CDAC Student Roundtable – Olga Russakovsky (Princeton)

Student Roundtable with Olga Russakovsky (Princeton University)

The Center for Data and Computing, in partnership with the UChicago Graduate Women in Computer Science, welcomes University of Chicago students (undergraduate, masters, and PhD) to register for a virtual roundtable discussion with Olga Russakovsky, moderated by UChicago CS PhD candidate Jean Salac. This event is open only to UChicago students.

Join us also for Dr. Russakovsky’s Distinguished Speaker Series lecture, entitled “Fairness in Visual Recognition,” on Monday January 25th from 3:00-4:00pm. Register here.

Speaker Bio: Dr. Olga Russakovsky is an Assistant Professor in the Computer Science Department at Princeton University. Her research is in computer vision, closely integrated with the fields of machine learning, human-computer interaction and fairness, accountability and transparency. She has been awarded the AnitaB.org’s Emerging Leader Abie Award in honor of Denice Denton in 2020, the CRA-WP Anita Borg Early Career Award in 2020, the MIT Technology Review’s 35-under-35 Innovator award in 2017, the PAMI Everingham Prize in 2016 and Foreign Policy Magazine’s 100 Leading Global Thinkers award in 2015. In addition to her research, she co-founded and continues to serve on the Board of Directors of the AI4ALL foundation dedicated to increasing diversity and inclusion in Artificial Intelligence (AI). She completed her PhD at Stanford University in 2015 and her postdoctoral fellowship at Carnegie Mellon University in 2017.

Moderator Bio: Jean Salac is a Computer Science PhD candidate and NSF Graduate Fellow at the University of Chicago’s CANON Lab working with Professor Diana Franklin. She earned her M.S. from UChicago in 2020 and her B.S. from the University of Virginia in 2017, both in Computer Science. Her research interests include computer science education and human-computer interaction. Her doctoral research focuses on identifying disparities young children face in CS education and developing strategies to overcome such challenges. She has been named an EECS Rising Star and her work has won Best Paper at the International Computing Education Research Conference (ICER) and an honorable mention for Best Paper at the conference on Human Factors in Computing Systems (SIGCHI). In addition to ICER and SIGCHI, she has also published her research at the conference on Innovation and Technology in Computer Science Education (ITiCSE) and the Technical Symposium on Computer Science Education (SIGCSE).

Part of the CDAC Winter 2021 Distinguished Speaker Series:

Bias Correction: Solutions for Socially Responsible Data Science

Security, privacy and bias in the context of machine learning are often treated as binary issues, where an algorithm is either biased or fair, ethical or unjust. In reality, there is a tradeoff between using technology and opening up new privacy and security risks. Researchers are developing innovative tools that navigate these tradeoffs by applying advances in machine learning to societal issues without exacerbating bias or endangering privacy and security. The CDAC Winter 2021 Distinguished Speaker Series will host interdisciplinary researchers and thinkers exploring methods and applications that protect user privacy, prevent malicious use, and avoid deepening societal inequities — while diving into the human values and decisions that underpin these approaches.