Invited Speakers

Pedro H.C. Sant’Anna, Emory University

Bio

Pedro H.C. Sant’Anna is a passionate applied econometrician, working in causal inference and semi- and non-parametric methods. Much of his recent work aims to develop, better understand, and further improve Difference-in-Differences (DiD) methods.

He is an Associate Professor of Economics at Emory University and an Amazon Scholar. He also serves as Associate Editor at the Journal of Econometrics and at the Journal of Business and Economic Statistics.

Previously, Pedro Sant’Anna was an Assistant Professor at Vanderbilt University and a Principal Researcher at Microsoft. Pedro earned his PhD in Econometrics from Universidad Carlos III de Madrid, Spain, and his BA in Economics from Ibmec-MG, Brazil.

Pedro has published in top journals in economics and has given guest lectures and seminars on DiD topics at leading universities around the world. He is also a co-author of several open-source packages for DiD methods, giving him first-hand experience with the practicalities of these modern tools. His industry experience also allows him to effectively communicate with a broad audience from many different backgrounds.

Title

Abstract

Linbo Wang, University of Toronto

Bio

Linbo Wang is Canada Research Chair in Causal Machine Learning, and an associate professor in the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences, University of Toronto. He is also a faculty affiliate at the Vector Institute and holds affiliate positions in the Department of Statistics at the University of Washington and the Department of Computer Science at the University of Toronto. His research focuses on causality and its interaction with statistics and machine learning.

Title

Abstract

Wenjing Zheng, Roblox

Bio

Wenjing Zheng is a Senior Data Science Manager at Roblox, where she leads the Ecosystem and Learning Platforms data science team in building scalable, impact-driven solutions. She holds dual PhDs from UC Berkeley and Université Paris Cité and brings over a decade of experience in causal machine learning. Prior to Roblox, Wenjing led experimentation and ads growth initiatives at Netflix. Her expertise spans causal inference, machine learning, and applied data science. Before entering the industry, she was an academic researcher focused on developing doubly robust semiparametric methods for causal inference.

Jeffrey Wong, Airbnb

Bio

Jeffrey Wong is a Senior Staff Engineer for Airbnb’s Experimentation Platform, and previously Principle Data Scientist for Experimentation at Netflix. Having dual roles in engineering and data science, Jeff’s research is in dynamic policy making rooted in causal effects, and highly performant software to build them. He applies this to build smart engineering systems for adtech, content promotion and experimentation.

In the community, Jeff is leading an open field in computational casual inference, where he is promoting the intersection of software, numerical methods, and causal inference. He is joining us at CMLKDD2025 to share more about these needs and opportunities.