Manos Papagelis

Assistant Professor

Scholarly Interests
  • Graph & Information Network Mining
  • (Big) Data Mining & Analysis
  • Databases & Knowledge Discovery
  • Recommendation Algorithms, Trust & Personalization
  • City Science/ Urban Informatics
Bio

Manos Papagelis is an Assistant Professor of Electrical Engineering  and Computer Science (EECS) at York University, Toronto, Canada. His  research falls is in the area of data science, with particular
interest in graph and information network mining, (big) data mining  and analysis, databases and knowledge discovery, recommendation algorithms & personalization, city science/urban informatics. Manos  holds a Ph.D. in Computer Science from the University of Toronto,  Canada, and a M.Sc. and a B.Sc. in Computer Science from the  University of Crete, Greece. Before joining York University, he was a
postdoctoral research scholar at the University of California,  Berkeley. In the past, he has worked at Yahoo! Labs, Barcelona as a  research intern and at the Institute of Computer Science, FORTH,  Greece as a research fellow. His research has appeared in ACM trans.  on knowledge discovery from data (ACM TKDD) and IEEE Trans. on  knowledge and data engineering (IEEE TKDE), he has filed three U.S.
patent applications and is the software architect of two large-scale  online systems – a conference management system and a system for  socio-technical analysis of green buildings. He has taught at the
University of California, Berkeley and at the University of Toronto,  Canada.

Selected Publications
  • Papagelis, M. (2015). Refining social graph connectivity via
    shortcut edge addition. ACM Transactions on Knowledge Discovery from
    Data (ACM TKDD), 10(2), 12.
  • Papagelis, M., Das, G., & Koudas, N. (2013). Sampling online social
    networks. IEEE Transactions on knowledge and data engineering (IEEE
    TKDE), 25(3), 662-676.
  • Doerr, M., & Papagelis, M. (2007). A method for estimating the
    precision of placename matching. IEEE transactions on knowledge and
    data engineering (IEEE TKDE), 19(8), 1089-1101.
Staff Information
Campus Address
Lassonde Building, Room 3050
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