Faculty + Research
Computational Biology and Bioinformatics
(Andrew Barto, Daniel Sheldon, Hava Siegelmann, Ileana Streinu)
Computational Biology refers broadly to the application of mathematical modeling, high-throughput computing, data integration, and algorithm development to generate testable hypotheses about biological entities and processes. Using these approaches, we attempt to answer important questions in molecular biology, genetics, biologically-inspired computation, and neuroscience, such as how a protein folds, how genes are expressed and regulated, how system-level behavior arises from the genetic code, how evolutionary history can inform biological processes, how biological systems are able to process information robustly, and how they learn and adapt to the environment. Our research is fundamentally concerned with efficient approaches to traverse large search spaces, perform inferences over high dimensional data sets, formally integrate diverse biological knowledge, and model biological systems and their behavior. Bioinformatics refers to the data management and processing of biomolecular data often collected on a genome-wide scale. Computational biologists and bioinformaticists typically leverage data generated by modern high-throughput assays including microarrays, mass spectrometry, confocal microscopy, sequencing and other advances in biotechnology.
Autonomous Learning Laboratory
Biologically Inspired Neural & Dynamical Systems Laboratory
Laboratory in Kine(ma)tics and Geometry (LinKaGe)