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publications

Machine and Deep Learning for Coating Thickness Prediction using Lamb Waves

Published in Wave Motion, 2023

Machine and deep learning for nondestructive coating thickness classification from Lamb wave dispersion maps.

Recommended citation: Maximilian Schmitz, Jin-Yeon Kim, Laurence J. Jacobs, Machine and deep learning for coating thickness prediction using Lamb waves, Wave Motion, Volume 120, 2023, 103137, ISSN 0165-2125, https://doi.org/10.1016/j.wavemoti.2023.103137. https://www.sciencedirect.com/science/article/abs/pii/S0165212523000239

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

Published in SPIE Smart Structures + Nondestructive Evaluation, 2024

LSTM and CNN framework for predicting interfacial bond quality and delamination in coated plates from guided wave data.

Recommended citation: Junzhen Wang, Maximilian Schmitz, Laurence J. Jacobs, and Jianmin Qu, Deep learning-based prediction of interfacial conditions in coated plates using guided waves, Proc. SPIE 12951, Health Monitoring of Structural and Biological Systems XVIII, 129511F (9 May 2024); https://doi.org/10.1117/12.3010200. https://doi.org/10.1117/12.3010200

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

Published in Ultrasonics, 2024

Deep learning NDE for locating and sizing coating delaminations from ultrasonic guided wave time-space images.

Recommended citation: Junzhen Wang, Maximilian Schmitz, Laurence J. Jacobs, Jianmin Qu, Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves, Ultrasonics, Volume 141, 2024, 107351, ISSN 0041-624X, https://doi.org/10.1016/j.ultras.2024.107351. https://doi.org/10.1016/j.ultras.2024.107351

Surviving the Paper Deluge: A One-Year Study in Learning From Demonstration [Science and Technology Watch]

Published in IEEE Robotics & Automation Magazine, 2026

Curated one-year survey of learning from demonstration research, distilling trends, key results, and open challenges across the recent LfD literature.

Recommended citation: A. Billard, R. Detry, N. Figueroa, M. Foriest, D. Lee and K. Yao, "Surviving the Paper Deluge: A One-Year Study in Learning From Demonstration [Science and Technology Watch]," in IEEE Robotics & Automation Magazine, vol. 33, no. 2, pp. 205-211, June 2026, doi: 10.1109/MRA.2026.3682522. https://ieeexplore.ieee.org/abstract/document/11563978

teaching

Guest lecture on Electronic Dance Music Production

Guest lecture, Georgia Tech, School of Music, 2021

Hold invited guest lecture on production of Electronic Dance Music (EDM) within the class MUSI6103 by Prof. Nat Condit-Schultz at the Georgia Institute of Technology. EDM is a popular field of music, though the spread and popularity is limited in the US. My talk helped to give an understanding of what EDM is, what it sounds like, and what fundamental production methods exist.

Student Project Supervision

Student Supervision, EPFL, Learning Algorithms and Systems Laboratory (LASA), 2025

Supervision of Bachelor and Master student projects on topics related to transfer learning for robotics and learning from demonstration at EPFL LASA.