I am at Google Brain, working on large-scale systems for machine learning.
Before Google, I worked at Bytedance as a senior research scientist.
I received my Ph.D from UC Berkeley (2018) and my bachelor's degree from Tsinghua (2013) in computer science.
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A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters
Yimin Jiang, Yibo Zhu, Chang Lan, Bairen Yi, Yong Cui, and Chuanxiong Guo
A Generic Communication Scheduler for Distributed DNN Training Acceleration
Yanghua Peng, Yibo Zhu, Yangrui Chen, Yixin Bao, Bairen Yi, Chang Lan, Chuan Wu, and Chuanxiong Guo
SafeBricks: Shielding Network Functions in the Cloud
Rishabh Poddar, Chang Lan, Raluca Ada Popa, and Sylvia Ratnasamy
ResQ: Enabling SLOs in Network Function Virtualization
Amin Tootoonchian, Aurojit Panda, Chang Lan, Melvin Walls, Katerina Argyraki, Sylvia Ratnasamy, and Scott Shenker
Embark: Securely Outsourcing Middleboxes to the Cloud
Chang Lan, Justine Sherry, Raluca Ada Popa, Sylvia Ratnasamy, and Zhi Liu
BlindBox: Deep Packet Inspection over Encrypted Traffic
Justine Sherry, Chang Lan, Raluca Ada Popa, and Sylvia Ratnasamy
Blocking-resistant Communication through Domain Fronting
David Fifield, Chang Lan, Rod Hynes, Percy Wegmann, and Vern Paxson
E2: A Framework for NFV Applications
Shoumik Palkar*, Chang Lan*, Sangjin Han, Keon Jang, Aurojit Panda, Sylvia Ratnasamy, Luigi Rizzo, and Scott Shenker
Explicit Path Control in Commodity Data Centers: Design and Applications
Shuihai Hu, Kai Chen, Haitao Wu, Wei Bai, Chang Lan, Hao Wang, Hongze Zhao, and Chuanxiong Guo
Secure and Scalable Fault Localization under Dynamic Traffic Patterns
Xin Zhang, Chang Lan, and Adrian Perrig