My research broadly covers high performance software systems, from datacenter congestion control to protocols for hybrid packet/circuit networks and efficient ML inference on resource-constrained devices.
Summer 2021: I worked on microburst congestion control for datacenters with Facebook's Systems & Infrastructure Host Networking team.
Fall 2021: I am an instructor for CMU's 15-441/641 Networking and the Internet course. Please reach out to me with questions about the course.
From 2016-2019, I was a lead student in the Intel Science and Technology Center for Visual Cloud Systems, where I worked under the guidance of Professor Dave Andersen and Michael Kaminsky. From 2014-2016, I built big data analytics systems with Kay Ousterhout and Professors Sylvia Ratnasamy and Scott Shenker in the NetSys Lab. 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