I am a Postdoc Associate at the Computer Science and Artificial Intelligence Laboratory of MIT since November 2016, working with Prof. Samuel Madden and Prof. Michael Stonebraker. Before that I worked for IBM T.J. Watson Research Center as a Research Staff Member. I received my Ph.D. degree in Computer Science from Worcester Polytechnic Institute, supervised by Prof. Elke Rundensteiner. I have conducted research in the broad areas of data sicence and systems ranging from the low-level core database performance optimization to designing the high level, application specific machinelearning techniques. My recent research falls in the emerging area of "Systems for AI and AI for Systems", focused on designing scalable algorithms and systems for the data scientists to effectively yet efficiently explore and discover knowledge from heterogeneous data sources -- especially anomalies.

[CV] [Google Scholar]

Research Interests

  • Anomaly Detection Algorithms and Systems
  • Scalable AI Systems
  • Big Data Analytics on Parallel and Distributed Platforms
  • Streaming Data Management and Analytics
  • Performance optimization of core database

News

  • 2019-11, paper "Continuously Adaptive Similarity Search" was accepted by SIGMOD 2020.
  • 2019-07, the project "Outlier Discovery Paradigm" was granted by NSF IIS. This is based on my PhD dissertation and recent research.
  • 2019-05, invited to the PC of CIKM 2019, ICDE 2020.
  • 2019-05, paper "Smile: A System to Support Machine Learning on EEG Data at Scale" was accepted by VLDB 2019 Industrial track.
  • 2019-03, paper "Efficient Discovery of Sequence Outlier Patterns" was accepted by VLDB 2019.
  • 2019-02, invited to the PC of VLDB 2020.
  • 2018-12, serving as PC members of SIGKDD, VLDB Demo 2019.
  • 2018-11, "SWIFT: Mining Representative Patterns from Large Event Streams" was accepted by VLDB 2019.
  • 2018-04, serving as PC members of SIGMOD/ICDE/DASFAA 2019, CIKM 2018.
  • 2016-11, I started my Postdoc in CSAIL at MIT, working with Prof. Samuel Madden.