Eric Vigoda

Eric Vigoda is a Professor of Computer Science at the University of California, Santa Barbara (UCSB). Previously, he held positions at Georgia Institute of Technology, including Director of the Algorithms and Randomness Center (2016–2019). He earned his PhD in Computer Science from UC Berkeley (1999) and BS/MS from Johns Hopkins University (1994). His research focuses on Markov Chain Monte Carlo (MCMC) algorithms, phase transitions in statistical physics, and randomized algorithms for approximate counting/sampling problems. He has authored over 100 papers and has been recognized with awards such as the Fulkerson Prize (2006) and the Machtey Award (1999).

Education: PhD (UC Berkeley, 1999), MS/BS (Johns Hopkins, 1994). Research interests include MCMC methods, randomized algorithms, and applications in evolutionary biology. His work bridges theoretical computer science and statistical physics, particularly in analyzing mixing times of Markov chains.

Awards: American Mathematical Society Fellow (2019), Fulkerson Prize (2006), Machtey Award (1999), William A. Baird Teaching Award (Georgia Tech, 2019). Supervised 6 PhD students, including Zongchen Chen (MIT Postdoc), Andreas Galanis (Oxford), and Linji Yang (Entrepreneur).

Teaching: Current courses include CS 130A (Data Structures and Graph Algorithms) and CS 190A (Randomized Algorithms). Former courses include Markov Chains in Evolutionary Biology and a MOOC on Graduate Algorithms for Georgia Tech's OMSCS program.

Contributions: Organized the 2022 Summer School on 'Spectral Independence and Entropy Decay' at UCSB. Collaborated with researchers on topics like high-dimensional expanders and phase transitions in sampling algorithms.