Our paper titled “Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts” has been published on Additive Manufacturing. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao. Industrial-scale production applications of additive manufacturing (AM) are growing rapidly, and scalable AM production requires quality systems that monitor and control […]
News
Shichen will start her internship at Meta this summer
Shichen has accepted an internship offer from Meta in 2022 Summer. She will work as a Machine learning engineer intern. She will conduct engineering projects and cutting-edge researches in the fields of Machine Learning (ML), Natural Language Processing (NLP). Her PhD research focusing on machine learning in manufacturing processes and her recent involvement in a […]
Siyuan Will Start his Internship at 3M
Siyuan has recently accepted an internship position at 3M as a Data Science intern this summer. He will work with the AI & ML lab at 3M’s Corporate Research Systems Laboratory to design new material structure and geometry using Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). His PhD research focusing on machine learning in […]
Paper on Multi-Object Tracking in Videos published on IEEE Access
Our paper titled “Efficient Online Tracking-by-Detection With Kalman Filter” has been published on IEEE Access. The paper was co-authored by Siyuan Chen and Prof. Chenhui Shao. Visual Multi-Object Tracking (MOT) has a promisingly broad application in manufacturing, construction, traffic, logistics, etc., especially in large-scale applications where it is not feasible to attach markers to many […]
Paper on adaptive sampling for multi-task Gaussian processes published in JMS
Our paper titled “Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Multi-task learning (MTL) is a machine learning technique used to enhance learning performance in similar-but-not-identical tasks. However, the accuracy of MTL […]
Kuan-Chieh receives fellowship from the Ministry of Education in Taiwan
The Government Scholarship to Study Abroad, a fellowship funded by the Ministry of Education in Taiwan, encourages exceptional doctoral students to study abroad in STEM and other fields. This fellowship is competitive since only ten students have the honor to receive it at the end. Kuan-Chieh is a Ph.D. student working on the quality control […]
Paper on Hybrid Multi-Task Learning-Based Response Surface Modeling Published in JMS
A paper entitled “hybrid multi-task learning-based response surface modeling in manufacturing” is recently published in the Journal of Manufacturing Systems. The paper was co-authored by Yuhang Yang and Chenhui Shao. This paper developed a hybrid multi-task learning-based method tocost-effectively model the response surfaces of multiple similar-but-not-identical manufacturing processes. The method was evaluated using a simulation-based numerical […]
ADML Wins New NSF Project on Digital Biomanufacturing
The project is titled, “Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization.” The funding comes from the NSF’s Advanced Manufacturing (AM) program within the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). Working with our collaborators at Brown University, we will create a digital manufacturing platform for cloud-based […]
ADML Receives Funding from AIFS
The Artificial Intelligence Institute for Next Generation Food Systems, or AIFS, was launched October 1, 2020 to solve the world’s biggest challenges to crop and food production facing our planet. For more information about AIFS, please see this news article and the AIFS website. In the first year of the Institute, we will work on […]
ADML Receives Funding from DOE
The group receives funding from the Advanced Manufacturing Office of the U.S. Department of Energy (DOE). The project, entitled “Novel Energy-Efficient Drying Technologies for Food, Pulp and Paper, and other Energy Intensive Manufacturing Industries,” aims to develop innovative dryer platforms for the food and forest products industry to reduce energy consumption and to improve product […]