Emre Kıcıman
Head Of Research, Copilot Tuning Partner Research Manager at Microsoft
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolEmre Kıcıman is Head of Research for Copilot Tuning at Microsoft and a long-time Microsoft Research scientist who bridges causal inference, machine learning, and real-world productivity applications. With a PhD from Stanford and a BS from UC Berkeley, he leads partner research on model innovations that shape how AI augments workplace tasks while keeping human and societal impacts central. His work combines deep technical rigor—reflected in contributions to the DoWhy causal-inference community through documentation and example curation—with practical leadership driving adoption of tuned models in product settings. Based in Seattle, he is known for translating complex causal and ML ideas into actionable research partnerships and accessible tools for engineers and researchers.
7 years of coding experience
BS, Electrical Engineering and Computer Science, BS, Electrical Engineering and Computer Science at UC BerkeleyPhD, Computer Science, PhD, Computer Science at Stanford University
Turkish
Github Skills (8)
read-me10
python10
documentation10
jupyter-notebook9
machine-learning4
causality4
causal4
data-science4
Programming languages (5)
Github contributions (5)
py-why/dowhy
Jun 2018 - Oct 2022
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
Technical Writer & Documentation Specialist
Technical Writer & Documentation SpecialistContributions:82 reviews, 15 commits, 44 PRs in 4 years 4 months
Contributions summary:Emre primarily focused on updating and maintaining the project's documentation, particularly the README.rst and example notebooks. They fixed broken links, corrected video tutorial references, and added section numbering to the example notebooks for improved readability. Furthermore, the user removed and updated test files, ensuring the project's examples run and are functional. This indicates a focus on improving user experience and maintaining the accuracy and completeness of project documentation.
fairness-mlcausal-modelspythoncausalbayesian-networks
py-why/pywhyllm
Jul 2023 - Mar 2025
Experimental library integrating LLM capabilities to support causal analyses
Contributions:15 reviews, 14 PRs, 15 pushes in 1 year 8 months
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.