[Theory] REMINDER: [TTIC Talks] 2/6 Talks at TTIC: Elijah Cole, California Institute of Technology

[Brandie Jones]

*When: *Monday, February 6th *at 11:30 AM CT*

*Where: *Talk will be given *live, in-person* at

                     TTIC, 6045 S. Kenwood Avenue

                     5th Floor, Room 530


*Virtually: *via Panopto (Livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7e78d4df-336b-4b23-83f0-af8e01158847>
)

*Who: *Elijah Cole, California Institute of Technology

*Title: *Learning from Real-World Data

*Abstract:   *Humans are collecting vast amounts of data to tackle societal
challenges like preserving biodiversity, improving healthcare outcomes, and
accelerating scientific discovery. However, in many domains we are
collecting far too much data to analyze manually. To meet these challenges,
we need machine learning algorithms to automatically extract knowledge from
this growing influx of raw data. Unfortunately, most machine learning
algorithms are not compatible with the limited, noisy, and weak supervision
often found in real-world settings.

This talk focuses on the task of learning from real-world data. I will
present progress on the problem of learning from minimal labels in contexts
like multi-label image classification and self-supervised representation
learning. Second, I will also describe how we can leverage small amounts of
domain context to improve machine learning algorithms in a modular and
flexible way, with examples from image classification and object
localization. Finally, I will show how studying real-world data leads to
new machine learning research questions and greater opportunities for
impact in important application domains.

*Bio:   *Elijah Cole (https://elijahcole.me/) is a Ph.D. candidate in the
Computing and Mathematical Sciences department at Caltech, advised by
Pietro Perona. He received a B.S.E. from Duke University in 2017 with a
double major in electrical engineering and mathematics. During his Ph.D. he
completed internships at Google Research, Microsoft AI for Earth, and the
Air Force Research Laboratory. His research focuses on deep learning and
computer vision, with an emphasis on learning from limited, noisy, and weak
supervision. He works with ecologists, physicians, and other domain experts
to align mainstream machine learning research with progress on important
real-world problems. Elijah’s work is funded by an NSF Graduate Research
Fellowship and the Resnick Sustainability Institute.

*Host: Greg Shakhnarovich  <greg at ttic.edu>*



--
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
Working Remotely on Tuesdays
Out of Office: Feb 9th - Feb 16th. Remote: Feb 17th - March 3rd.
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