معرفی
Jun Wang is an Assistant Professor in the Department of Mechanical Engineering at Santa Clara University (SCU). He received his Ph.D. and M.S. in Mechanical Engineering from SUNY Buffalo (2019, 2015) and a B.S. from Xi’an Jiaotong University (2012). He previously served as a Postdoctoral Associate in Mechanical Engineering at University of Maryland, College Park and worked in the IDEAL Lab, focusing on Engineering Design, Optimization, and Machine Learning.
- Education:
- Ph.D., Mechanical Engineering, SUNY Buffalo (2019)
- M.S., Mechanical Engineering, SUNY Buffalo (2015)
- B.S., Mechanical Engineering, Xi’an Jiaotong University (2012)
Dr. Wang’s research lies at the intersection of Engineering Design, Machine Learning/Artificial Intelligence, Advanced Manufacturing, and Topology Optimization. His lab, GIFTED (Geometric Informatics For Technology, Engineering, and Design), develops AI-driven methods for geometric modeling, metamaterial design, and additive manufacturing. Specific projects include inverse/generative design frameworks, surrogate models for manufacturing simulation, and high-resolution multi-scale material synthesis.
His recent publications focus on machine learning integration with isogeometric analysis, implicit surface-based inverse design of cellular materials, and optimization of non-periodic metamaterials. Notable collaborations include work on vision transformers for image enhancement and generative adversarial networks for engineering applications. Trends in his research emphasize bridging geometric modeling with cloud computing and big data to accelerate design cycles.
- Scientific Awards:
- Best Paper Award (IDETC-CIE 2021) for non-periodic cellular design
Dr. Wang mentors graduate researchers Shaoliang Yang (Ph.D. candidate) and Kevin Wang (Master’s graduate). His former students Matthew Franke (now at Intel) and Yunchen Li (now at Hymson USA Inc.) have transitioned to industry roles. His NSF-funded project ($249,829) explores data-driven inverse design of additively manufacturable materials.
At SCU, he leads the GIFTED Lab, which combines geometric modeling, machine learning, and advanced manufacturing to revolutionize design workflows. The lab’s work targets performance-equivalent designs generated via AI, with applications in aerospace, automotive, and biomedical industries.
