- Program Year: 1
- Academic Institution: Cornell University
- Field of Study: Computer Science
- Academic Advisor: Nathan Kallus
Practicum Not Yet Completed
A.B. Physics and Mathematics, Harvard University, 2015
Summary of Research
Machine learning has the potential to automate decision-making in a multitude of domains. One pitfall of our current approaches is that making robust decisions from data requires isolating causal mechanisms, whereas current machine learning solutions focus primarily on predictive modeling from correlations. The lack of causal approaches in most data modeling efforts reduces algorithms' ability to generalize, decreases model interpretability, increases susceptibility to adversarial attacks, and impacts algorithmic fairness.
My research lies at the intersection of machine learning, causal inference and robust data-driven decision making. I am interested in building methods for credible decision making in a variety of contexts, including observational, sequential (online), and interactive (reinforcement learning) settings. My end goal is to improve robustness and interpretability of machine learning models by incorporating causal thinking into our modeling pipelines. This is the first step towards trustworthy, secure, and fair automated decision making.
N. Kallus and M. Oprescu. "Robust and agnostic learning of conditional
distributional treatment effects". arXiv preprint arXiv:2205.11486 (2022). In Submission.
S. Mouatadid, P. Orenstein, G. Flaspohler, J. Cohen, M. Oprescu, E. Fraenkel, and L. Mackey. Adaptive Bias Correction for Improved Subseasonal Forecasting. arXiv preprint arXiv:2209.10666 (2022). In Submission.
K. Battocchi, E. Dillon, M. Hei, G. Lewis, M. Oprescu, V. Syrgkanis. "Estimating the Long-Term Effects of Novel Treatments". In Advances in Neural Information Processing Systems, 2021.
S. Mouatadid, P. Orenstein, G. Flaspohler, M. Oprescu, J. Cohen, et al. "Learned Benchmarks for Subseasonal Forecasting". arXiv preprint arXiv:2109.10399 (2021). In Submission.
G. Flaspohler, F. Orabona, J. Cohen, S. Mouatadid, M. Oprescu, P. Orenstein, L. Mackey. "Online Learning with Optimism and Delay". In International Conference on Machine Learning, PMLR, 2021.
V. Syrgkanis, G. Lewis, M. Oprescu, M. Hei, K. Battocchi, et al. "Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber". Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021.
V. Syrgkanis, V. Lei, M. Oprescu, M. Hei, K. Battocchi, G. Lewis. "Machine learning estimation of heterogeneous treatment effects with instruments". In Advances in Neural Information Processing Systems, 2019. Spotlight presentation.
M. Oprescu, V. Syrgkanis, K. Battocchi, M. Hei, and G. Lewis. "EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects". In CausalML Workshop, NeurIPS, 2019. Spotlight presentation.
M. Oprescu, V. Syrgkanis, Z. S. Wu. "Orthogonal random forest for causal inference". In International Conference on Machine Learning, 2019.
K. Arbour, M. Oprescu, J. Hakim, et al. "Multifactorial Model to Predict Response to PD-(L) 1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer". Journal of Thoracic Oncology, 2019.
M. Hamilton, S. Raghunathan, M. Oprescu et al. "Flexible and Scalable Deep Learning with MMLSpark". In International Conference on Predictive Applications and APIs, 2018.
Meta PhD Research Fellowship Finalist, 2022
cum laude, Harvard University, 2015
High Honors, Harvard University Physics Department, 2015
Derek C. Bok Award for Distinction in Teaching (Data Science), Harvard, 2014
Excellence in Summer Research Award, Johns Hopkins University, 2014