Machine Learning and Friends Lunch: Silvia Sellán (Columbia University), Stochastic Computer Graphics

Content

Speaker

Silvia Sellán (Columbia University)

Abstract

Computer Graphics research has long been dominated by the interests of large film, television, and social media companies, forcing other, more safety-critical applications (e.g., medicine, engineering, security) to repurpose graphics algorithms originally designed for entertainment. In this talk, Sellán will advocate for a perspective shift in our field that allows us to design algorithms directly for these safety-critical application realms. She will show that this begins by reinterpreting traditional Graphics tasks (e.g., 3D modeling and reconstruction) from a statistical lens and quantifying the uncertainty in our algorithmic outputs, as exemplified by the research she has conducted for the past five years. Sellán will end by mentioning several ongoing and future research directions that carry this statistical lens to entirely new problems in Graphics and Vision and into specific applications.

Bio

Silvia is a current postdoc at MIT and an incoming faculty member at Columbia University, working on Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow, and the winner of the 2021 University of Toronto Arts & Science Dean’s Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH.