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Research Interests

My research broadly spans the areas of data science, signal processing, machine learning, and artificial intelligence. The overarching theme of my work has been the design and development of models and algorithms that can extract actionable and interpretable insights from multi-aspect/multi-modal data, typically with very little or no supervision. A major focus of my research has been on the development and advancement of tensor methods and their applications in high-impact real-world problems, including misinformation detection on the web, graph and social network analytics and mining, explainable AI, and detection of gravitational waves.

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Contact

Email

  • epapalexcs dot ucr dot edu
  • vagelis.papalexakisgmail dot com

Location

3132 Multidisciplinary Research Building
Computer Science & Engineering Department
University of California Riverside
900 University Ave
Riverside, CA 92521
USA

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About me

Hello there! My name is Vagelis Papalexakis (my 'official' name is Evangelos, but nobody really calls me that) and I come from the beautiful city of Athens, Greece. I received my Electronic & Computer Engineering Diploma and M.Sc at the Technical University of Crete in Chania, under the supervision of Professor Nikos Sidiropoulos. I received my Ph.D from the Computer Science Department of Carnegie Mellon University under the supervision of Professor Christos Faloutsos.

Currently, I am an Assistant Professor at the Computer Science & Engineering Department at the University of California Riverside, working broadly on data mining, machine learning, and data science research. You can get a quick idea about my research by browsing through my list of publications.

A major focus of my research is tensor decompositions for machine learning and data science. In order to get a better idea of this research area, feel free to check out two relevant recent surveys that I have co-authored: one geared more towards data science practitioners and one geared more towards the fundamental concepts necessary to start algorithmic research in the area.


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