Ilias Bilionis
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Professor of Mechanical Engineering
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About
Dr. Ilias Bilionis is an Associate Professor at Purdue University in the United States. His research interests encompass complex concentrated alloys, machine learning applications in physics, and Bayesian methods for data analysis in dynamical systems. He has published recent articles on topics such as high-hardness alloys, uncertainty quantification in modeling physical systems, and eco-feedback interventions in energy conservation. Dr. Bilionis's work integrates experimental data with advanced computational techniques to address challenges in materials science and energy management.
Recent Grants
Grant: Close
CPS: Medium: A meta-learning approach to enable autonomous buildings
Open Date: 2021-07-01
Close Date: 2024-06-30
Grant: Close
DMREF: Discovery of high-temperature, oxidation-resistant, complex, concentrated alloys via data science driven multi-resolution experiments and simulations
Open Date: 2019-10-01
Close Date: 2023-09-30
Grant: Close
Workshop and Grantees Meeting: Positioning Engineering Design and Systems Engineering Research for Sustained Societal Impact; West Lafayette, Indiana; October 7-8, 2019
Open Date: 2019-08-01
Close Date: 2021-07-31
Grant: Close
AMPS: Collaborative Research: Efficient Algorithms for Ultra-Fast Detection of Power System Contingencies in the Transient Regime
Open Date: 2018-08-15
Close Date: 2022-07-31
Grant: Close
SCC-IRG Track 1: Sociotechnical Systems to Enable Smart and Connected Energy-Aware Residential Communities
Open Date: 2018-01-01
Close Date: 2022-12-31
Positions (1)
Ilias Bilionis
Purdue University
Purdue University is seeking a postdoctoral researcher for the ICoN Programmable Cloud Lab AI effort, led by Ilias Bilionis and Tianyi Zhang . The project focuses on agentic AI for scientific settings, especially systems that can interact safely and reliably with the physical world, including scientific instruments and laboratory systems. The research topics include harness engineering for scientific AI agents , curating and evaluating reusable agent skills , connecting coding agents with instruments , designing safe and auditable systems for autonomous experimentation , and developing domain-specific languages for scientific workflows . The post is especially relevant for candidates interested in AI for science, autonomous labs, scientific machine learning, and trustworthy AI systems. Applicants should have a PhD in engineering, computer science, applied mathematics, mathematics, physics, materials science, or a related scientific field. Strong experience with agentic AI , particularly coding agents, is preferred. Helpful background includes harness engineering, tool use, agent evaluation, scientific automation, and laboratory instrumentation. Knowledge of category theory is highlighted as an exceptional qualification. The position offers a collaborative environment spanning AI, experimentation, instrumentation, data, and physical modeling. The post also notes that Alejandro Strachan and Arun Mannodi Kanakkithodi are hiring a related postdoc on the same project with more emphasis on materials science expertise. Interested candidates should use the application link provided in the post. No deadline or funding amount is specified in the announcement.
Articles (15)
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2021
Collaborators (7)
Xiaoxu Zhong
Stanford University
Sayantan Bhattacharya
Assistant Professor
University of Maryland Baltimore County
Panagiota Karava
Purdue University
Shirley Dyke
Professor
Purdue University
Torsten Reimer
Professor
Purdue University
Jongseong Choi
Assistant Professor
State University of New York, Korea
Tatiana Ringenberg
Assistant Professor at Purdue University
Purdue University
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