Yue Cheng
Assistant Professor at the University of Virginia

I am an Assistant Professor of Data Science and Computer Science at the University of Virginia. My research covers a range of topics including distributed systems, serverless and cloud computing, storage systems, operating systems, and high-performance computing. My current research focuses on designing scalable, high-performance, and easy-to-use computer systems that manage and process huge volume of data.
Currently I am working on: (1) Serverless and FaaS: improving serverless computing using a end-to-end approach that cuts across the entire software-hardware stack: (stateful) applications, middleware, platforms, and lower-level OS/HW (watch this Youtube video summarizing our recent focus on serverless computing); (2) Sys4ML: building better (computing and storage) systems for distributed machine learning; and (3) ML4Sys: improving systems software and infrastructure management by using learned or data-driven approaches.
I am the recipient of an NSF CAREER Award (2021), an Amazon Research Award (2021), a Meta Research Award (2022), and the IEEE CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing (2022). Prior to joining UVA, I was an Assistant Professor of Computer Science at George Mason University, from 2017 to 2022. I received my Ph.D. degree in Computer Science from Virginia Tech, working with Dr. Ali R. Butt. During my Ph.D. I spent two summers at IBM Research Almaden in 2013 and 2014, and six months at Dell EMC Princeton Office in 2015.
news
Dec 2022 | Congrats to Redwan, Ahmad, and Yuqi on their paper on deep learning I/O caching accepted to FAST 2023! |
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Sep 2022 | Congrats to Zhaoyuan on his paper accepted to DRBSD-8 co-located with SC 2022! |
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Aug 2022 | In Fall ‘22, I am joining the School of Data Science and the Department of Computer Science at the University of Virginia. |
Jul 2022 | ![]() |
Jun 2022 | Congrats to Yuqi on his paper on serverless function scheduling accepted to SC 2022! |
May 2022 | This summer my students will intern at MSR (Ben Carver), ByteDance (Yuqi Fu, Jingyuan Zhang), and Argonne National Lab (Zhaoyuan Su)! Congrats! |
May 2022 | 🏆 Thrilled to receive an Outstanding Teaching Award from CS @ Mason! |
Aug 2021 | Congrats to Li and Haoliang on rKube accepted to SoCC 2021! |
Aug 2021 | A collaborative FMSG grant funded by NSF (with Jia Liu @ Auburn). Thanks, NSF! |
Jun 2021 | Congrats to Zheng on FedAT accepted to SC 2021! |
Apr 2021 | Congrats to Ao on FaaSNet accepted to USENIX ATC 2021! |
Mar 2021 | Honored to receive a gift from Adobe Research for our work on serverless computing! Thanks, Adobe! |
Feb 2021 | Thrilled to receive an NSF CAREER Award for my work on building serverless cloud storage infrastructure. Thanks, NSF! |
Oct 2020 | Excited to receive an Amazon Research Award with Liang Zhao from Emory! |
Aug 2020 | Congrats to Junxiang and Zheng on their paper getting accepted to IEEE ICDM 2020! |
Aug 2020 | Congrats to Ben, Jingyuan, and Ao on Wukong getting accepted by ACM SoCC 2020! Wukong is a super-fast serverless parallel computing framework built atop AWS Lambda. Wukong achieves up to 68X speedup over state-of-the-art serverless parallel processing frameworks. Wukong project is online. We are happy to accept contributions! |
Jul 2020 | Two projects got funded by NSF. With the new MRI grant, we will be building a new HPC infrastructure to support the growing computing needs for Mason users. With an OAC grant, we will be building a new model parallel deep learning training infrastructure. Thanks NSF! |
Mar 2020 | Congrats to Zheng, Ahsan, and Syed on TiFL getting accepted to ACM HPDC 2020! |
Dec 2019 | Congrats to Ao, Jingyuan, and Xiaolong on InfiniCache getting accepted to USENIX FAST 2020! InfiniCache is a first-of-its-kind, cost-effective, object cache that is built atop ephemeral cloud funtions. InfiniCache is 31-96x cheaper than existing cloud cache services (e.g., AWS ElastiCache) while offering same or better performance. Fork InfiniCache on GitHub. |
selected publications
- USENIX FAST’23SHADE: Enable Fundamental Cacheability for Distributed Deep Learning TrainingIn 21th USENIX Conference on File and Storage Technologies (FAST 23) 2023