Daehan Won

Daehan Won is an Associate Professor at the School of Systems Science and Industrial Engineering, Binghamton University. His research focuses on integrating large-scale data analysis, machine learning, and physics-informed models to address challenges in manufacturing, healthcare, and agriculture. He holds a PhD from the University of Washington and dual MS/BS degrees from KAIST.

• Education:
• PhD, University of Washington
• MS, KAIST
• BS, KAIST

His work spans manufacturing process optimization (e.g., solder joint prediction, reflow oven recipes), healthcare analytics (e.g., autism classification via EEG), and agricultural applications (e.g., leaf disease classification). Recent trends in his research emphasize hybrid models merging domain-specific physics principles with neural networks for precision in electronics manufacturing and medical diagnostics.

Publications highlight contributions to defect diagnosis in pick-and-place machines, energy-efficient reflow processes, and explainable AI for imbalanced manufacturing data. His interdisciplinary approach bridges engineering, computer science, and healthcare.

No scientific awards are listed, but his extensive publication record reflects active research engagement. He collaborates on projects involving smart manufacturing systems and AI-driven solutions for real-world problems.