Full Contact Information of Organizers

• Jihoon Chung, Assistant Professor, Pusan National University, Korea, jhchung@pusan.ac.kr

• Chenang Liu, Assistant Professor, Oklahoma State University, chenang.liu@okstate.edu

• Yao Ma Assistant Professor, Rensselaer Polytechnic Institute, may13@rpi.edu

• Diane Oyen, Scientist and Team Leader of Machine Learning, Los Alamos National Laboratory, doyen@lanl.gov

• Yinan Wang Assistant Professor, Rensselaer Polytechnic Institute, wangy88@rpi.edu

Workshop Description

In the era of the Internet of Things (IoT), with the rapid development of advanced sensing, data storage, data analytics, and high-performance computing technologies, both manufacturing industries and healthcare systems are experiencing a data‑driven revolution. However, the unique characteristics of manufacturing and healthcare systems prevent the direct application of existing data-driven methods. Their characteristics include (1) systematic physical principles, (2) high demand for interpretability, robustness, and trustworthiness, and (3) limited computation resources and the need for instant decision-making. These characteristics raised pressing needs to develop domain-aware data-driven approaches for critical tasks in manufacturing and healthcare systems, such as smart diagnosis, automatic control, design optimization, customized analytics, etc.

This workshop aims to demonstrate the recent progress of data science research, which focuses on addressing the unique challenges in manufacturing and healthcare systems, such as the gaps in data quality/security assurance, domain-aware data analytics, improvement of trustworthiness, etc. We cordially invite submissions that focus on recent advances in research/development of data science, which are motivated by real-world problems in manufacturing and healthcare. Papers and/or posters focus on both theoretical foundations and applications are welcomed from the areas including but not limited to:

Topics of Interest

Methodology

• Active learning
• Bayesian optimization
• Contrastive learning
• Data-driven inverse modeling
• Deep reinforcement learning
• Domain-aware machine learning
• Generative model
• Graph machine learning
• Image/video processing
• Incremental learning
• Surrogate modeling
• Trustworthy machine learning
• Uncertainty quantification

Application

• Additive manufacturing
• Anomaly detection
• Biomanufacturing
• Cyber-physical systems
• Dataset design
• Data quality and security
• Design optimization
• Disease screening and prediction
• Material/process degradation
• Healthcare and biomedical data analytics
• Imaging and sensing
• Process monitoring and control
• Quality control
• Root cause diagnosis
• Sustainable manufacturing

Description of the Workshop:

This workshop is expected to be a full-day event with two half-day sessions, split by a lunch break. The format of this workshop will include invited keynote presentations and accepted presentations from submitted papers. The tentative workshop agenda is shown as follows.

Tentative Workshop Agenda

Morning Session

• 10:00 – 10:10 Welcome and Opening Remarks
• 10:10 – 10:30 Contributing paper presentation 1
• 10:30 – 10:50 Contributing paper presentation 2
• 10:50 - 11:25 Keynote presentation 1
• 11:25 – 11:45 Contributing paper presentation 3

Lunch Break

Afternoon Session

• 13:30 - 13:50 Contributing paper presentation 4
• 13:50 - 14:10 Contributing paper presentation 5
• 14:10 - 14:45 Keynote presentation 2
• 14:45 – 15:20 Keynote presentation 3
• 15:20 - 15:25 Closing Remarks

*The schedule may be subject to change according to the SDM conference schedule.

Description of Target Audience

This workshop is expected to invite leading researchers in the domain of data mining, machine learning, advanced manufacturing, and healthcare systems. The target audience will be program officers from funding agencies, industries, researchers who are interested in data science and its applications, and undergraduate/graduate students in the general engineering discipline who plan to pursue a career related to data science. The list of tentative participants is given as follows.

