Registration & Talk details

We invite you to a speaker series focused on learning about equity, diversity and inclusion practices and initiatives in Statistics and Data Science. Our next speaker will be Dr. Aleksandra Korolova, Assistant Professor of Computer Science and Public Affairs, Princeton University. 

Date/Time: March 26, 2026, 11:00am – 12:00pm

Talk title: Lessons from auditing the hidden societal impacts of ad delivery algorithms

Abstract: Although targeted advertising has been touted as a way to give advertisers a choice in who they reach, increasingly, ad delivery algorithms designed by the ad platforms are invisibly refining those choices. In this talk, I will present our findings from "black-box" auditing of the role of ad delivery algorithms in shaping who sees opportunity and political ads using only the tools and data accessible to any advertiser. I will then discuss legal and policy efforts to mitigate the harmful effects of ad delivery in these domains, including their shortcomings and potential paths forward.

Bio: Aleksandra Korolova is an Assistant Professor of Computer Science and Public Affairs at Princeton University, where she is also affiliated with the Center for Information Technology Policy. She studies societal impacts of AI, and develops and deploys algorithms and technologies that enable data-driven innovations while preserving privacy, fairness, and robustness. She also designs and performs algorithm and AI audits. Aleksandra is a co-winner of the 2011 PET Award for outstanding research in privacy enhancing technologies for being among the first to identify privacy risks of microtargeted advertising. Her work on RAPPOR, the first commercial deployment of differential privacy, has been recognized by ACM Conference on Computer and Communications Security 2024 Test-of-Time Award. Aleksandra's research on discrimination in ad delivery has received the 2019 CSCW Honorable Mention Award and Recognition of Contribution to Diversity and Inclusion, was a runner-up for the 2021 WWW Best Student Paper Award, and was a winner of the 2025 FAccT Best Paper Award. Aleksandra is a recipient of the Presidential Early Career Award for Scientists and Engineers, a Sloan Research Fellowship and the NSF CAREER Award.

If you would like to attend this virtual talk, please register using the link below:

https://ubc.zoom.us/meeting/register/bTdpB5a2S5SngAX1d1ch6Q

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This talk is one of the Statistics Equity, Diversity and Inclusion Speaker Series. For more information, please visit: https://www.stat.ubc.ca/seminar-series

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: The presence of big data, characterized by exceptionally large sample size, often brings the challenge of outliers and data distributions that exhibit heavy tails. An online learning estimation that incorporates anti-outlier capabilities while not relying on historical data is therefore urgently required to achieve robust and efficient estimators. In this talk, we introduce an innovative online learning approach based on a mode kernel-based objective function, specifically designed to address outliers and heavy-tailed distributions in the context of big data. The developed approach leverages mode regression within an online learning framework that operates on data subsets, which enables the continuous updating of historical data using pertinent information extracted from a new data subset. We demonstrate that the resulting estimator is asymptotically equivalent to the mode estimator calculated using the entire dataset. Monte Carlo simulations and an empirical study are presented to illustrate the finite sample performance of the proposed estimator.