HE Bingsheng

COM3-02-12

651 67998

www.comp.nus.edu.sg/~hebs

Professor
Vice Dean, Research

Ph.D. (Computer Science, Hong Kong University of Science & Technology, 2008)
B.E. and B.B.A. (Computer Science & Engineering, 2nd Major: Business Administration, Shanghai Jiao Tong University, China, 2003)

Dr. Bingsheng He is currently a Professor at Department of Computer Science, National University of Singapore. Before joining NUS in May 2016, he held a research position in the System Research group of Microsoft Research Asia (2008-2010) and a faculty position at Nanyang Technological University, Singapore. He got his Bachelor degree in Shanghai Jiao Tong University (1999-2003), and his Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). Bingsheng has served as a PC member for international conferences in databases (e.g.,ACM SIGMOD, VLDB, IEEE ICDE), cloud computing (e.g., ACM SoCC) and parallel and distributed systems (e.g., SC, HPDC and IPDPS), and as a demo co-Chair in VLDB 2017, PC co-Chair in IEEE CloudCom 2014/2015 , HardBD2016 and IEEE BigData Congress 2018. He has served on editorial boards of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS). His current research interests include Big data management systems (with special interests in cloud computing and emerging hardware systems), Parallel and distributed systems and Cloud Computing. His papers are published in prestigious international journals (such as ACM TODS and IEEE TKDE/TPDS/TC) and proceedings (such as ACM SIGMOD, VLDB/PVLDB, ACM/IEEE SuperComputing, ACM HPDC, and ACM SoCC). He is affiliated with both the Systems and Networking group and the Database group at NUS.

RESEARCH AREAS

Database

Systems & Networking

RESEARCH INTERESTS

Big Data Systems On New Hardware
Cloud Computing
High Performance Computing
Data System Research and Applications on Emerging Architectures (GPU, many-core CPU and FPGA, NVRAM/NVM, and cloud computing etc)

RESEARCH PROJECTS

Enhancing Legal Document Services with Accessible and Private LLM Technology

This research focuses on developing a local, privacy-preserving Large Language Model (LLM) for legal document services. By eliminating reliance on external servers, the proposed solution enhances user privacy, efficiency, and reliability. The study addresses challenges related to memory and computational constraints through optimisations, aiming to provide accessible and secure document processing.

Database, Systems & Networking

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

Amelie Chi Zhou*, Yifan Gong*, Bingsheng He and Jidong Zhai. Efficient Process Mapping in Geo-Distributed Cloud Data Centers. SC17: International Conference for High Performance Computing, Networking, Storage and Analysis 2017.
Haikun Liu, Yujie Chen, Xiaofei Liao, Hai Jin, Long Zheng and Bingsheng He. Hardware/Software Cooperative Caching for Hybrid DRAM/NVM Memory Architectures. ACM ICS 2017: 2017 International Conference on Supercomputing. [Acceptance rate: 28/117]
Amelie Chi Zhou*, Shadi Ibrahim, Bingsheng He. On Achieving Efficient Data Transfer for Graph Processing in Geo-Distributed Datacenters. ICDCS 2017: International Conference on Distributed Computing Systems.
Kai Zhang^, Jiayu Hu, Bingsheng He, Bei Hua. DIDO: Dynamic Pipelines for In-Memory Key-Value Stores on Coupled CPU-GPU Architectures. ICDE 2017: IEEE International Conference on Data Engineering ICDE, 2017.
Shuhao Zhang*, Bingsheng He, Daniel Dahlmeier, Chi Zhou and Thomas Heinze. Revisiting the Design of Data Stream Processing Systems on Multi-Core Processors. ICDE 2017: IEEE International Conference on Data Engineering ICDE, 2017.
Shanjiang Tangˆ, Bingsheng He, Shuhao Zhang*, Zhaojie Niu*. Elastic Multi-Resource Fairness: Balancing Fairness and Efficiency in Coupled CPU-GPU Architectures. SC16: International Conference for High Performance Computing, Networking, Storage and Analysis 2016.
Paul Johns*, Jiong He*, Bingsheng He. GPL: A GPU-based Pipelined Query Processing Engine. SIGMOD 2016: ACM SIGMOD International Conference on Management of data, 2016.
Shuang Chen*, Shunning Jiang*, Bingsheng He, Xueyan Tang. A Study of Sorting Algorithms on Approximate Memory. SIGMOD 2016: ACM SIGMOD International Conference on Management of data, 2016.

AWARDS & HONOURS

2017 IEEE/ACM WILLIAM J. MCCALLA ICCAD BEST PAPER AWARD (Front End) (for the work “Jieru Zhao, Liang Feng, Wei Zhang, Sharad Sinha, Yun (Eric) Liang, Bingsheng He. COMBA: A Comprehensive Model-Based Analysis Framework for High Level Synthesis of Real Applications.”). https://iccad.com/award_recipients
IEEE IC2E 2016 Best Paper Runner Up (for the work “Zhaojie Niu, Bingsheng He and Fangming Liu. Not All Joules are Equal: Towards Energy-Efficient and Green-Aware Data Processing Frameworks”).
Top-quality papers of FPL 2015 (for the work “Zeke Wang^, Bingsheng He, Wei Zhang. A Study of Data Partitioning on OpenCL-based FPGA”).
IEEE CloudCom 2014 Best Ph.D. Consortium Paper Award (for the work “Amelie Chi Zhou* and Bingsheng He. Simplified Monetary Optimizations for Workflows in IaaS Clouds”).
Best Demo Award in IEEE SECON 2014 (for the work “Wan Du, Mo Li, Zikun Xing, Bingsheng He, Lloyd Hock Chye Chua, Zhenjiang Li, Yuanqiang Zheng and Pengfei Zhou, Demo Abstract: Wind Measurements for Water Quality Studies in Urban Reservoirs.”).
Spotlight article of IEEE Transactions on Cloud Computing March 2014, vol. 2 no. 1. http://www.computer.org/csdl/trans/cc/2014/01/06702454.pdf. (for the work “Amelie Chi Zhou* and Bingsheng He, Transformation-based Monetary Cost Optimizations for Workflows in the Cloud.”)
“Best system demos” in VLDB 2013 (for the work “Jianlong Zhong*, Bingsheng He. Parallel Graph Processing on Graphics Processors Made Easy”, out of 140+ submissions, invited submission to SIGMOD Record).
“Best research papers” in ACM SIGMOD 2008 (for the work “Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander. Relational Joins on Graphics Processors”, invited submission to ACM TODS).