Ratings & reviews
Creates a collaborative learning environment.
Taught by or worked with Tom?
Add your rating. It takes about 30 seconds.
About Tom
Tom Hayes is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo. He earned his B.A. in Mathematics from Michigan State University in 1993, M.S. in Mathematics from the University of Chicago in 1994, and Ph.D. in Computer Science from the University of Chicago in 2003, with a thesis on rapidly mixing Markov chains for graph colorings. His career includes an NSF Postdoctoral Fellowship at the University of California, Berkeley from 2004 to 2006, Research Assistant Professor at the Toyota Technological Institute at Chicago from 2006 to 2008, and faculty positions at the University of New Mexico starting as Assistant Professor in 2008, advancing to Associate Professor before joining the University at Buffalo in 2022. Hayes has received the NSF Faculty Early Career Development (CAREER) Award from 2012 to 2017, NSF Postdoctoral Fellowship in Mathematical Sciences from 2004 to 2006, Danny Lewin Best Student Paper Award at ACM STOC 2003 for his paper on randomly coloring graphs of girth at least five, and National Science Foundation Graduate Fellowship from 1993 to 1998. He has served on program committees for major conferences including STOC 2012, FOCS 2008, and RANDOM 2007 and 2013.
Hayes's research focuses on theoretical computer science and machine learning, encompassing convergence rates for Markov chains, sampling algorithms for random combinatorial structures, physics of algorithms, distributed algorithms for radio-enabled sensor networks, randomized algorithms, graph colorings, online optimization, and communication complexity. His influential publications include "Stochastic linear optimization under bandit feedback" (COLT 2008, with V. Dani and S.M. Kakade), "The AdWords problem: online keyword matching with budgeted bidders under random permutations" (EC 2009, with N.R. Devanur), "The price of bandit information for online optimization" (NIPS 2007, with V. Dani and S.M. Kakade), "A general lower bound for mixing of single-site dynamics on graphs" (FOCS 2005, with A. Sinclair), and "A simple condition implying rapid mixing of single-site dynamics on spin systems" (FOCS 2006). These works, published in premier venues like FOCS, STOC, and COLT, have garnered significant citations and advanced understanding in algorithmic theory and optimization. Hayes has delivered invited talks such as "Randomized Queueing for Online Matching" at the Information Theory and Applications Workshop in 2012 and "Lifting Markov Chains for Faster Mixing" at Dagstuhl Seminar in 2011.
Trending Computer Science Research & Publication News
AI Research Scooping: OpenAI & Anthropic Priority Disputes
Photo by Brecht Corbeel on Unsplash
arXiv Two-Submission Monthly Cap Explained | AcademicJobs
Photo by Kelly Sikkema on Unsplash
AI-Powered Systematic Review Reproducibility | AcademicJobs
Photo by Igor Omilaev on Unsplash
EOSC Federation: Federated Data Publishing | AcademicJobs
Photo by Haberdoedas on Unsplash
Repository Audit Updates: CoreTrustSeal, ISO 16363
Photo by Zulfugar Karimov on Unsplash
Research Software PID Mandates Expand | AcademicJobs
Photo by Google DeepMind on Unsplash