Columbia University Statistical Machine Learning Symposium aims to bring together leading researchers whose work is at the forefront of theoretical, methodological, and applied statistical machine learning.  At a high level, statistical machine learning aims to develop statistical methodology that allows computers to extrapolate from complex observations. Such methods have proved to be powerful tools in many computer science and engineering applications like image processing, artificial intelligence, and natural language processing, as well as in other disciplines like astronomy, genetics, finance, physics, and medicine.

The 2023 Statistical Machine Learning Symposium is a two-day workshop that will be held April 7-8, 2023 at Columbia University, School of Social Work, 1255 Amsterdam Ave, Room 311-312. It will consist of lectures from invited speakers in academia and industry, as well as lightning talks and poster presentations from junior researchers (graduate students and postdocs; please submit abstracts here no later than March 21, 2023). See here for the schedule and click here to register.

These activities are made possible through the generous support of the Department of Statistics and Data Science Institute (DSI).

Organizing committee: Daniel Hsu, Samory Kpotufe, Arian Maleki, Cindy Rush, Eren Kizildag, Belinda Tzen.

List of Invited Speakers:

Misha Belkin (UCSD)
Joan Bruna Estrach (NYU)
Matt Hoffman (Google)
Stefanie Jegelka (MIT)
Adam Klivans (UT Austin)
Zongming Ma (UPenn)
Marina Meila (UW)
Vahab Mirrokni (Google)
Nati Srebro (TTIC)
Pragya Sur (Harvard)
Mengdi Wang (Princeton)
Dana Yang (Cornell)

Previously, the Department of Statistics, with the generous support of the TRIPODS Institute (hosted at DSI), has held numerous workshops and talks on statistical machine learning. See past events from Fall 2019 and Spring 2020, comprising a Special Year of Statistical ML (2019-2020).