Arya Mazumdar

Arya Mazumdar's research in error-correcting codes has produced the first efficiently implementable family of codes over permutations, as well as the best-known fundamental limits of locally repairable codes for distributed storage. Mazumdar's current research interests include recent advances in statistical machine learning in the application domains of interactive learning algorithms, statistical reconstructions, community detection, and distributed optimization. He is also very interested in fundamental problems that involve trade-offs between communication and computation in distributed settings. 

Before coming to the Manning College of Information and Computer Science (CICS) at the University of Massachusetts Amherst, Mazumdar was an assistant professor at the University of Minnesota. He received his PhD degree from the University of Maryland, College Park (2011), after which he was a postdoctoral scholar at the Massachusetts Institute of Technology (2011-2012). Mazumdar has spent time in tech industries in the past, including in Amazon AI and Search, HP Labs, and IBM Almaden Research Center. 

Mazumdar is a recipient of the 2015 NSF CAREER award, the 2020 EURASIP JASP Best Paper Award, and the 2010 IEEE ISIT Jack K. Wolf Paper Award. He is also a recipient of the 2011 Distinguished Dissertation Fellowship Award from the University of Maryland. His works have regularly appeared in the top venues of information theory and machine learning, multiple times as spotlight papers. He is an associate editor of IEEE Transactions on Information Theory and an area editor of Now Publishers Foundation and the Trends series.