Welcome to my page. I am an assistant professor at the Department of Mechanical Engineering at the University of Kansas.

My research vision is to develop data-driven physics-informed design intelligence for advanced material-structure systems by leveraging artificial intelligence and computational mechanics. I am broadly interested in interdisciplinary topics at the frontiers of design optimization, material design, design for manufacturing, solid mechanics, material informatics, and scientific machine learning.

We are looking for talented students to join our Design Intelligence Laboratory at the University of Kansas. Please check our Openings and send me an email if you are interested in working with me.

Experience

• From 2023 to 2024, I am a research associate at the Northwestern University advised by Prof. Wei Chen and Prof. Horacio Espinosa for data-driven design of nonlinear multiscale material systems and advanced manufacturing.
• From 2020 to 2022, I worked with Prof. Diran Apelian and Prof. Ramin Bostanabad as a postdoctoral fellow at the University of California Irvine to develop reduced-order models and data-driven surrogates for multiscale metallic alloys with tomography-infused manufacturing-induced porosity distribution.
• From 2016 to 2019, I was a research engineer in the design optimization team of the MSC NASTRAN to design, develop, test and maintain topology optimization solvers on the generative design platform APEX for additive manufacturing.
• In 2016, I received my Ph.D in Mechanical Engineering from the University of Wisconsin Madison. I was supervised by Prof. Krishnan Suresh and my research was about augmented topological level-set methods for large-scale thermo-elastic topology optimization.

News

• 08/2024: our Design Intelligence Laboratory at the University of Kansas has now been officially established. Multiple openings are available for graduate students in Spring/Fall 2025.
• 01/2024: our paper of “Data-Driven Physics-Constrained Recurrent Neural Networks for Multiscale Damage Modeling of Metallic Alloys with Process-Induced Porosity” is published by the Computational Mechanics.
• 10/2023: I attended the SES Annual Technical Conference at the University of Minnesota and presented our lastest work on machine learning surrogates of multiscale damage analysis.
• 09/2023: I serve as a minisymposium organizer for the IACM-MMLDE-CSET2023 Conference at the University of Texas El Paso.
• 07/2023, I give a presentation about our work of scientific machine learning for material mechanics modeling on the Sandia NL’s Machine Learning & Deep Learning Workshop
• 03/2023: our paper of “Adaptive Spatiotemporal Dimension Reduction in Concurrent Multiscale Damage Analysis” is published by the Computational Mechanics.
• 01/2023: our paper of “Data-Driven Calibration of Multifidelity Multiscale Fracture Models via Latent Map Gaussian Process” is published by the Journal of Mechanical Design.