Xiaoping Du

Xiaoping Du is a Professor of Mechanical Engineering at Purdue University, based at the Indianapolis campus. His research focuses on design optimization, probabilistic methods, reliability-based design, and uncertainty quantification in machine learning and manufacturing. He holds a Ph.D. in Mechanical Engineering from the University of Illinois at Chicago (2002).

Dr. Du's work emphasizes integrating machine learning with traditional engineering principles to address uncertainty in complex systems. His research spans reliability analysis, additive manufacturing, and stochastic modeling, with applications in aerospace, biomedical, and transportation systems.

• Education: Ph.D., Mechanical Engineering, University of Illinois at Chicago, 2002.

His research interests include:

• Uncertainty quantification for machine learning models.
• Reliability-based design under dynamic and time-dependent conditions.
• Probabilistic methods for high-dimensional systems.
• Optimization under uncertainty in manufacturing processes.

His recent publications highlight advances in hybrid reliability methods, uncertainty separation in mixed data, and envelope-based time-dependent design. His work often bridges physics-based models with data-driven techniques.

• Awards:
  • 2023 Trustees' Teaching Award (IUPUI).
  • 2017 Curators' Distinguished Professor (University of Missouri).
  • Multiple teaching awards from Missouri University of Science and Technology and University of Missouri–Rolla.

No student advisees are listed, though his teaching awards reflect extensive educational contributions. His work on reliability-based design optimization has been applied in aerospace, biomedical, and additive manufacturing contexts.