Our group focuses on the design and optimization of high-performance stream processing systems. We are dedicated to advancing the state of the art in various domains, including streaming databases, stream machine learning, stream data mining, and stream IoT. By leveraging cutting-edge techniques in concurrency control, adaptive scheduling, and data compression, we aim to address the scalability and efficiency challenges posed by real-time data streams. Our research contributions have been recognized in top-tier conferences and journals, reflecting our commitment to innovation and excellence in the field of stream data processing. If you like to join our team, please first answer a few questions in questionnaire.

[New] We have one joint PhD scholarship available (NTU and Shanghai Jiaotong University). Details can be found at the link. If you are interested, please drop me an email with your CV and publications if any.

Streaming Database

Stream Processing Engine

Stream Window Join

Stream Transactions

Stream Machine Learning/LLM, RAG

Stream Data Mining

Stream IoT

click to see our other ancillary topics

At any time, we have a limited number of slots available. Unfortunately, we cannot respond to every message we receive. To help manage the process, we are using the questionnaire. We guarantee we will read the responses to this form but cannot guarantee a response. However, an impressive set of answers to this questionnaire is much more likely to result in a response than almost all other forms of contact.

If you are interested in pursuing graduate studies at NTU, please apply at the portal.


Our Sesame Python API package has been released to pypi: https://pypi.org/project/pysame. Sesame is scalable stream mining library on modern hardware written in C++ By now Sesame contains several representative real-world stream clustering algorithms and synthetic algorithms. Sesame was first announced in SIGMOD 2023.

A collaborate work Low-Latency Video Conferencing via Optimized Packet Routing and Reordering is accepted to IEEE IWQoS 2024! Congrats to Prof.Amelie Chi Zhou

We have announced a lab regulations about how our lab works.

One paper CStream: Parallel Data Stream Compression on Multicore Edge Devices is accepted to TKDE! Congrats to Xianzhi Zeng! This is the third 1st author top-tier publication since Xianzhi joined the team in late 2021.

One paper Fast Parallel Recovery for Transactional Stream Processing on Multicores is accepted to ICDE 2024! Congrats to the visiting PhD student Jianjun Zhao

A collaborate work Data-Aware Adaptive Compression for Stream Processing is accepted to TKDE! Congrats to Prof.Feng Zhang

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