My research broadly covers high performance software systems, from network function policy upgrades to protocols for hybrid packet/circuit networks and efficient ML inference on resource-constrained devices.
I was advised by Professor Dave Andersen and Michael Kaminsky for three years. In the NetSys Lab, I worked on big data analytics systems with Kay Ousterhout and Professors Sylvia Ratnasamy and Scott Shenker. I received my B.S. in Electrical Engineering and Computer Science from UC Berkeley in 2015.
I am from San Diego, CA.
Publications Google Scholar
Adapting TCP for reconfigurable datacenter networks NSDI 2020
Matthew K. Mukerjee, Christopher Canel, Weiyang Wang, Daehyeok Kim, Srinivasan Seshan, Alex C. Snoeren
Scaling video analytics on constrained edge nodes SysML 2019
Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky,
Subramanya R. Dulloor
Mainstream: Dynamic stem-sharing for multi-tenant video processing USENIX ATC 2018
Angela Jiang, Daniel L.-K. Wong, Christopher Canel, Lilia Tang, Ishan Misra, Michael Kaminsky,
Michael A. Kozuch, Padmanabhan Pillai, David G. Andersen, Gregory R. Ganger
Monotasks: Architecting for performance clarity in data analytics frameworks SOSP 2017
Kay Ousterhout, Christopher Canel, Sylvia Ratnasamy, Scott Shenker
Performance clarity as a first-class design principle HotOS 2017
Kay Ousterhout, Christopher Canel, Max Wolffe, Sylvia Ratnasamy, Scott Shenker