Miruna Oprescu

PRODiG+ Fellow in Computer Science, Stony Brook University

Ph.D. in Computer Science, Cornell University

Trustworthy AI • Causal Inference • Machine Learning

miruna [AT] cs.stonybrook.edu

About

I am a SUNY PRODiG+ Fellow in the Department of Computer Science at Stony Brook University, with a planned tenure-track transition in 2028 following successful review. I earned my Ph.D. in Computer Science at Cornell University, advised by Nathan Kallus. My doctoral work was supported by a DOE Computational Science Graduate Fellowship. Previously, I was a Senior Data and Applied Scientist at Microsoft Research, where I made foundational contributions to EconML, an open-source Python library for causal machine learning. I hold an A.B. in Physics and Mathematics from Harvard University.

I'm currently building the Trustworthy Inference, Learning, Decision-making, and Evaluation (TILDE) Lab.

Interested in joining TILDE Lab?

I'm looking for motivated undergraduate, master's, and Ph.D. students to help build TILDE Lab! If your interests overlap with our research directions, please get in touch using the guidelines below.

Current Stony Brook students: Email me your CV, degree level (undergraduate, master's, or Ph.D.), program and year, and a few sentences about your research interests and relevant experience.

Prospective Ph.D. students: Apply through Stony Brook's Computer Science Ph.D. admissions process and mention my name in your application. You're also welcome to email me your CV, intended entry year, and a brief description of your interests.

Contact: miruna@cs.stonybrook.edu (subject: “TILDE Lab Research Interest”).

TILDE Lab

We develop principled machine learning methods for settings where data are imperfect, assumptions are uncertain, and actions shape future observations. Our interests span three connected themes:

Inference: What can we reliably learn from imperfect evidence? Causal inference, statistical machine learning, and uncertainty quantification, including learning from observational, experimental, and structured data. Examples: [ICML'26] and [NeurIPS'25].
Decision-making: How should an agent act and learn through interaction? Reinforcement learning, adaptive experimentation, and policy evaluation, with an interest in learning from human feedback. Examples: [NeurIPS'25].
Trustworthiness: When can we trust the resulting conclusions and behavior? Robustness, reliable evaluation, and understanding the limits of what data can establish. Examples: [ICML'23] and [NeurIPS '24].

We are also interested in applying these methods to questions in healthcare, climate and Earth systems, and neuroscience—and we're always open to exploring new domains!

News

[Sep '26] I joined Stony Brook University's Department of Computer Science as a SUNY PRODiG+ Fellow and am building TILDE Lab! I'm looking for students interested in trustworthy learning, inference, and decision-making.
[Apr '26] I defended my Ph.D. thesis: Reliable Causal Inference Under Unreliable Assumptions: Machine Learning Methods for Observational, Quasi-Experimental, and Structured Data [slides].
[Apr '26] Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference was accepted at ICML 2026!
[Feb '26] New preprint: Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework.
[Jan '26] New preprint: Recovering Whole-Brain Causal Connectivity under Indirect Observation with Applications to Human EEG and fMRI.

Earlier news

[Oct '25] New preprint: Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference.
[Sep '25] Efficient Adaptive Experimentation with Noncompliance was accepted at NeurIPS 2025!
[Sep '25] GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding was accepted at NeurIPS 2025!
[Aug '25] Wrapped up my internship at Netflix with the Machine Learning Inference and Research group.
[Jul '25] My essay on using spatiotemporal causal modeling to study links between wildfire smoke and respiratory disease won the 2025 Communicate Your Science & Engineering (CYSE) contest; featured in DEIXIS Magazine.
[May '25] New preprint: Efficient Adaptive Experimentation with Noncompliance.
[Mar '25] Gave a talk at the SIAM Conference on Computational Science and Engineering (CSE25): Causal Inference for Spatiotemporal Interventions. Talk slides.
[Feb '25] New preprint: GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders.
[Nov '24] Gave a talk at the Workshop in Operations Research and Data Science (WORDS), Duke University: Reliable Treatment Effect Estimation Using Weak Instruments and Observational Data. Talk abstract.
[Sep '24] Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data was accepted at NeurIPS 2024!
[Sep '24] Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes was accepted at NeurIPS 2024!
[Aug '24] Wrapped up my internship at the Brookhaven National Lab, Computational Science Initiative.
[Aug '24] Passed my admission to candidacy exam (A Exam)! Talk slides.
[Jul '24] Presented my research at the 2024 DOE CSGF Annual Program Review in D.C. Poster and photo.
[Jun '24] New preprint: Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data.
[Mar '24] New preprint: Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes.
[Jan '24] Low-rank MDPs with Continuous Action Spaces was accepted at AISTATS 2024!
[Nov '23] New preprint: Low-Rank MDPs with Continuous Action Spaces.
[Sep '23] Gave a talk at the Brookhaven National Lab, Computational Science Initiative: Uncertainty Quantification in Causal Inference: Sharp and Efficient Bounds on Heterogeneous Causal Effects Under Hidden Confounding. Talk abstract.
[Jul '23] Presented my research at the 2023 DOE CSGF Annual Program Review in D.C. Poster.
[Apr '23] B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding was accepted at ICML 2023!
[Apr '23] New preprint: B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding.
[Jan '23] Robust and Agnostic Learning of Conditional Distributional Treatment Effects was accepted at AISTATS 2023!
[Aug '22] Finished my internship at Netflix with the Algorithms Engineering team.
[May '22] New preprint: Robust and Agnostic Learning of Conditional Distributional Treatment Effects.
[Apr '22] Awarded the Department of Energy Computational Science Graduate Fellowship for 2022–2026! Announcement.
[Feb '22] Named finalist of the 2022 Meta Ph.D. Research Fellowship. Announcement.

