Rui Fan
Associate Professor, PI
Information Science and Technology
Shanghai Tech University
China
Biography
Rui Fan joined ShanghaiTech University as an Associate Professor, PI in January 2017. Prior to this he was an assistant professor at Nanyang Technological University, Singapore. He received his BSc in computer science and mathematics from Caltech in 2000, and his PhD in computer science from MIT in 2008. He was a postdoc at the University of Toronto in 2008, and a Viterbi postdoc at the Technion, Israel in 2010. His PhD dissertation received MIT's Sprowl's Award for best computer science theses. He also received two best student paper awards at the ACM Principles of Distributed Computing (PODC) conference. His research interests span a number of topics in parallel and distributed computing. He is especially interested in the design of efficient algorithms and algorithmic solutions to problems on large datasets. Applications areas which interest him include machine learning, energy efficient computing, and managing large scale systems. He also works on distributed algorithms and synchronization related issues, as well as computational lower bounds.
Research Interest
Parallel and distributed computing, Algorithms and lower bounds, Big data, machine learning, Energy efficient computing
Publications
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Rui Fan and Nancy Lynch. Gradient Clock Synchronization. In Proceedings of the 23rd ACM Symposium on Principles of Distributed Computing (PODC), 2004.
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Rui Fan and Nancy Lynch. An W(n log n) Lower Bound on the Cost of Mutual Exclusion. In Proceedings of the 25th ACM Symposium on Principles of Distributed Computing (PODC), 2006.
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Dmitri Perelman, Rui Fan and Idit Keidar. On Maintaining Multiple Versions in STM. In Proceedings of the 29th ACM Symposium on Principles of Distributed Computing (PODC), 2010.
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Pan Lai, Rui Fan. Fast Optimal Nonconcave Resource Allocation. In IEEE Conference on Computer Communications 2015 (INFOCOM), 2015.
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Pham Nguyen Quang Anh, Rui Fan and Yonggang Wen. Balanced Hashing and Efficient GPU Sparse General Matrix-Matrix Multiplication. In 30th International Conference on Supercomputing (ICS), 2016
