[Theory] 2/3 Talks at TTIC: Elena Sizikova, New York University

[Mary Marre]

When:        Thursday, February 3rd at *11:00 am CT*

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

                    TTIC, 6045 S. Kenwood Avenue

                    5th Floor, Room 530

*Virtually:*    Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_g74nkZ2tR72wU-oSeXqqHw>*)


*Who: *         Elena Sizikova, New York University


*Title:*

Learning with Incomplete Supervision in Imaging Applications


*Abstract:*

There exists an abundance of neural network techniques in computer vision
that achieve impressive performance on various visual processing tasks.
Most of these methods require large and fully-supervised training datasets,
impeding their applicability in the medical imaging domain. In this talk, I
discuss the limitations of large-scale supervised training for medical
image analysis, and propose several approaches to address these challenges.
Specifically, I show how synthetic data can improve computational model
performance when real datasets are small or not available. I also discuss
how weakly-supervised and unsupervised learning can circumvent the reliance
on large-scale supervision. The proposed new methodology is evaluated on
various computer vision and medical imaging benchmarks.


*Bio:*

Dr. Elena Sizikova is a Moore Sloan Faculty Fellow in the Center for Data
Science, New York University (NYU). She received her BA in Mathematics and
Computer Science from the University of Oxford, UK in 2013. She completed
her PhD in Computer Science at Princeton University in July 2019, where she
was NSF Graduate Research Fellow in the 3D Vision Lab advised by Professor
Thomas Funkhouser. During her PhD studies, she spent time as a research
intern at Siemens Healthineers and Adobe Research. She has received best
paper awards at the ECCV 2016 Workshop on Virtual and Augmented Reality
(VARVAI) and the EUROGRAPHICS 2016 Workshop on Graphics and Cultural
Heritage (GHC). In recognition for her work, she was selected as a 2020
Rising Star in Engineering in Health in the School of Engineering and
College of Physicians and Surgeons at Columbia University. Elena's research
focuses on developing new computational methods and algorithms in computer
vision which aim to address pressing challenges in medical imaging,
biomedical research, and more generally visual understanding tasks (see
https://esizikova.github.io to learn more about her work).

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



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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