Publications

Most recent publications on Google Scholar.
* Equal contribution. ^‡ Alphabetical order.

Selected
Preprints
All

Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

Ayush Khot*, Miruna Oprescu*, Maresa Schröder, Ai Kagawa, Xihaier Luo

ICML'26: International Conference on Machine Learning. 2026.

Paper Arxiv Slides Code

Efficient Adaptive Experimentation with Noncompliance

Miruna Oprescu, Brian M Cho, Nathan Kallus

NeurIPS'25: Advances in Neural Information Processing Systems. 2025.

Paper Arxiv Slides Code

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding

Miruna Oprescu, David K. Park, Xihaier Luo, Shinjae Yoo, Nathan Kallus

NeurIPS'25: Advances in Neural Information Processing Systems. 2025.

Paper Arxiv Slides Code

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data

Miruna Oprescu, Nathan Kallus

NeurIPS'24: Advances in Neural Information Processing Systems. 2024.

Paper Arxiv Slides Poster Code

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes

Andrew Bennett, Nathan Kallus, Miruna Oprescu*, Wen Sun, Kaiwen Wang*

NeurIPS'24: Advances in Neural Information Processing Systems. 2024.

Paper Arxiv Poster Code

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

ICML'23: International Conference on Machine Learning. 2023.

Paper Arxiv Poster Code

Robust and Agnostic Learning of Conditional Distributional Treatment Effects

Nathan Kallus*, Miruna Oprescu*

AISTATS'23: International Conference on Artificial Intelligence and Statistics. 2023.

Paper Arxiv Poster Code

Estimating the Long-Term Effects of Novel Treatments

Keith Battocchi^‡, Eleanor Dillon^‡, Maggie Hei^‡, Greg Lewis^‡, Miruna Oprescu^‡, Vasilis Syrgkanis^‡

NeurIPS'21: Advances in Neural Information Processing Systems. 2021.

Paper Arxiv

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, Yifeng Wu, Paul Lo, Huigang Chen, Totte Harinen, Jeong-Yoon Lee

SIGKDD'21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Tutorial). 2021.

Slides

EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects

Miruna Oprescu, Vasilis Syrgkanis, Keith Battocchi, Maggie Hei, Greg Lewis

NeurIPS'19 Do the right thing: Machine Learning and Causal Inference for Improved Decision Making. 2019.

Paper Slides Poster Code

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis

NeurIPS'19: Advances in Neural Information Processing Systems. 2019. Spotlight.

Paper Arxiv Poster Code

Orthogonal Random Forest for Causal Inference

Miruna Oprescu*, Vasilis Syrgkanis*, Zhiwei Steven Wu*

ICML'19: International Conference on Machine Learning. 2019.

Paper Arxiv Poster Code

Recovering Whole-Brain Causal Connectivity under Indirect Observation with Appications to Human EEG and fMRI

Sangyoon Bae, Miruna Oprescu, David K. Park, Shinjae Yoo, Jiook Cha

Preprint. 2026.

Paper Arxiv

Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework

Maresa Schröder, Miruna Oprescu, Stefan Feuerriegel, Nathan Kallus

Preprint. 2026.

Paper Arxiv

Recovering Whole-Brain Causal Connectivity under Indirect Observation with Appications to Human EEG and fMRI

Sangyoon Bae, Miruna Oprescu, David K. Park, Shinjae Yoo, Jiook Cha

Preprint. 2026.

Paper Arxiv

Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework

Maresa Schröder, Miruna Oprescu, Stefan Feuerriegel, Nathan Kallus

Preprint. 2026.

Paper Arxiv

Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

Ayush Khot*, Miruna Oprescu*, Maresa Schröder, Ai Kagawa, Xihaier Luo

ICML'26: International Conference on Machine Learning. 2026.

