Bonnie Berger is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), with a joint appointment in Computer Science in EECS. She is also head of the Computation and Biology group and member of the Theory of Computation group at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). After beginning her career working in algorithms at MIT, she was one of the pioneer researchers in the area of computational molecular biology and, together with the many students she has mentored, has been instrumental in defining the field. She has won numerous awards and honors including: member of the American Academy of Arts and Sciences, the NIH Margaret Pittman Director's Lecture Award, Biophysical Society's Dayhoff Award, inclusion in Technology Review Magazine's inaugural TR100 as a top young innovator, ACM Fellow, ISCB Fellow, RECOMB Test of Time Award, NSF Career Award, as well as, recently, Honorary Doctorate from EPFL and elected to American Institute of Medical and Biological Engineering (AIMBE) College of Fellows. She currently serves as Vice President of ISCB, as Head of the steering committee for RECOMB, and on the NIGMS Advisory Council.
We aim to develop a context-aware data-driven functional genomics framework that can characterize tissue-specific gene representations, provide context-aware genotype to phenotype mapping, and enable network-based exploration of disease genetics.
This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.
The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.
Our lab focuses on designing algorithms to gain biological insights from advances in automated data collection and the subsequent large data sets drawn from them.
Theory research at CSAIL covers a broad spectrum of topics, including algorithms, complexity theory, cryptography, distributed systems, parallel computing and quantum computing.
