About Me (CV): I have been an Assistant Professor at SUTD since 2021. Before that, I did a postdoc from 2020 to 2021 with Prof. Volker Markl (founder of Apache Flink) at TU Berlin. I obtained my PhD and Bachelor’s degrees from NUS (2019) and NTU (2014), respectively. My research emphasizes the design of database systems and big data processing frameworks, with a special interest in high-performance stream processing systems. I regularly serve on the program committees of HPC venues such as SC, ICDCS, and ICPP, and DB/DM venues like ICDE, KDD, and EDBT. I will join SCSE at NTU as an assistant professor in November 2023. I work on Parallel and Distributed Computing, Database & ML systems, and Data-Centric Machine Learning, with a key theme of “event stream processing.”
Important Annoucement (13/Sep/2023): Since announced in early August, we have received few hundres of emails querying PhD positions for 2024 Intake. We are now pleased to announce that we have identified a sufficient number of qualified candidates. These selected applicants have been invited to participate in a visiting student program at our Lab this coming November. Upon successful completion of this program, they are expected to formally join our Lab as PhD students. We would like to express our gratitude to all who have shown interest in our program. Please be advised that we have ceased accepting new PhD applications for this intake cycle, except in the event that any of the already-identified candidates decline our offer. However, we continue to offer opportunities for Research Assistant and visiting student positions, for which the interview process is ongoing. Interested individuals are encouraged to reach out to us via email for additional information.
I lead the IntelliStream Team: We are a systems research group. From a high-level perspective, our research goal is to optimize and employ distributed and parallel stream processing technology to better support existing areas (e.g., databases, big data analytics) and emerging big data applications (e.g., stateful NFV, fast continual learning). This is vital for improving performance and reducing resource consumption, especially in the network-connected world supported by technologies like 5G, IoT, etc.
Selected PublicationsAuthor notations: ∗ denotes the author is a student advised by me. # denotes the author is a staff advised by me.
- SIGMODData Stream Clustering: An In-Depth Empirical StudyProc. ACM Manag. Data Jun 2023
- SIGMODMorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on MulticoresProc. ACM Manag. Data May 2023
- ICDEScalable Online Interval Join on Modern Multicore Processors in OpenMLDBIn 2023 IEEE 39rd International Conference on Data Engineering (ICDE) May 2023
- ICDEParallelizing Stream Compression for IoT Applications on Asymmetric MulticoresIn 2023 IEEE 39rd International Conference on Data Engineering (ICDE) May 2023
- SIGMODParallelizing Intra-Window Join on Multicores: An Experimental StudyIn Proceedings of the 2021 International Conference on Management of Data (SIGMOD) May 2021
- ICDETowards Concurrent Stateful Stream Processing on Multicore ProcessorsIn 2020 IEEE 36th International Conference on Data Engineering (ICDE) May 2020
- SIGMODBriskStream: Scaling Data Stream Processing on Shared-Memory Multicore ArchitecturesIn Proceedings of the 2019 International Conference on Management of Data (SIGMOD) May 2019
- ICDEMulti-Query Optimization for Complex Event Processing in SAP ESPIn 2017 IEEE 33rd International Conference on Data Engineering (ICDE) May 2017
- ICDERevisiting the Design of Data Stream Processing Systems on Multi-Core ProcessorsIn 2017 IEEE 33rd International Conference on Data Engineering (ICDE) May 2017