Sergül Aydöre
Staff Applied Scientist at Datadog
New York, New York, United States
Summary
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Senior🎓
Top SchoolSergül Aydöre is a Staff Applied Scientist in New York with 11 years of experience building large-scale AI systems that combine LLMs, retrieval, and memory to enable trustworthy autonomous reasoning and infrastructure automation. Her work at Datadog and AWS spans agentic systems, memory-augmented architectures, hallucination mitigation, and telemetry-driven reliability, with production contributions to Amazon Q, Amazon Personalize, AWS Clean Rooms, and Amazon Macie. She brings a strong research foundation—PhD in Electrical Engineering from USC and prior academic roles—to practical ML engineering, including contributions to scikit-learn that improved estimator robustness and performance. Comfortable moving models from research into production, she blends signal-processing rigor from her brain-signal and audio analysis work with cloud-scale engineering to reduce false positives and improve system interpretability.
11 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical Engineering, Doctor of Philosophy (Ph.D.), Electrical Engineering at University of Southern CaliforniaEskisehir Fatih Anadolu LisesiBS and MS, Electrical Engineering, BS and MS, Electrical Engineering at Boğaziçi University
English, Turkish, Greek
Github Skills (8)
scikit10
scikit-learn10
machine-learning10
python10
testing10
data-analysis9
statistics8
numpy8
Programming languages (9)
Github contributions (5)
scikit-learn/scikit-learn
May 2017 - Feb 2020
scikit-learn: machine learning in Python
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 6 PRs, 73 comments in 2 years 9 months
Contributions summary:Sergül contributed to the scikit-learn library by addressing several issues related to machine learning models. Their work included removing precomputed support in nearest centroid, optimizing mean computations in feature agglomeration, and adding tests for sample weights across various estimators. They also fixed issues and made minor changes to improve the code quality and performance.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
sergulaydore/EE551A_Fall_2018
Aug 2018 - Feb 2019
Contributions:51 commits, 45 pushes, 1 branch in 5 months
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