avatar for Ilias Bilionis

Ilias Bilionis

Purdue University
Assistant Professor
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 an Assistant Professor of Mechanical Engineering at Purdue University, where he established the Predictive Science Laboratory (PSL). The mission of PSL is to understand how to optimally design engineering systems (ES) under uncertainty. The applications of PSL span the range between technical (e.g., oil production, power system operation, electric machines) and sociotechnical systems (e.g., efficient office and residential buildings, extra-terrestrial habitats). His research has been funded by NSF, NASA, DARPA, Ford, Facebook, Purdue University, and the University of Illinois. His collaborative research programs have been awarded a total of $23.7M.
Bilionis’ research develops communication channels between theories and between theories and data. The communication protocol is based on probability theory (thought of as an extension of logic under uncertainty) with an additional layer of causality (expressed through physical laws in the form of differential equations and graphical models). Due to the high cost of information acquisition, engineering applications are characterized by small numbers of observations, e.g., how many times can one test an aircraft in a wind tunnel? In this regime, standard statistical/machine learning techniques fail because they cannot extrapolate beyond observations. This is where PSL operates. It develops methods that exploit physical knowledge to predict beyond observations with limited data.
Bilionis’ has published 34 journal papers, 2 book chapters, and 15 conference papers. He has chaired/co-chaired 19 Master and Ph.D. student committees and was presented with the “Outstanding Faculty Mentor of Mechanical Engineering Graduate Students” award. He has developed a graduate course on uncertainty quantification which seeks to introduce engineers to probabilistic thinking and machine learning directly on engineering applications. Bilionis provides code for all concepts discussed in class in the form of incomplete Python notebooks. Video lectures are available through nanoHUB. Based on his teaching evaluations, he has received the “Outstanding Engineering Teacher Recognition” three times.

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