Machine Learning Workloads And Networking


Manya Ghobadi

Massachusetts Institute of Technology

The keynote speaker Manya Ghobadi's face
Manya Ghobadi is faculty in the EECS department at MIT. Her research spans different areas in computer networks, focusing on optical reconfigurable networks, networks for machine learning, and high-performance cloud infrastructure. Her work has been recognized by the ACM-W Rising Star award, Sloan Fellowship in Computer Science, ACM SIGCOMM Rising Star award, NSF CAREER award, Optica Simmons Memorial Speakership award, best paper award at the Machine Learning Systems (MLSys) conference, as well as the best dataset and best paper awards at the ACM Internet Measurement Conference (IMC). Manya received her Ph.D. from the University of Toronto and spent a few years at Microsoft Research and Google before joining MIT.

Session Type




What makes machine learning workloads challenging? Manya will explore three dimensions of designing next-generation machine learning systems: congestion control, network topology, and computation frequency.
More details later.