Curriculum Vitae
I am currently the Scientific Coordinator at the Research Network Data Science at the University of Vienna.
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
University of Vienna
April 2021 - present
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
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
2022
- David Arbour, Drew Dimmery, Tung Mai, and Anup Rao. (2022) "Online Balanced Experimental Design."
Preprint - 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
2021
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