March 23-24 2022
Sys/ML Workshop
Sponsored by UT Austin MLL
GDC Building, UT Austin (main campus)
This workshop will explore emerging research problems and opportunities at the exciting intersection between computer systems and machine learning. We will consider how to rethink the design of computer systems from the ground up to enable principled ML-driven optimization, and explore new theoretical ML questions that must be addressed to ensure that systems offer the desired performance, robustness, and safety in practice. Alongside, we will discuss new ideas in the design of systems to support advancements in large-scale learning problems. This Sys/ML workshop will bring together academics from UT Austin and beyond, as well as ML practitioners from the industry, to chart a research agenda for "systems + ML research" with an emphasis on real-world deployability.
Participants
Adam Kilvans
UT Austin
Adam Wierman
Caltech
Aditya Akella
UT Austin
Alexandros G Dimakis
UT Austin
Atlas Wang
UT Austin
Aws Albarghouthi
UW Madison
Bayan Bruss
Capital One
Ben Fauber
Dell Technologies
Benjamin Van Roy
Stanford
Brighten Godfrey
UIUC
Calvin Lin
UT Austin
Carlo Curino
Microsoft
Chris Rossbach
UT Austin
Constantine Caramanis
UT Austin
Devavrat Shah
MIT
Diana Marculescu
UT Austin
Hadi Esmaeilzadeh
UCSD
Hyeji Kim
UT Austin
Josiah Hanna
UW Madison
Kshiteej Mahajan
Google Brain
Lili Qiu
UT Austin
Lizy John
UT Austin
Mark Hill
Microsoft
Mohammad Alizadeh
MIT
Mohit Tiwari
UT Austin
Neeraja J Yadwadkar
UT Austin
Olga Papaemmanouile
Brandeis University
Peter Stone
UT Austin
Qiang Liu
UT Austin
Raajay Viswanathan
Uber
Sanjay Shakkottai
UT Austin
Sharon Li
UW Madison
Swarat Chaudhuri
UT Austin
Taesang Yoo
Qualcomm
Vijay Chidambaram
UT Austin
Yuke Zhu
UT Austin
Organizers
Aditya Akella
UT Austin
Sanjay Shakkottai
UT Austin