Paper Arxiv Slides Code

Efficient Adaptive Experimentation with Noncompliance

Miruna Oprescu, Brian M Cho, Nathan Kallus

NeurIPS'25: Advances in Neural Information Processing Systems. 2025.

Paper Arxiv Slides Code

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding

Miruna Oprescu, David K. Park, Xihaier Luo, Shinjae Yoo, Nathan Kallus

NeurIPS'25: Advances in Neural Information Processing Systems. 2025.

Paper Arxiv Slides Code

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data

Miruna Oprescu, Nathan Kallus

NeurIPS'24: Advances in Neural Information Processing Systems. 2024.

Paper Arxiv Slides Poster Code

Low-rank MDPs with Continuous Action Spaces

Andrew Bennett*, Nathan Kallus*, Miruna Oprescu*

AISTATS'24: International Conference on Artificial Intelligence and Statistics. 2024.

Paper Arxiv Poster

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes

Andrew Bennett, Nathan Kallus, Miruna Oprescu*, Wen Sun, Kaiwen Wang*

NeurIPS'24: Advances in Neural Information Processing Systems. 2024.

Paper Arxiv Poster Code

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, Lester Mackey

NeurIPS'23: Advances in Neural Information Processing Systems. 2023.

Paper Arxiv Code Data

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

ICML'23: International Conference on Machine Learning. 2023.

Paper Arxiv Poster Code

Adaptive Bias Correction for Improved Subseasonal Forecasting

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey

Nature Communications. 2023.

Paper Arxiv Code

Robust and Agnostic Learning of Conditional Distributional Treatment Effects

Nathan Kallus*, Miruna Oprescu*

AISTATS'23: International Conference on Artificial Intelligence and Statistics. 2023.

Paper Arxiv Poster Code

Estimating the Long-Term Effects of Novel Treatments

Keith Battocchi^‡, Eleanor Dillon^‡, Maggie Hei^‡, Greg Lewis^‡, Miruna Oprescu^‡, Vasilis Syrgkanis^‡

NeurIPS'21: Advances in Neural Information Processing Systems. 2021.

Paper Arxiv

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, Yifeng Wu, Paul Lo, Huigang Chen, Totte Harinen, Jeong-Yoon Lee

SIGKDD'21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Tutorial). 2021.

Slides

Online Learning with Optimism and Delay

Genevieve E Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey

ICML'21: International Conference on Machine Learning. 2021.

Paper Arxiv Code

Multifactorial Model to Predict Response to PD-(L) 1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer

Kathryn Arbour, Miruna Oprescu, Joe Hakim, Hira Rizvi, Max Leiserson, Mark Ginsburg, Andrew Plodkowski, Jay Sauter, Isabel Preeshagul, Sharon Gillett, Philip Rosenfield, Lester Mackey, Miro Dudik, Matthew Hellmann

Journal of Thoracic Oncology (Extended Abstract). 2019.

Paper Arxiv

EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects

Miruna Oprescu, Vasilis Syrgkanis, Keith Battocchi, Maggie Hei, Greg Lewis

NeurIPS'19 Do the right thing: Machine Learning and Causal Inference for Improved Decision Making. 2019.

Paper Slides Poster Code

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis

NeurIPS'19: Advances in Neural Information Processing Systems. 2019. Spotlight.

Paper Arxiv Poster Code

Orthogonal Random Forest for Causal Inference

Miruna Oprescu*, Vasilis Syrgkanis*, Zhiwei Steven Wu*

ICML'19: International Conference on Machine Learning. 2019.

Paper Arxiv Poster Code

Vitæ

Full CV in PDF.

Stony Brook University 2026 - present

PRODiG+ Fellow
Computer Science Department
Cornell University 2021 - 2026

Ph.D. in Computer Science
Minor: Operations Research
Advisor: Nathan Kallus
Netflix May - Aug 2025

Machine Learning Research Intern
Machine Learning and Inference Research
Mentors: Nathan Kallus, David Hubbard
Cornell University Dec 2024

M.S. in Computer Science
Awarded enroute to Ph.D.
Brookhaven National Laboratory May - Aug 2024

Research Intern
Computational Science Initiative
Mentor: Shinjae Yoo
Netflix May - Aug 2022

Machine Learning Research Intern
Algirithms Engineering Group
Mentors: Sudeep Das, Aish Fenton
Microsoft Research 2017 - 2021

Senior Data and Applied Scientist
ALICE Team
Core Contributior to EconML
Microsoft 2015 - 2017

Software Development Engineer
Cloud Machine Learning Group
Harvard University 2011 - 2015

Joint A.B. in Physics and Mathematics
Minor: Computer Science
Johns Hopkins University May - Aug 2014

Research Intern
Mentor: Suchi Saria

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