Dr. Arijit Khan

Based in Aalborg, Denmark

Bio:

Arijit Khan is an IEEE senior member and an associate professor in the Department of Computer Science, Aalborg University, Denmark. 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. He has been an assistant professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. Arijit is the recipient of the prestigious IBM PhD Fellowship in 2012-13. He published more than 70 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), ACM WSDM, and ACM CIKM. Arijit co-presented tutorials on emerging graph queries, applications, and big graph systems at VLDB (2017, 2015, and 2014), ACM CIKM (2022), and at IEEE ICDE 2012. 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 (APWeb-WAIM 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-now, proceedings chair of EDBT 2020, and IEEE ICDE TKDE poster track co-chair 2023.

Available Lectures

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Data Management for Emerging Problems in Large Networks
Graphs are widely used in many application domains, including social networks, knowledge graphs, biological networks, software collaboration, geo-spatial road networks, interactive gaming, among...
Graph-based Management and Mining of Ethereum Blockchain Data

Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous...
On Uncertain Graphs Modeling and Queries
Uncertainty is evident in graph data due to a variety of reasons, such as noisy measurements, inconsistent, incorrect, and possibly ambiguous information sources, lack of precise...

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