Short Bio

Alex Dimakis is a UT Austin Professor and the co-director of the National AI Institute on the Foundations of Machine Learning. He received his Ph.D. from UC Berkeley and the Diploma degree from NTU in Athens, Greece. He has published more than 150 papers and received several awards including the James Massey Award, NSF Career, a Google research award, the UC Berkeley Eli Jury dissertation award, and several best paper awards. He served as an Associate Editor for several journals, as an Area Chair for major Machine Learning conferences (NeurIPS, ICML, AAAI) and as the chair of the Technical Committee for MLSys 2021. He is an IEEE Fellow for contributions to distributed coding and learning. His research interests include Information Theory and Machine Learning.

Curriculum vitae with full list of publications.

Technical Program Committees

  • AAAI (Area chair), NeurIPS (Area Chair), ICML (Area chair), ICLR, AISTATS, ISIT, MLSys (TPC chair)

  • IEEE Journal on Selected Areas in Information Theory (JSAIT) Lead editor for the Inaugural issue.

  • Associate editor, IEEE Transactions on Information Theory (2014-2019)

  • Associate editor, IEEE Signal Processing (2012-2015)

Awards and Selected presentations

  • University of Toronto Department of ECE, Distinguished Lecture, 2019

  • Information Theory and Applications (ITA) Invited Plenary Speaker, 2019

  • 2018 James Massey Award

  • CISS 2018 Invited Plenary speaker, March 2018.

  • Communication Theory Workshop (CTW) Plenary Speaker, May 2015

  • IEEE Distinguished Lecturer (Information Theory Society), 2015.

  • ARO Young Investigator Award, 2014.

  • Communications Society and Information Theory Society Joint Paper Award, 2012

  • Google Research Faculty award

  • IEEE Netcode Keynote speaker, 2010

  • IEEE ComSoc Data Storage Committee Best Paper Award, 2010

  • NSF Career Award, 2011.

  • 2008 Eli Jury Dissertation Award.

  • Microsoft Research Fellowship for 2007-2008.

  • Best Paper award in IEEE/ACM Symposium on Information Processing in Sensor Networks (IPSN ’05).