About
I am an AI Research Lead/VP at JPMorgan AI Research in New York City. My recent research spans problems in causal inference, optimal adaptive experimental design, and algorithmic fairness. Prior to joining J.P. Morgan, I was a PhD student in the Department of Statistics & Data Science at Carnegie Mellon University, where I worked with Edward Kennedy and Alexandra Chouldechova on causal inference problems related to algorithmic fairness.
During my PhD, I completed summer internships in data science at Google (in 2018 and 2019) and Box (in 2017). Before starting my PhD, I worked as a Senior Faculty Research Specialist at the Center for Advanced Study of Language at the University of Maryland, where I conducted research in areas such as psycholinguistics, speech perception, and signal detection theory.
Interests
- Causal Inference
- Algorithmic Fairness
- Semiparametric Inference
- Sequential Decision Making
Education
PhD in Statistics, 2021
Carnegie Mellon University
MS in Statistics, 2017
Carnegie Mellon University
BS in Math, 2016
University of Maryland
BA in Linguistics, 2009
University of Michigan
Experience
AI Research Lead/VP
JPMorgan AI ResearchSenior Researcher
JPMorgan AI ResearchData Scientist Intern
GoogleDeveloped improved methodology to estimate ads lift/incrementality using combined experimental and observational data.Data Scientist Intern
GoogleBuilt a machine learning pipeline to probabilistically match entities in text with entries in a database.Data Science Intern, Business Analytics
BoxBuilt a machine learning pipeline to automatically identify new marketing and sales leads.Senior Faculty Research Specialist
University of Maryland Center for Advanced Study of LanguageDesigned, conducted, and analyzed experiments in areas such as psycholinguistics, speech perception, and signal detection.
News & Events
- 03/2025 Poster presentation (paper here) by colleague Mohsen Ghassemi at the AAAI Conference on Artificial Intelligence in Philadelphia, PA.
- 03/2025 Reviewer for the ACM conference on Fairness, Accountability, and Transparency (FAcct) in Athens, Greece.
- 12/2024 Poster presentation by colleague/former intern Harit Vishwakarma at the NeurIPS Workshop on Statistical Frontiers in LLMs and Foundation Models in Vancouver, Canada.
- 09/2024 “Why Counterfactuals Matter for Fairness”: Invited talk given to the Nokia Bell Labs Responsible AI group (virtual).
- 09/2024 Reviewer for the NeurIPS workshop Algorithmic Fairness through the Lens of Metrics and Evaluation.
- 04/2024 Paper presentation by colleague/former intern Tom Cook at the Conference on Causal Learning and Reasoning (CLeaR) in Los Angeles, CA.
- 03/2024 Reviewer for the ACM conference on Fairness, Accountability, and Transparency (FAcct) in Rio de Janeiro, Brazil.
- 03/2024 Paper accepted to Transactions on Machine Learning Research.
- 02/2024 Presentation (paper/poster) at the AAAI Conference on Artificial Intelligence in Vancouver, Canada.
- 12/2023 Paper presentation by colleague/former intern Tom Cook at the NeurIPS workshop Adaptive Experimental Design and Active Learning in the Real World in New Orleans, LA.
- 12/2023 Presentation (paper/poster) at the NeurIPS workshop Adaptive Experimental Design and Active Learning in the Real World in New Orleans, LA.
- 08/2023 Paper presentation at the UAI workshop Epistemic Uncertainty in Artificial Intelligence (E-pi UAI) in Pittsburgh, PA.
- 06/2023 Reviewer for the European Workshop on Algorithmic Fairness in Winterthur, Switzerland.
- 05/2023 Paper accepted to Transactions on Machine Learning Research.
- 03/2023 Reviewer for the ACM conference on Fairness, Accountability, and Transparency (FAcct) in Chicago, IL.~
- 12/2022 Paper presentation at the NeurIPS workshop Algorithmic Fairness through the Lens of Causuality and Privacy (virtual).
- 12/2022 Reviewer for the NeurIPS workshop Algorithmic Fairness through the Lens of Causuality and Privacy (virtual).
- 12/2022 Attending the ACM International Conference on AI in Finance (ICAIF) in New York, NY.
- 12/2022 Paper accepted to AAAI 2024 in Vancouver, Canada.
- 11/2022 Our paper was nominated was for a Best Paper award at the NeurIPS workshop Adaptive Experimental Design and Active Learning in the Real World in New Orleans, LA.
- 10/2022 Poster presentation by colleague Alessandro Fabris at the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EEAMO) in Arlington, VA.
- 08/2022 Paper presentation by colleague Raphael Sonabend at the KKD Workshop on Applied Data Science for Healthcare in Washington, DC.
- 06/2022 Paper presentation at the ACM conference on Fairness, Accountability, and Transparency (FAcct) in Seoul, South Korea.
