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Background

Sara Kohtz is an assistant professor in the School of Systems Science and Industrial Engineering at °®¶¹´«Ã½. She received her bachelor’s (2016) and master’s degrees (2017) in industrial engineering at °®¶¹´«Ã½. She completed her PhD in industrial engineering from the University of Illinois at Urbana-Champaign in 2024.

Her work encompasses machine learning theory and applications to high-impact engineered systems, and her dissertation research is focused on physics-informed machine learning for energy systems, specifically applications of prognostics and health management, reliability engineering, design optimization and sensor placement.

Her present work includes meshing multidisciplinary fields to develop novel machine-learning methodologies for engineering applications, specifically fault diagnosis in high power electrified systems.

She is a member of ASME, IISE and the Society of Women Engineers (SWE).

Education

• PhD: Industrial Engineering – University of Illinois at Urbana-Champaign

• MS: Industrial Engineering – °®¶¹´«Ã½

• BS: Industrial Engineering – °®¶¹´«Ã½

Research Interests

• Data science
• Physics-informed machine learning
• Reliability engineering
• Prognostics and health management
• Optimal system design
• Energy systems