SEAD Lab Presented in ASME IDETC-CIE Conference

This August, SEAD Lab actively contributed to the ASME IDETC-CIE Conference, one of the leading international forums for Design and Computers & Information in Engineering.

Our presentations included:

Jinyang Li & Jie Chen (2025). An Integer Approximation for the Discrete Fourier Transform Matrix for Fast Time-Series Data Analysis.

Jun Li & Jie Chen (2025). Bayesian Hierarchical Models and Neural Networks for Defect-Based Fatigue Prediction in Additive Manufacturing.

Jie Chen, Pengfei Ou, Yuxin Chang, Hengrui Zhang, Xiaoyan Li, Edward Sargent & Wei Chen (2025). Adaptive Uncertainty-Aware Deep Learning for Materials Discovery with High-Dimensional Design Inputs.

These talks highlight SEAD Lab’s ongoing work at the intersection of machine learning, uncertainty quantification, and engineering design, showcasing both methodological advances and applications in materials and manufacturing.