Miruna Oprescu
PHD Candidate
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMiruna Oprescu is an Applied Scientist and CS PhD student at Cornell Tech with 10 years of experience in data science and machine learning, formerly a Senior Data and Applied Scientist at Microsoft Research based in New York. She specializes in causal inference and heterogeneous treatment effect estimation, contributing to Microsoft Research’s econml toolkit by prototyping and implementing core Orthogonal Random Forest components (OrthoTree, BaseOrthoForest, DishonestOrthoForest). Her work goes beyond algorithms to harden implementations—fixing bugs, ensuring correct model weighting, and improving robustness for production use. She bridges econometrics and modern ML, translating research-grade methods into reliable tools for real-world economic and policy decision making.
10 years of coding experience
7 years of employment as a software developer
Bachelor of Arts (B.A.), Physics and Mathematics, Secondary in Computer Science, 3.75, Bachelor of Arts (B.A.), Physics and Mathematics, Secondary in Computer Science, 3.75 at Harvard CollegeOvidius High SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Cornell University
Romanian, English, French, Spanish
Github Skills (7)
causal10
machine-learning10
causality10
econometrics10
python10
scikit-learn9
scikit9
Programming languages (4)
Github contributions (5)
py-why/EconML
Jun 2018 - Aug 2021
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:83 reviews, 81 commits, 66 PRs in 3 years 2 months
Contributions summary:Miruna contributed significantly to the development of the `econml` toolkit, focusing on the implementation of the Orthogonal Random Forest (ORF) algorithm for heterogeneous treatment effect estimation. They created the prototype and implemented the core components including `OrthoTree`, `BaseOrthoForest`, and `DishonestOrthoForest`. Furthermore, they worked on refining the implementation to fix bugs and address issues like ensuring proper weighting of the models and improving the robustness and reliability of the overall model.
decision-makingfairness-mlpythonoutcomeproblems
Azure-Samples/hdinsight-pyspark-cntk-integration
Feb 2017 - Jul 2018
Instructions and examples for installing CNTK on an HDInsight cluster and running CNTK-Pyspark applications from Jupyter notebooks.
Contributions:14 commits, 4 PRs, 11 pushes in 1 year 5 months
hdinsightpythoninstallinginstructionsconnector
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