Themis Gouleakis

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@ntu.edu.sg
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About | Publications | Awards

Themis Gouleakis

I am an Assistant Professor in the College of Computing and Data Science at Nanyang Technological University.

Research interests

My research focuses on the design and analysis of algorithms for big data. I have a wide range of research interests within this area including testing properties of probability distributions and sublinear-time algorithms. I am also interested in analysing sublinear time algorithms using new models of computation that better capture the ability to query real world data and the goals of dealing with noisy data in certain real world applications. More recently, I have also been working on distributed algorithms, local computation algorithms and learning-augmented online algorithms.

  • Sublinear-time algorithms and property testing
    Sublinear algorithms exploit the structure of the specific problem at hand and are able to get an approximate answer in sublinear time using clever random sampling. One of the settings I am particularly interested in is learning and testing properties of distributions over large domains. In this setting, the aim is to design algorithms that use only a small (usually sublinear in the domain size) number of samples from an unknown distribution and aim to perform various tasks, such as density estimation, parameter estimation or testing membership in a particular class of distributions.

  • Computational learning theory
    I am particularly interested in robust learning algorithms to perform learning tasks in the presence of noise or corrupted data with applications to machine learning and artificial intelligence.

  • Learning-augmented algorithms
    The enormous success of the field of machine learning in recent years has the potential to benefit other areas of computer science such as online algorithms, where the goal is to exploit the ability of machine learning algorithms to make predictions of future input to the algorithm while obtaining worst case guarrantees for such algorithms. I am also interested in extending this concept of machine learned advice beyond online algorithms.

  • Distributed graph algorithms
    I am interested in distributed graph algorithms under various distributed models of computation, such as LOCAL, CONGEST, MPC and their variants or combinations.

Education and research experience

Previously, I have worked as a postdoctoral researcher at the University of Southern California supervised by Ilias Diakonikolas and as a postdoctoral fellow at the Algorithms & complexity department of Max-Planck-Institute for informatics and as a senior research fellow at National University of Singapore, supervised by Arnab Bhattacharyya and Vincent Y. F. Tan .

I completed my Ph.D. at MIT affiliated with the Theory of Computation group in CSAIL and advised by Ronitt Rubinfeld.

I was a co-organizer of the NUS AlgoTheory Seminar (2022-2023) and (2023-2024).

Journal Publications

Conference Proceedings

Manuscripts

Teaching

Awards and Honors

  • Out­stand­ing Pa­per Award: 33rd An­nual Con­fer­ence on Neural In­for­ma­tion Pro­cess­ing Sys­tems, 2019
  • Onassis Foundation Scholarship (2012-2015): Scholarship for doctoral studies.
  • Paris Kanellakis Fellowship (2012-2013): Fellowship for first year MIT EECS students.