- 06/2022 Reviewer for the ACM conference on Fairness, Accountability, and Transparency (FAcct) in Seoul, South Korea.
- 06/2022 Paper presentation by colleague Alessandro Fabris at the European Workshop on Algorithmic Fairness in Zurich, Switzerland.
- 12/2021 ‘Fair and accurate risk assessment for healthcare decision making.’: Invited talk given at the 14th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (virtual).
- 12/2021 Poster presentation at NeurIPS workshop Algorithmic Fairness through the Lens of Causality and Robustness (virtual).
- 12/2021 Reviewer for the NeurIPS workshop Algorithmic Fairness through the Lens of Causality and Robustness (virtual).
- 12/2021 Program Committee/Reviewer for the NeurIPS workshop ML for the Developing World (ML4D) (virtual).
- 12/2021 Reviewer for the NeurIPS Datasets and Benchmarks track.
- 03/2021 Paper presentation at the ACM conference on Fairness, Accountability, and Transparency (FAcct) (virtual).
- 03/2021 Reviewer for the ACM conference on Fairness, Accountability, and Transparency (FAcct) (virtual).
- 12/2020 Paper presentation at the NeurIPS workshop Algorithmic Fairness through the Lens of Causality and Interpretability (virtual).
- 12/2020 Program Committee/Reviewer for the NeurIPS workshop ML for the Developing World (ML4D) (virtual).
- 08/2020 Paper presentation at the Joint Statistical Meetings (virtual).
- 04/2020 Presentation at the CMU Symposium on AI and Social Good in Pittsburgh, PA.
- 01/2020 Paper presentation by colleague Amanda Coston at the ACM Conference on Fairness, Accountability, and Transparency (FAT*) in Barcelona, Spain.
- 12/2019 Paper/poster presentation at NeurIPS workshop ‘‘Do the Right Thing’: Machine Learning and Causal Inference for Improved Decision Making in Vancouver, Canada.
- 12/2019 Program Committee/Reviewer for the NeurIPS workshop ML for the Developing World (ML4D) in Vancouver, Canada.
- 12/2019 Travel grant for NeurIPS in Vancouver, Canada.
- 05/2019—08/2019: Data Scientist internship at Google in Mountain View, CA.
- 05/2019 Poster presentation at the Atlantic Causal Inference Conference in Montreal, CA.
- 04/2019 Paper presentation at SIGBOVIK in Pittsburgh, PA.
- 01/2019: Poster presentation at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) in Honolulu, HA.
- 01/2019: Travel grant for the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) in Honolulu, HA.
- 07/2018: Paper presentation at the Doctoral Consortium at the Educational Data Mining conference in Buffalo, NY.
- 06/2018—08/2018: Data Scientist internship at Google in New York City.
- 06/2018: Poster presentation at the Classification Society Meeting in Stony Brook NY.
- 05/2018—08/2018: Data Science internship at Box in Redwood City, CA.
- 05/2018: Poster presentation at the Atlantic Causal Inference Conference in Pittsburgh, PA.
- 11/2017: National finalist at the Citadel Data Open in New York, NY.
- 09/16/2017: Winner of the Citadel Data Open datathon at Carnegie Mellon, with fellow students Nic Dalmasso, Kwangho Kim, and Chirag Nagpal. (550+ student applications, around 125 students selected to compete)
Papers
(2025).
Auditing and Enforcing Conditional Fairness via Optimal Transport.
Proceedings of the AAAI Conference on Artificial Intelligence. [Authors 1-4 contributed equally].
(2024).
Semiparametric Efficient Inference in Adaptive Experiments.
Proceedings of the Third Conference on Causal Learning and Reasoning.
(2024).
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing.
Proceedings of the AAAI Conference on Artificial Intelligence.
(2024).
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access.
Transactions on Machine Learning Research [Authors 2-4 contributed equally].
(2023).
Active Learning with Missing Not At Random Outcomes.
NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World.
(2023).
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss.
Transactions on Machine Learning Research [Authors 2-4 contributed equally].
(2022).
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes.
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT).
(2021).
Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.
American Journal of Epidemiology.
(2021).
Comment on “Statistical Modeling: The Two Cultures” by Leo Breiman.
Observational Studies [Authors 1-2 contributed equally].
(2021).
Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds.
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT).
(2016).
Memory and Language Improvements following Cognitive Control Training.
Journal of Experimental Psychology: Learning, Memory, and Cognition.
(2016).
Modeling Triage Decision Making.
Proceedings of the 38th Annual Meeting of the Cognitive Science Society.
(2016).
The bilingual advantage: Conflict monitoring, cognitive control, and garden-path recovery.
Cognition.
(2013).
Contrasting interference profiles for agreement and anaphora: Experimental and modeling evidence.
Journal of Memory and Language.
(2012).
Evidence for language transfer leading to a perceptual advantage for non-native listeners.
Journal of the Acoustical Society of America.