Utku Evci
Research Scientist at Google DeepMind
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolUtku Evci is a research scientist at Google DeepMind with 11 years of experience building and shipping machine learning systems, currently focused on improving learning algorithms. A Fulbright M.Sc. graduate from NYU Courant, he has a strong track record at Google Brain and as an AI resident, contributing to high-profile open-source projects like Scenic (JAX computer vision) and Dopamine (reinforcement learning), where he worked on model implementations, checkpointing, and tf.keras refactors. His background spans both theory and engineering — from debugging meta-learning data pipelines to implementing robust model initialization and deployment fixes — showing an unusual blend of research rigor and production-grade ML engineering. Based in Montreal, he combines deep academic credentials with practical contributions to widely used research frameworks, and his GitHub history reflects a knack for subtle but critical infrastructure improvements that prevent runtime failures.
11 years of coding experience
4 years of employment as a software developer
Master of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at New York UniversityExchange Student Elektrik ve Elektronik Mühendisliği, Exchange Student Elektrik ve Elektronik Mühendisliği at Technical University of MunichBachelor of Science (B.Sc.) Computer Engineering, Bachelor of Science (B.Sc.) Computer Engineering at Koç UniversityExchange Student Computer Engineering, Exchange Student Computer Engineering at Nanyang Technological University Singapore
Turkish, İngilizce, German, İtalyanca
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (21)
transformers10
python10
machine-learning10
reinforcement-learning10
flax10
keras10
deeplearning-ai10
deep-learning10
tensorflow10
resnet10
ai10
computer-vision10
jax10
data-augmentation10
ata9
Programming languages (5)
Github contributions (5)
google-research/meta-dataset
Apr 2019 - Feb 2022
A dataset of datasets for learning to learn from few examples
Role in this project:
ML Engineer
ML EngineerContributions:38 commits, 1 PR, 9 pushes in 2 years 10 months
Contributions summary:Utku's commits primarily focused on debugging and improving the data pipeline within the meta-dataset project, specifically addressing issues related to data augmentation and episode sampling. These changes involved modifications to the code related to gin configurations for episode descriptions, as well as the implementation of runtime error handling to ensure the proper functioning of the data sampling methods. The commits showcase a detailed understanding of the data processing and sampling logic within the context of meta-learning.
meta-learningdatasetmachine-learningbenchmarklearning-to-learn
google/dopamine
Aug 2019 - Sep 2022
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 1 comment in 3 years 2 months
Contributions summary:Utku primarily focused on implementing and updating machine learning models within the Dopamine framework, as evidenced by the addition of new Keras-based models for Atari environments. They also refactored the network backbone to use `tf.keras` and updated existing agent implementations like DQN and Rainbow. Furthermore, the user's contributions involved addressing initialization issues and test suite enhancements for Rainbow and Implicit Quantile Networks.
reinforcement-learning-algorithmsfast-prototypinggooglereinforcement-learningprototyping
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