Shichen will begin an internship position as a Software Engineer in Machine Learning at Meta in Summer 2024. She will utilize advanced machine learning methods to conduct feature engineering and improve the content click-through rate for Facebook apps. Sixian will start a summer internship as a Machine Learning Scientist (PhD) at Wayfair in Boston. She […]
After graduating with his Ph.D. in Mechanical Engineering (with a graduate minor in Statistics) from our lab, Manan will start his career in industry as a Senior Data Scientist at C3.ai in Redwood City, CA. Founded by veteran Silicon Valley entrepreneur Tom Siebel, C3 AI is a leading provider of Enterprise AI solutions to a […]
Two students from our lab at Illinois – Yuquan Meng and Manan Mehta – graduated with their Ph.D. degrees this May! Both students were hooded by Prof. Shao in the university-wide Ph.D. hooding ceremony held on 10th May 2024 at the State Farm Center at the University of Illinois Urbana-Champaign. Our best wishes to both […]
Our paper titled “Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography” has been published on Journal of Manufacturing Processes. The paper was co-authored by Sixian Jia, Jieliyue Sun, Andrew Howes, Prof. Michhelle R. Dawson, Prof. Kimani C. Toussaint Jr. , and Prof. Chenhui Shao. Two-photon lithography (TPL) is an additive […]
Recently, our paper titled “Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuquan Meng, Kuan-Chieh Lu, Zhiqiao Dong, Shichen Li, and Prof. Chenhui Shao. Modern manufacturing is featured by rapid reconfiguration and agile adaptation that necessitate varying process configurations. In ultrasonic metal welding, a process […]
Our PhD students Kuan-Chieh, Zhiqiao, and Manan published papers and present their research at MSEC 2023 & NAMRC 51 at Rutgers University this summer. Kuan-Chieh’s paper titled “Online Cost-Effective Classification of Mixed Tool and Material Conditions in Ultrasonic Metal Welding: Towards Integrated Monitoring and Control” has been published at MSEC 2023. Ultrasonic metal welding (UMW) […]
Yuquan Meng has successfully defended his doctoral dissertation titled “Physics-Informed Machine Learning for Smart Decision-Making in Ultrasonic Metal Welding.” The defense was conducted before an esteemed Examination Committee consisting of Prof. Chenhui Shao, Prof. Placid Ferreira, Prof. Srinivasa Salapaka, and Prof. Pingfeng Wang. In his research, Yuquan Meng has made significant contributions to the field […]
Our paper titled “End-to-end online quality prediction for ultrasonic metal welding using sensor fusion and deep learning” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yulun Wu, Yuquan Meng and Prof. Chenhui Shao. In industrial-scale production applications of ultrasonic metal welding (UMW), there is a strong need for predicting joint […]
Kuan-Chieh will start an internship position at Intel in summer 2023. He will work with the Logic Technology Development Group to analyze epitaxially grown films and develop an AI algorithm to predict the quality based on process conditions. His Ph.D. research focuses on data efficiency in manufacturing quality monitoring and real-time parameter adjustment for disturbance […]
Manan has accepted an internship offer from Seagate Technology for Summer 2023 where he will work as an AI/Machine Learning Intern. He will work in Seagate’s highly dynamic Global Wafer Systems (GWS) Team and collaborate with a global team of Data Scientists and Machine Learning Engineers to shape the future of new data products in […]