Adaptive Intelligent Materials & Systems Center
Doctoral Degrees
Awarded
– Developed data-driven and real-time in-flight safety monitoring techniques integrating multisource sensing, signal processing, and machine learning techniques for detecting unsafe operations (anomaly detection) and enhancing aviation safety (research supported by NASA)
– Focused on damage diagnosis in integrated circuit (IC) packages using non-destructive evaluation techniques including flash thermography, ultrasonic guided waves, and acoustic emission testing; developed data-driven damage diagnosis technique (research supported by Intel Corp.)
– Developed damage diagnosis techniques combining ultrasonic guided waves and machine learning algorithms to detect and classify fatigue damage in carbon fiber composite structures (research supported by ARL)
Linkedin: https://www.linkedin.com/in/hyunseong-lee-8262701bb
- After my PhD at ASU, I pursued at Postdoc at Lawrence Berkeley National Lab in California. Then I received a Marie Sklodowska-Curie Postdoctoral Fellowship from the European Commission, which brought me to TU Delft in the Netherlands.
- After successfully finishing up the fellowship, I took up my current position.
- Currently, working on understanding the degradation of polymers/plastics during recycling and how to mitigate this degradation.
- Developed damage localization, anomaly detection, and health monitoring algorithms for carbon fiber composite structures, integrated circuit packages, and commercial aircraft in AIMS.
- Created deep learning-based optical inspection systems with centralized analysis for high-volume smart manufacturing at Seagate Technology.
- Developed and implemented natural language processing, machine learning, and LLM/GenAI solutions to understand and address customer inquiries at Microsoft.
- Recieved the Dean’s Fellowship and then later was awarded the Army Research Lab (ARL) Journeyman Fellowship to support research
- Studied composites, with an emphasis on high temperature ceramic matrix composites (CMCs) and developed fracture mechanics-based damage models to capture the complex nonlinear behavior of woven CMCs.
- Currently, leveraging my composites and CMC experience at Raytheon, becoming a member of the Mechanical Analysis and Test department
- I currently work at the intersection of computational mechanics, material science, digital twin, and generative AI.
- I successfully led R&D efforts in material and process modeling for projects funded by U.S Army ERDC and ARL.
- I developed and deployed production ready simulation tools for metal binder jet manufacturing of aerospace components for weight reduction and cost savings.
- I also developed multiscale models to accelerate the development of multifunctional polymer composites for injection molding and polymer additive manufacturing.
- I co-authored in a few journals and presented at technical conferences.
- Contributed to 20 research projects and advised 27 graduate students at the University of Oklahoma
- Received the Vice President for Research and Partnerships Annual Award for Excellence in Research Grants, The University of Oklahoma, 2024 – 2022
- Received William H. Barkow Presidential Professorship, The University of Oklahoma, 2022
- Developed data-driven and real-time in-flight safety monitoring techniques integrating multisource sensing, signal processing, and machine learning techniques for detecting unsafe operations (anomaly detection) and enhancing aviation safety (research supported by NASA)
- Focused on damage diagnosis in integrated circuit (IC) packages using non-destructive evaluation techniques including flash thermography, ultrasonic guided waves, and acoustic emission testing; developed data-driven damage diagnosis technique (research supported by Intel Corp.)
- Developed damage diagnosis techniques combining ultrasonic guided waves and machine learning algorithms to detect and classify fatigue damage in carbon fiber composite structures (research supported by ARL)
Linkedin: https://www.linkedin.com/in/hyunseong-lee-8262701bb