Curriculum Vitae
I am the Professor of Data Science for the Common Good at the Hertie School’s Data Science Lab.
Research Interests
- Causal Inference
- Machine Learning
- Data Science
- Experimental Design
Education
New York University
PhD in Political Methodology
Advisors: Cyrus Samii, Neal Beck, Josh Tucker
New York, NY
Granted May 2016
- Dissertation: Essays on Causal Inference and Machine Learning with Application to Nonprofits
- Winner of 2015 Williams Award for Best Dissertation Proposal in Political Methodology from the Society of Political Methodology
UNC Chapel Hill
B.A. in International and Area Studies with distinction
Chapel Hill, NC
Granted June 2010
Experience
Hertie School
January 2024 - present
Professor of Data Science for the Common Good
University of Vienna
April 2021 - December 2023
Scientific Coordinator
- Supervisor: Philipp Grohs
Facebook Core Data Science
Sept 2016 - March 2021
Research Scientist
- Part of Eytan Bakshy’s Adaptive Experimentation team
- Developed statistical, machine learning and experimental methodology
- Ran adaptive and contextual field experiments with a variety of product teams
- Integrated advanced methodologies into a toolkit for scalable and automatic experimentation intended for optimization (Ax) - released at F8 2019)
- Developed scalable methods for robust observational causal inference as the technical lead of our “CausalML” initiative
Princeton University
September 2015 - May 2016
Pre-doctoral fellow
- Supervised by Kosuke Imai
Facebook Core Data Science
Summer 2015
Summer Intern
- Statistical and Decision Science Team
- Supervised by Eytan Bakshy
Teaching
Internal datacamp class on designing and analyzing experiments
2017-2019
NYU Undergraduate
TA for Power and Politics in America (under Jonathan Nagler)
Fall 2014
TA for Games, Strategy and Politics (under Steven Brams)
Fall 2013
NYU Graduate
TA for Quantitative Methods II (under Nathaniel Beck)
Spring 2015
TA for Quantitative Methods II (under Cyrus Samii)
Spring 2014
High Performance Computing Talk for NYU Datalab
February 2014
Introduction to R for NYU Datalab
January 2013
Published
2024
2023
- Andrew Guess, Neil Malhotra, Jennifer Pan, Pablo Barberá, Hunt Alcott, Taylor Brown, Adriana Crespo-Tenorio, Drew Dimmery, Deen Freelon, Matthew Gentzkow, Sandra González-Bailón, Edward Kennedy, Young Mie Kim, David Lazer, Devra Moehler, Brendan Nyhan, Carlos Velasco Rivera, Jaime Settle, Daniel Thomas, Emily Thorson, Rebekah Tromble, Arjun Wilkins, Magdalena Wojcieszak, Beixian Xiong, Chad Kiewet de Jong, Annie Franco, Winter Mason, Natalie Jomini Stroud, and Joshua Tucker. (2023) "How do social media feed algorithms affect attitudes and behavior in an election campaign?." Science
Published - Andrew Guess, Neil Malhotra, Jennifer Pan, Pablo Barberá, Hunt Alcott, Taylor Brown, Adriana Crespo-Tenorio, Drew Dimmery, Deen Freelon, Matthew Gentzkow, Sandra González-Bailón, Edward Kennedy, Young Mie Kim, David Lazer, Devra Moehler, Brendan Nyhan, Carlos Velasco Rivera, Jaime Settle, Daniel Thomas, Emily Thorson, Rebekah Tromble, Arjun Wilkins, Magdalena Wojcieszak, Beixian Xiong, Chad Kiewet de Jong, Annie Franco, Winter Mason, Natalie Jomini Stroud, and Joshua Tucker. (2023) "Reshares on social media amplify political news but do not detectably affect beliefs or opinions." Science
Published - Brendan Nyhan, Jaime Settle, Emily Thorson, Magdalena Wojcieszak, Pablo Barberá, Annie Chen, Hunt Alcott, Taylor Brown, Adriana Crespo-Tenorio, Drew Dimmery, Deen Freelon, Matthew Gentzkow, Sandra González-Bailón, Andrew Guess, Edward Kennedy, Young Mie Kim, David Lazer, Neil Malhotra, Devra Moehler, Jennifer Pan, Daniel Thomas, Rebekah Tromble, Carlos Velasco Rivera, Arjun Wilkins, Beixian Xiong, Chad Kiewet de Jong, Annie Franco, Winter Mason, Natalie Jomini Stroud, and Joshua Tucker. (2023) "Like-minded sources on Facebook are prevalent but not polarizing." Nature
Published - Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali Ozkes, and Management Science Reproducibility Collaboration. (2023) "Reproducibility in Management Science."
Preprint Published
2022
- David Arbour, Drew Dimmery, Tung Mai, and Anup Rao. (2022) "Online Balanced Experimental Design." ICML
Preprint Published - Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, and Eytan Bakshy. (2022) "Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies."
Preprint Published
2021
- David Arbour, Drew Dimmery, and Arjun Sondhi. (2021) "Permutation Weighting." ICML
Preprint Published - David Arbour, Drew Dimmery, and Anup Rao. (2021) "Efficient Balanced Treatment Assignments for Experimentation." AISTATS
Preprint Github Published - My Phan, David Arbour, Drew Dimmery, and Anup Rao. (2021) "Designing Transportable Experiments Under S-admissability." AISTATS
Preprint Github Published
2020
2019
2016
- Drew Dimmery and Andrew Peterson. (2016) "Shining the Light on Dark Money: Political Spending by Nonprofits." RSF: The Russell Sage Foundation Journal of the Social Sciences
Published
2012
- C.T Kullenberg, S.R. Mishra, Drew Dimmery, and the NOMAD Collaboration. (2012) "A search for single photon events in neutrino interactions." Physics Letters B
Published
Working Papers / Non-archival
2021
- Molly Offer-Westort and Drew Dimmery. (2021) "Experimentation for Homogenous Policy Change."
Preprint
2019
- Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, and Eytan Bakshy. (2019) "Real-world Video Adaptation with Reinforcement Learning." ICML Workshop - RL4RealLife
Preprint - Sam Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, and Eytan Bakshy. (2019) "Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints." NeurIPS Workshop - Safety and Robustness in Decision Making
Preprint