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I’m part of Systems Research Group at Northeastern University. I’m broadly interested in computer systems. I build systems that enable users to verify their outsourced services. In particular, my recent research includes: verifying the execution of applications on untrusted servers, verifying behaviors of black-box databases,
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Cheng Tan, Deepak Patil, Antonino Tumeo, Gabriel Weisz,...
- Cheng Tan - Home
Cheng Tan. Google & ASU. I am currently a software engineer...
- Publications - Home
Cheng Tan. Google & ASU. I am currently a software engineer at Google working on Edge TPU. I am also a visiting scientist in the School of Electrical, Computer and Energy Engineering at ASU.
- Bellevue, WA
Cheng Tan | Home. Bio. I am a third-year Ph.D. candidate (2021~) at Zhejiang University and Westlake University, advised by Stan Z. Li. My research focuses on generative AI for Science and focuses on solving key problems in the field of science, such as biomolecular design and spatiotemporal predictive learning.
Necessary and sufficient stabilizing conditions for networked control systems with simultaneous transmission delay and packet dropout. Cheng Tan, Huanshui Zhang. the proceedings of 54th IEEE...
Nov 22, 2022 · SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning. Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li. Recent years have witnessed remarkable advances in spatiotemporal predictive learning, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies.
Professor Tan-To Cheung is the Co-Chief of the Divisions of Hepatobiliary & Pancreatic Surgery and Liver Transplantation within the Department of Surgery, Queen Mary Hospital, the University of Hong Kong, and President of the Hong Kong Society of Hepatobiliary and Pancreatic Surgery, Vice president (External Affairs) of College of Surgeons on ...
Cheng Tan, Deepak Patil, Antonino Tumeo, Gabriel Weisz, Steve Reinhardt, Jeff Zhang. 2023 IEEE/ACM International Conference On Computer Aided Design (ICCAD), November 2023. ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs.
