Giannis Daras
Postdoctoral Associate
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolGiannis Daras is a Postdoctoral Associate at MIT with a PhD from UT Austin and roughly a decade of experience bridging machine learning research and software engineering. His work focuses on generative models, especially learning from limited, corrupted, and out-of-distribution data, with clear interest in translating these advances to scientific and practical domains. He has industry research experience at Google and contributed to spaCy and the popular thinc library—implementing and refactoring PyTorch multi-headed attention and adding visualization features. Earlier roles include co-founding a fintech startup and engineering contributions across NLP tooling and web development, reflecting a blend of entrepreneurship and hands-on systems work. Based in Austin, he pairs deep theoretical training with pragmatic implementation skills, often surfacing instrumentation and visualization to make model behavior more interpretable.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The University of Texas at AustinHigh School, High School at Model Experimental Lyceum of AnavrytaHigh School, High School at Model Experimental Gymnasium of AnavrytaEngineer’s Degree Electrical and Computer Engineering, Engineer’s Degree Electrical and Computer Engineering at National Technical University of Athens
English, German, Greek
Github Skills (9)
pytorch10
machine-learning10
deep-learning10
python10
nlp9
artificial-intelligence8
functional-programming7
tensorflow5
jax4
Programming languages (6)
Github contributions (5)
explosion/thinc
Apr 2019 - Jun 2019
đź”® A refreshing functional take on deep learning, compatible with your favorite libraries
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 1 PR in 1 month
Contributions summary:Giannis primarily focused on implementing and refactoring a PyTorch-based multiheaded attention layer within the `thinc` library. Their work involved integrating a tested implementation of the PytorchMultiHeadedAttention module, adding attention visualization capabilities, and refactoring existing attention mechanisms. The user's commits also included modifications to support the visualization features within the attention layer and refactoring of the attention code.
pythonfunctional-programmingtensorflowtype-checkingnatural-language-processing
giannisdaras/thinc
Dec 2018 - May 2019
đź”® spaCy's Machine Learning library for NLP in Python
Contributions:304 commits, 1 PR, 221 pushes in 5 months
nlppythonmachine-learningspacymachine-learning-library
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