Research
Focus areas and publications
Focus Area 1: Physics-informed data-driven modeling and optimization for processes and operations
Developing novel statistical models, machine learning methods, and optimization algorithms with emphasis on both generalizability and interpretability.
Integrating physics principles and domain knowledge of complex processes into data-driven modeling to improve prediction, diagnosis, and optimization.
Publication examples
A multimodal transfer learning framework for nondestructive fatigue life prediction for additively manufactured metal parts
Publihsed on IISE Transactions in 2025
Layer-wise spatial modeling of porosity in additive manufacturing
Published on IISE Transactions in 2019
Real-time monitoring with vibration signals and its application to chemical mechanical planarization by using Dirichlet process Gaussian mixture models
Published on IEEE Transactions on Automation Science and Engineering in 2016
A machine learning framework with Shapley’s additive explanations to assess solder joint reliability for electronic packaging
Published on Journal of Electronic Materials in 2025Focus Area 2: Heterogeneous sensing and data-driven modeling for process monitoring, quality prediction, and operation optimization in additive manufacturing
Investigating materials-process-structure-property-performance relationships in different additive manufacturing processes using advanced sensing and machine learning.
Understanding the fatigue performance of parts made from the laser powder bed fusion (L-PBF) process in terms of process conditions, defects, and surface roughness, and improving the accuracy of fatigue life prediction for L-PBF parts in critical applications.
Publication examples
Feature-based volumetric defect classification in metal additive manufacturing
Published on Nature Communications in 2022
Defect criticality analysis on fatigue life of L-PBF 17-4 PH stainless steel via machine learning
Published on International Journal of Fatigue in 2022
Determining critical surface features affecting fatigue behavior of additively manufactured Ti-6Al-4V
Published on International Journal of Fatigue in 2025
A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing
Published on Journal of Intelligent Manufacturing in 2023
Nonparametric Bayesian framework for material and process optimization with nanocomposite fused filament fabrication
Published on Additive Manufacturing in 2022
Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors
Published on Journal of Manufacturing Science and Engineering in 2015Focus Area 3: AI in distributed smart manufacturing to advance critical fabrication capabilities and resilient supply chains
Adapting cutting-edge AI technologies, such as LLM, multimodal learning, and federated learning, into distributed manufacturing paradigms equipped with I4.0 infrastructure.
Investigating AI methods and technologies for materials design, in-situ process monitoring and quality prediction for semiconductor manufacturing and electronic packaging.
Publication examples
Wafer quality monitoring using spatial Dirichlet process based mixed-effect profile modeling scheme
Published on Journal of Manufacturing Systems in 2018
FedCOT: Personalized Federated Transfer Learning with Conditional Optimal Transport for Manufacturing Predictive Modeling
Published on IEEE Transactions on Automation Science and Engineering in 2026
