Machine Learning Instructors


Chris Jermaine – Professor of Computer Science, Director of Data Science

Chris Jermaine studies data analytics: how to analyze, store, retrieve, and manipulate large and heterogeneous data sets. Within this problem space, most of my work focuses on:  the systems-oriented problems that arise when building software to manage large and diverse data sets; and the difficulties that arise when applying statistical methods to such data sets.

I received a BA from the Mathematics Department at UCSD, an MSc from the Computer Science and Engineering Department at OSU (my advisor at OSU was Renee Miller, who is now at Toronto), and a PhD from the College of Computing at Georgia Tech (my advisor at Georgia Tech was Ed Omiecinski). I am the recipient of a 2008 Alfred P. Sloan Foundation Research Fellowship, a National Science Foundation CAREER award, a 2007 ACM SIGMOD Best Paper Award, a 2009 ACM SIGKDD Best Paper runner up, and a 2017 ICDE Best Paper Award. I have been at Rice since January, 2009, and I was on the faculty of the computer science department at the University of Florida from 2002, through August, 2010.

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Anastasios Kyrillidis – Noah Harding Assistant Professor, Assistant Professor, Computer Science and Electrical & Computer Engineering

Tasos Kyrillidis’ research interests include (but not limited to): Optimization for machine learning, Convex and non-convex algorithms and analysis, Large-scale optimization and Any problem that includes a math-driven criterion, and requires an efficient method for its solution.

I am a Noah Harding Assistant Professor at the Computer Science department at Rice University. Prior to that, I was a Goldstine PostDoctoral Fellow at IBM T. J. Watson Research Center (NY), and a Simons Foundation PostDoc member at the University of Texas at Austin. I finished my PhD at the CS Department of EPFL (Switzerland).

My research interests include (but not limited to):

  • Optimization for machine learning
  • Convex and non-convex algorithms and analysis
  • Large-scale optimization
  • Any problem that includes a math-driven criterion, and requires an efficient method for its solution.

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Santiago Segarra – Assistant Professor, Computer Science and Electrical & Computer Engineering

Santiago’s research areas include: Data Science for Networks. Modeling, analysis, and design of networked systems. Signal processing, machine learning, optimization, and algebraic topology applied to the understanding of networks and network data.

Other topics of interest include clustering in social and technological networks, authorship attribution problems, abstract representations of network data structures, networks in Neuroscience, linear and nonlinear network dynamics, social networks and team dynamics, and processing of signals defined on graphs

Santiago received the B.Sc. degree in industrial engineering with highest honors (Valedictorian) from the Instituto Tecnológico de Buenos Aires (ITBA), Argentina, in 2011, the M.S. in electrical engineering from the University of Pennsylvania (Penn), Philadelphia, in 2014 and the Ph.D. degree in electrical and systems engineering from Penn in 2016. From September 2016 to June 2018 he was a postdoctoral research associate with the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. Since July 2018, Dr. Segarra is an Assistant Professor in the Department of Electrical and Computer Engineering at Rice University. His research interests include network theory, data analysis, machine learning, and graph signal processing.

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