Miroslav Dudik - Senior Principal Researcher at Microsoft

Miroslav Dudik

Senior Principal Researcher at Microsoft

New York, New York, United States

Join Prog.AI to see contacts

Summary

🤩

Rockstar

🎓

Top School

Miroslav Dudik is a Senior Principal Researcher at Microsoft with over 8 years of industry experience and a long academic pedigree including a PhD in Computer Science from Princeton. He has deep expertise in machine learning research, particularly on fairness in ML—evidenced by early contributions to the well-known Fairlearn project where he implemented foundational Lagrangian reduction methods and solver compatibility fixes. His career blends academic rigor from postdoctoral work at Carnegie Mellon with practical research roles at Yahoo! and IBM, enabling him to move ideas from theory to production. Based in New York, he combines a strong engineering background with a track record of building reusable tooling and reproducible research. Colleagues describe him as a pragmatic researcher who anticipates deployment challenges while advancing principled approaches to algorithmic fairness.

8 years of coding experience

4 years of employment as a software developer

Computer Science, Computer Science at Univerzita Komenského v Bratislave

California Institute of Technology

PhD, Computer Science, PhD, Computer Science at Princeton University

Github Skills (10)

scikit10

scikit-learn10

machine-learning10

python10

binary-classification9

algorithm9

algorithms9

ai8

Programming languages (4)

TypeScriptHTMLJupyter NotebookPython

Github contributions (5)

fairlearn/fairlearn

May 2018 - Jan 2023

A Python package to assess and improve fairness of machine learning models.

Role in this project:

Data Scientist

Contributions:386 reviews, 49 commits, 89 PRs in 4 years 8 months

Contributions summary:Miroslav contributed initial material to the repository, likely setting up the foundational elements of the project. Their work included adding a Python file (`fairlearn/classred.py`) related to Lagrangian reduction for fair binary classification, indicating a focus on fairness in machine learning. The user also modified initialization and handling of constraints and added support for PyPI. Further commits fixed bugs and updated the LP solver for backward compatibility.

python-packagefairness-assessmentfairness-mlpythongroup-fairness

MiroDudik/fairlearn

Mar 2020 - Jan 2023

A Python package that implements a variety of algorithms that mitigate unfairness in supervised machine learning.

Contributions:3 reviews, 127 pushes, 26 branches in 2 years 10 months

pythonsupervisedmachine-learningvarietypython-package

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial