-making, memory, behavior, and morphogenesis. Methods for compiling abstract molecular programs into actual molecules are developed and tested in the laboratory.
Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering; Investigator, Heritage Medical Research Institute
Professor Yang's research area is biophotonics—the imaging and extraction of information from biological targets through the use of light. His research efforts can be categorized into two major groups: chip-scale microscopy imaging and time-reversal based optical imaging.
Martin and Eileen Summerfield Professor of Applied Physics and Electrical Engineering
Professor Amnon Yariv's research focuses on the theoretical and technological underpinning of optical communication. Present projects include: new types of semiconductor lasers, optical phase-lock systems and coherent photonics, hybrid Si/III-V devices for lasers, detectors and modulation, "Slow" light propagation in artificial periodic dielectric waveguides.
Professor of Computing and Mathematical Sciences
Yisong Yue's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured machine learning. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, clinical therapy, tutoring systems, data-driven animation, behavior analysis, sports analytics, experiment design for science, learning to optimize, policy learning in robotics, and adaptive planning & allocation problems.