Research Interests

My research bridges signal processing and data science through designing and developing scalable and interpretable algorithms for mining big multi-aspect data, and applying those algorithms into real-world problems, achieving superior performance, and obtaining valuable insights that can drive scientific discovery.

For more details, check out my Research Statement.

Download my CV



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


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

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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 recently 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 research statement or 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.

An exciting new direction of research in our lab is drawing connections between matrix and tensor decompositions and deep learning. In particular, in recent works (KDD 18 Deep Learning Day, NeurIPS 18 Workshop on Integration of Deep Learning Theories) we show preliminary results on visualizing and characterizing the complexity of a deep convolutional neural network using matrix and tensor decomposition. Stay tuned for more!

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