I am currently a Ph.D. candidate in Electrical Engineering at Stanford University, advised by Christos Kozyrakis.

My research centers around building performant and scalable systems for machine learning to train, serve, and enable applications with large machine learning models. I am broadly interested in applying tools across distributed systems, networking, storage, databases, security, and computer architecture. My research won the IEEE S&P Distinguished Practical Paper Award and is a Top Pick in Hardware and Embedded Security. I am grateful to have been selected as an MLCommons Rising Star. My work is generously supported by a Stanford Graduate Fellowship and a Meta Ph.D. Fellowship in AI System HW/SW Co-Design.

I was recently a Visiting Researcher in the FAIR and infrastructure teams at Meta from 2020-2022, where I designed and optimized data storage and ingestion infrastructure for Meta’s machine learning training pipelines. I received my B.S. in Electrical and Computer Engineering from Cornell University in 2018, where I worked with Ed Suh on hardware security.

Publications

[arXiv] cedar: Optimized and Unified Machine Learning Input Data Pipelines
Mark Zhao, Emanuel Adamiak, and Christos Kozyrakis
Preprint, Under Submission

[OSDI’24] High-throughput and Flexible Host Networking for AI Accelerators
Athinagoras Skiadopoulos, Zhiqiang Xie, Mark Zhao, Qizhe Cai, Saksham Agarwal, Jacob Adelmann, David Ahern, Carlo Contavalli, Michael Goldflam, Vitaly Mayatskikh, Raghu Raja, Daniel Walton, Rachit Agarwal, Shrijeet Mukherjee, and Christos Kozyrakis
Proceedings of the 2024 USENIX Symposium on Operating Systems Design and Implementation, July, 2024

[ATC’23] Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training
Mark Zhao, Satadru Pan, Niket Agarwal, Zhaoduo Wen, David Xu, Anand Natarajan, Pavan Kumar, Shiva Shankar P, Ritesh Tijoriwala, Karan Asher, Hao Wu, Aarti Basant, Daniel Ford, Delia David, Nezih Yigitbasi, Pratap Singh, Carole-Jean Wu, and Christos Kozyrakis
Proceedings of the 2023 USENIX Annual Technical Conference, July, 2023

[MLSys’23] RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
Mark Zhao, Dhruv Choudhary, Devashish Tyagi, Ajay Somani, Max Kaplan, Sung-Han Lin, Sarunya Pumma, Jongsoo Park, Aarti Basant, Niket Agarwal, Carole-Jean Wu, and Christos Kozyrakis
Proceedings of the 6th Conference on Machine Learning and Systems, June, 2023

[ISCA’22] Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
Mark Zhao, Niket Agarwal, Aarti Basant, Buğra Gedik, Satadru Pan, Mustafa Ozdal, Rakesh Komuravelli, Jerry Pan, Tianshu Bao, Haowei Lu, Sundaram Narayanan, Jack Langman, Kevin Wilfong, Harsha Rastogi, Carole-Jean Wu, Christos Kozyrakis, and Parik Pol
Proceedings of the 49th International Symposium on Computer Architecture, June, 2022

[ASPLOS’22] ShEF: Shielded Enclaves for Cloud FPGAs
Mark Zhao, Mingyu Gao, and Christos Kozyrakis
Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, March, 2022

[SoCC’21] Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
Francisco Romero*, Mark Zhao*, Neeraja J. Yadwadkar, and Christos Kozyrakis
Proceedings of the 12th ACM Symposium on Cloud Computing, November, 2021
(* denotes equal contribution)

[CCS’18] HyperFlow: A Processor Architecture for Nonmalleable, Timing-Safe Information Flow Security
Andrew Ferraiuolo, Mark Zhao, Andrew C. Myers, and G. Edward Suh
Proceedings of the 25th ACM Conference on Computer and Communications Security, October, 2018

[S&P’18] FPGA-Based Remote Power Side-Channel Attacks
Mark Zhao and G. Edward Suh
Proceedings of the 39th IEEE Symposium on Security and Privacy, May, 2018
Distinguished Practical Paper Award
Top Pick in Hardware and Embedded Security

Contact Me

Please reach out to me at “myzhao@stanford.edu”.