Christian Borgs

À propos

Christian Borgs is Professor in the Berkeley AI Research Group (opens in new tab) (BAIR) in the EECS department at Berkeley (opens in new tab).

He worked at Microsoft Research for over 22 years, where he started in 1997 as co-founder and co-director of the Theory Group. In 2008, he co-founded Microsoft Research New England (opens in new tab) in Cambridge, Massachusetts, a lab that brings together traditional CS and statistics research with economics as well as qualitative social science research. Borgs was Deputy Managing Director of this lab from 2008 until he left Microsoft for Berkeley in 2020.

Borgs holds a Ph.D. in mathematical physics from the University of Munich (opens in new tab) and a Habilitation in mathematical physics from the Free University in Berlin (opens in new tab). He was the C4 chair of Statistical Mechanics at the University of Leipzig (opens in new tab) before founding the Microsoft Research theory group. Among the honors he has received are the Karl-Scheel Prize (opens in new tab) of the German Physical Society (opens in new tab), and the Heisenberg Fellowship (opens in new tab) of the German Research Council (opens in new tab). Borgs has twice been a member of the Institute for Advanced Study in Princeton (opens in new tab). He is a Fellow of the American Mathematical Society (opens in new tab), and the American Association for the Advancement of Science (opens in new tab). He serves on several editorial boards, prize committees, and governing boards, including the Board of the Institute for Mathematics and its Applications (opens in new tab) (IMA), where he was Chair from 2017 until 2019.

Borgs is author of about 140 research papers and inventor of about 30 patents. His current research focuses on the science of networks, including mathematical foundations, particularly the theory of graph limits (which he co-invented about 15 years ago), graph processes, graph algorithms, and applications of graph theory from economics to systems biology. Borgs is also well known for his earlier work on mathematical statistical physics, including the theory of first-order phase transitions and finite-size effects. He was one of the first to apply methods from mathematical statistical physics to problems in theoretical computer science, including phase transitions in combinatorial optimization, and the study of Markov chains. He has recently begun to work on aspects of responsible AI, from differential privacy to questions of bias in automatic decision making.