Academic Profile : No longer with NTU

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Asst Prof Arijit Khan
Assistant Professor, School of Computer Science and Engineering
External Links
Arijit Khan is an assistant professor (tenure-track) in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He earned his PhD from the Department of Computer Science, University of California, Santa Barbara, USA, and did a post-doc in the Systems group at ETH Zurich, Switzerland. Arijit is the recipient of the prestigious IBM PhD Fellowship in 2012-13. He published more than 50 papers in premier databases and data mining conferences and journals including ACM SIGMOD, VLDB, IEEE TKDE, IEEE ICDE, SIAM SDM, USENIX ATC, EDBT, The Web Conference (WWW), and ACM CIKM. Arijit co-presented tutorials on emerging graph queries and big graph systems at IEEE ICDE 2012, and at VLDB (2017, 2015, and 2014). He served in the program committee of ACM KDD, ACM SIGMOD, VLDB, IEEE ICDE, IEEE ICDM, EDBT, ACM CIKM, and in the senior program committee of WWW. Arijit served as the co-chair of Big-O(Q) workshop co-located with VLDB 2015, wrote a book on uncertain graphs in Morgan & Claypool’s Synthesis Lectures on Data Management. He contributed invited chapters and articles on big graphs querying and mining in the ACM SIGMOD blog, Springer Handbook of Big Data Technologies, and in Springer Encyclopedia of Big Data Technologies. He was invited to give tutorials and talks across 10 countries, including in the National Institute of Informatics(NII) Shonan Meeting on "Graph Database Systems: Bridging Theory, Practice, and Engineering", 2018, Japan, Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data (APWebWAIM 2017), International Conference on Management of Data (COMAD 2016), and in the Dagstuhl Seminar on graph algorithms and systems, 2014 and 2019, Schloss Dagstuhl - Leibniz Center for Informatics, Germany. Dr Khan is serving as an associate editor of IEEE TKDE 2019-21 and the proceedings chair of EDBT 2020.

Web Home Page:
Big-graphs management and analytics, with a focus on user-friendly, online querying and pattern mining in social and information networks, using scalable algorithms and machine learning techniques.

keywords: big-graphs, big-data, graph-systems, knowledge graphs, uncertain graphs, graph streams, databases, data mining, machine learning, algorithms.
  • Human-AI Collaboration for User-Guided Complex Networks Querying and Exploration
  • Machine Learning and Data Mining over Dynamic and Stream Networks