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.
UT Austin
Caltech
UT Austin
UT Austin
UT Austin
UW Madison
Bayan Bruss
Capital One
Ben Fauber
Dell Technologies
Benjamin Van Roy
Stanford
Brighten Godfrey
UIUC
Calvin Lin
UT Austin
Carlo Curino
Microsoft
UT Austin
UT Austin
Devavrat Shah
MIT
UT Austin
Hadi Esmaeilzadeh
UCSD
Hyeji Kim
UT Austin
UW Madison
Kshiteej Mahajan
Google Brain
UT Austin
UT Austin
Mark Hill
Microsoft
Mohammad Alizadeh
MIT
UT Austin
UT Austin
Olga Papaemmanouile
Brandeis University
UT Austin
UT Austin
Uber
UT Austin
Sharon Li
UW Madison
UT Austin
Taesang Yoo
Qualcomm
UT Austin
UT Austin
UT Austin
UT Austin