Skip to main content

Papailiopoulos, Lee paper tops highly cited list for influential journal

Written By: Jason Daley

A paper authored by Kangwook Lee and Dimitris Papailiopoulos, both assistant professors in the Department of Electrical and Computer Engineering, was recently ranked as the most cited in the influential journal IEEE Transactions of InfoTheory between 2016 and 2020. According to data released by Google Scholars “Scholar Metrics,” the paper “Speeding up distributed machine learning using codes,” which appeared in the March 2018 issue of the journal, was cited 525 times during the period.

In 2020, the same paper won the 2020 Joint Communications Society/Information Theory Society Paper Award.

“Why did it get cited many times? It introduced an innovative way of speeding up distributed machine learning (research topics studied in machine learning and distributed systems research) using codes,” explains Lee. “This opened up a new interdisciplinary research topic across these fields and ignited a lot of interesting follow-up research ideas.”

Paper co-authors include Maximilian Lam and Kannan Ramchandran of the University of California at Berkeley and Ramtin Pedarsani of the University of California at Santa Barbara.