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Wang, Jun


Jun Wang is an Assistant Professor in the Mechanical Engineering Department. He was a Postdoctoral Associate of Mechanical Engineering at the University of Maryland, College Park. He received his Ph.D. and M.S. in Mechanical Engineering from Buffalo-SUNY University and his B.S. from Xi’an Jiaotong University, China. His research interests are Data-Driven Design and Manufacturing, Physics-Driven Design, Generative Design, and Design for Additive Manufacturing. His current work is at the intersection of Machine Learning, Engineering Design, and Advanced Manufacturing. He has explored research areas in Computational Geometry, Design and Optimization, FEA, Additive Manufacturing, Machine Learning, and Computer Vision.


Ph.D., Mechanical Engineering, The State University of New York at Buffalo, 2019
M.S., Mechanical Engineering, The State University of New York at Buffalo, 2015 
B.S., Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, 2012 

Current Research Interests

I am exploring areas at the intersection of Engineering Design, Machine Learning/Artificial Intelligence, Advanced Manufacturing, and Topology Optimization. My specific research topics include Geometric Modeling, Metamaterial Design, Physics-Driven Design, and Data-Driven Design/Simulation/Manufacturing (Inverse Design & Generative Design).

Courses Taught

  • MECH 102: Introduction to Mathematical Methods in Mechanical Engineering
  • MECH 171/294: Applied Machine Learning in Engineering and Design
  • MECH 115: Machine Design II
  • MECH 294: Design Project using Machine Learning

Honors and Awards

  • NSF-CDS&E-2245299 Title: Collaborative Research: Data-Driven Inverse Design of Additively Manufacturable Aperiodic Architected Cellular Materials. Awarded: June 2023 ($249,829 PI: Jun Wang, 100%).
  • Best Paper Award for ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE) 2021 awarded by CIE Division, Virtual.


  • "Implicitly Represented Architected Materials for Multi-ScaleDesign and High-Resolution Additive Manufacturing." Advanced MaterialsTechnologies (2023).
  • "Deep learning-based inverse design framework for property targeted novel architectured interpenetrating phase composites." Composite Structures (2023). Darshil Patel, Ruoyu Yang, Jun Wang, Rahul Rai, and Gary Gargush. 10.1016/j.compstruct.2023.116783 
  • "Neural Network-Assisted Design: A Study of Multiscale Topology Optimization With Smoothly Graded Cellular Structures ." Journal of Mechanical Design (2022). Sina Rastegarzadeh, Jun Wang, and Jida Huang.
  • "Two-Scale Topology Optimization with Isotropic and Orthotropic Microstructures." Designs (2022). Sina Rastegarzadeh, Jun Wang, and Jida Huang.
  • "IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures." Computer Methods in Applied Mechanics and Engineering (2022). Jun Wang, Wei (Wayne) Chen, Daicong Da, Mark Fuge, and Rahul Rai.
  • "Functionally Graded Non-Periodic Cellular Structure Design and Optimization." Journal of Computing and Information Science in Engineering (2021). Jun Wang and Jida Huang.
  • "Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods." Journal of Mechanical Design (2021). Qiuyi Chen, Jun Wang, Phillip Pope, Wei (Wayne) Chen, and Mark Fuge.
  • "Hierarchical combinatorial design and optimization of non-periodic metamaterial structures." Additive Manufacturing (2020). Jun Wang, Jesse Callanan, Oladapo Ogunbodede, and Rahul Rai.
  • "Generative design of conformal cubic periodic cellular structures using a surrogate model-based optimisation scheme." International Journal of Production Research (2020). Jun Wang and Rahul Rai.
  • "Investigation of Compressive Deformation Behaviors of Cubic Periodic Cellular Structural Cubes through 3D Printed Parts and FE Simulations." Rapid Prototyping Journal (2019). Jun Wang, Rahul Rai, and Jason Armstrong.
  • "Deep Learning-Based Stress Prediction for Bottom-Up SLA 3D Printing Process." The International Journal of Advanced Manufacturing Technology 102, no. 5-8 (2019): 2555-2569. Aditya Khadikar, Jun Wang, and Rahul Rai.
  • "Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-lithography." Computer-Aided Design 99 (2018): 29-42. Jun Wang, Sonjoy Das, Rahul Rai, and Chi Zhou.
Now Hiring

Funded Ph.D. and Master’s Openings

  • See more details here to join Dr. Jun Wang's research group

Assistant Professor, Department of Mechanical Engineering