Ilias Bilionis
Professor at Purdue University
West Lafayette, Indiana, United States
Summary
👤
Senior🎓
Top SchoolIlias Bilionis is a Professor at Purdue University with 12 years of experience blending applied mathematics, uncertainty quantification, and scientific machine learning to tackle complex engineered systems from electric motors to extra-terrestrial habitats. He holds a PhD in Applied Mathematics from Cornell and has translated foundational research into practical inverse-problem and calibration work at Argonne National Lab and in climate, energy, and medical-device applications. His research sits at the intersection of physics, probability, and data, developing principled algorithms that handle expensive computer models and bring predictive capability to real-world systems. An active contributor to open-source ML tools, he has improved reliability and usability in the widely used GPy Gaussian processes library, notably enhancing MCMC and transformation plotting. Based in West Lafayette, he leads interdisciplinary teams at the Predictive Science Lab to push scientific ML from theory toward deployable solutions. Colleagues note his uncommon blend of rigorous theory, software craftsmanship, and applied engineering focus.
12 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD), Applied Mathematics, 4/4, Doctor of Philosophy (PhD), Applied Mathematics, 4/4 at Cornell UniversityDiploma, Applied Mathematics, 9.3/10, Diploma, Applied Mathematics, 9.3/10 at National Technical University of Athens
Greek, English
Github Skills (8)
machine-learning10
python10
numpy10
gaussian-processes10
matplotlib9
mcmc8
scipy8
testing7
Programming languages (5)
Github contributions (5)
SheffieldML/GPy
Aug 2015 - Aug 2015
Gaussian processes framework in python
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 1 PR, 1 comment in 7 days
Contributions summary:Ilias primarily contributed to the `gpy` repository, a Gaussian processes framework in Python, by addressing bugs and enhancing the functionality of transformation plots and PDF handling. They fixed issues related to plotting and the MCMC sampler, and implemented features such as the calculation of Jacobians. Their work also involved adding comments, beautifying the code, and refining the MCMC sampler functionality, demonstrating a focus on improving the library's reliability and usability for machine learning applications.
gaussiangaussian-processespython
PurdueMechanicalEngineering/me-239-intro-to-data-science
Jul 2020 - May 2022
Contributions:143 commits, 135 pushes, 1 branch in 1 year 9 months
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