Ilias Bilionis

Bilionis obtained his Diploma in Applied Mathematics and Physical Sciences from the National Technical University of Athens in 2008. In 2013, he obtained his Ph.D. in Applied Mathematics from Cornell University. After graduation, he spent a year working as a postdoctoral researcher at the Mathematics and Computer Science Division of Argonne National Laboratory. In August 2014, he became faculty of Mechanical Engineering at Purdue University, where he established the Predictive Science Laboratory (PSL). The mission of Bilionis' group is to create AI technologies that accelerate the pace of engineering innovation with particular emphasis on probabilistic approaches (thought of as an extension of logic under uncertainty) with an additional layer of causality (expressed through physical laws and graphical models). The current applications of PSL span the range between purely technical systems (e.g., electric machines, high-performance materials) and sociotechnical (e.g., smart buildings, extra-terrestrial habitats). Research sponsors include NSF, NASA, DARPA, Purdue University, University of Illinois UC, and Ford.
Bilionis has published more than 35 journal papers, 2 book chapters, and several conference papers. He has been very active advising Master and Ph.D. students, and in 2019 he was presented with the “Outstanding Faculty Mentor of Mechanical Engineering Graduate Students” award. Furthermore, he enjoys teaching and has received the “Outstanding Engineering Teacher Recognition” three times.

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Introduction to Scientific Machine Learning (edX)

Aug 21st 2023
Introduction to Scientific Machine Learning (edX)
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Learn the basics of machine learning with hands-on practical examples on engineering applications. This course provides an introduction to data analytics for individuals with no prior knowledge of data science or machine learning. The course starts with an extensive review of probability theory as the language of uncertainty, discusses [...]