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Professor Timos Sellis

Director of Swinburne Data Science Research Institute
PhD in Computer Science, University of California, Berkeley, United States; MSc in Computer Science, Harvard University, United States; Diploma in Electrical Enginerring, National Technical Univ. of Athens, Greece

Biography

Professor Timos Sellis is Director of Swinburne’s Data Science Research Institute. His research interests include big data, data streams, personalisation, data integration, and spatio- temporal database systems.

Professor Sellis was elected IEEE Fellow in 2009 for his contributions to database query optimisation and spatial data management, and ACM Fellow in 2013 for his contributions to database query optimisation, spatial data management and data warehousing. In 2018 he was awarded the IEEE TCDE Impact Award, in recognition of his impact in the field and for contributions to database systems research and broadening the reach of data engineering research.

Up until 2012 Professor Sellis was Director of the Institute for the Management of Information Systems and a Professor at the National Technical University of Athens. He holds an MSc degree from Harvard University, a PhD from the University of California at Berkeley, and has served as president of the National Council for Research and Technology of Greece.

Research interests

Database Systems; Data Science; Big Data

PhD candidate and honours supervision

Higher degrees by research

Accredited to supervise Masters & Doctoral students as Principal Supervisor.

Fields of Research

  • Database Management - 080604
  • Global Information Systems - 080606

Teaching areas

Database Systems

Awards

  • 2018, International, Impact Award, IEEE TCDE
  • 2013, International, ACM Fellow, ACM
  • 2009, International, IEEE Fellow, IEEE

Publications

Also published as: Sellis, Timos; Sellis, T.; Sellis, Timoleon
This publication listing is provided by Swinburne Research Bank. If you are the owner of this profile, contact us to update.

Recent research grants awarded

  • 2019: Examining a new method for relapse prediction in schizophrenia and bipolar disorder *; Jack Brockhoff Foundation Fund Scheme
  • 2018: Identifying technological trajectories using machine learning algorithms *; ARC Linkage Projects Scheme
  • 2018: Linked semantic platforms for social & physical infrastructure & wellbeing *; ARC Linkage Infrastructure and Equipment Scheme
  • 2017: Trajectory Data Processing - Spatial Computing meets Information Retrieval *; ARC Discovery Projects Scheme
  • 2016: Effective and Efficient Query Processing over Dynamic Social Networks *; ARC Discovery Projects Scheme
  • 2016: Sonepar Data and Predictive Analytics *; Sonepar Asia Pacific Ltd
  • 2014: Efficient and effective ad-hoc search using structured and unstructured geospatial information *; Aarhus University, Denmark

* Chief Investigator


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