• Michael Biehler, Georgia Tech
• Hongliang Chi, New Jersey Institute of Technology
• Xiaoyu Chen, University at Buffalo
• Enyan Dai, Penn State University
• Tyler Derr, Vanderbilt University
• Jiayuan Ding, Michigan State University
• Wenning Feng, Goldman Sach
• Wei Jin, Emory University
• Vanessa Job, University of New Mexico
• Chen Kan, University of Texas at Arlington
• Andrew Law, IoTeX
• Jia Liu, Auburn University
• Xiaorui Liu, North Carolina State University
• Jennifer Pazour, Rensselaer Polytechnic Institute
• Harry Shomer, Michigan State University
• Wenbo Sun, University of Michigan
• Wenmeng Tian, Mississippi State University
• Selma Wanna, University of Texas at Austin
• Yu Wang, Vanderbilt University
• Qiong Wu, AT&T
• Xiaowei Yue, Tsinghua University
• Jie Zhang, Nielsen
• Tong Zhao, Snap Inc.

Important Dates

• Travel Award Application: Jan. 30, 2025; link
• Paper Submission: Feb. 26, 2025; Link to be released
• Acceptance Notification: March 10, 2025
• Final Paper Submission: April 1, 2025

Biography of the organizers:

• Jihoon Chung is an Assistant Professor in the Department of Industrial Engineering at Pusan National University in Korea. He received his Ph.D. degree in Industrial and Systems Engineering at Virginia Polytechnic Institute and State University (Virginia Tech) (May 2023). He also obtained M.S. and B.S. degrees in Industrial Engineering at the Korea Advanced Institute of Science and Technology (KAIST) and Hanyang University, respectively. His research area is developing data-driven methods, including artificial intelligence, machine learning, and statistical learning, to achieve quality assurance in advanced manufacturing processes and solve various problems in healthcare systems and material analysis. His research was recognized as several best paper/poster awards in IISE Quality Control & Reliability Engineering division and INFORMS Quality, Statistics, and Reliability section.

• Chenang Liu is an Assistant Professor in the School of Industrial Engineering and Management at Oklahoma State University. He earned his Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2019. He also received his master’s degree in Statistics from Virginia Tech in 2017 and double bachelor’s degrees from Zhejiang University in 2014. His research interests include data-driven analytics, process quality monitoring and control methodologies, and artificial intelligence-enabled techniques for smart manufacturing, healthcare, and service system applications. His research contributions were recognized by multiple best paper/poster awards. His ongoing research projects are also funded by the federal agencies including NSF, NIH, and FDA, as well as the Oklahoma Center for the Advancement of Science and Technology (OCAST). He is an associate editor of the Journal of Intelligent Manufacturing (JIM), a guest editor of the ASME Journal of Computing and Information Science in Engineering (JCISE), and an executive guest editor of the Journal of Manufacturing Systems (JMS). He was the organizer of 2022 IISE South Central Regional Conference, and he also served as the co-chair of the Data Analytics and Information Systems (DAIS) track in 2023 IISE Annual Conference.

• Yao Ma is an Assistant Professor in the Department of Computer Science at the Rensselaer Polytechnic Institute (RPI). He received his Ph.D. in Computer Science from Michigan State University (MSU) in 2021, with a focus on machine learning with graph-structured data. His research contributions to this area have led to numerous innovative works presented at top-tier conferences such as KDD, WWW, WSDM, ICLR, NeurIPS, and ICML. He has also organized and presented several well-received tutorials at AAAI and KDD, attracting over 1000 attendees. He is the author of the book “Deep Learning on Graphs”, which has been downloaded tens of thousands of times from over 100 countries. He was awarded the Outstanding Graduate Student Award (2019-2020) from the College of Engineering at MSU.

• Diane Oyen, Scientist and Team Leader of Machine Learning, Los Alamos National Laboratory. She received her Ph.D. in Computer Science at the University of New Mexico and her B.S. in Electrical Engineering from Carnegie Mellon. She joined Los Alamos National Laboratory in 2013, leading projects on trustworthy machine learning and on computer vision for scientific imagery, involving over a dozen research scientists plus postdocs and students. Her research focuses on developing data science approaches for scientific and national security applications; particularly through the use of graph-based machine learning to understand complex patterns in data. She has organized workshops at top-tier computer vision and machine learning conferences including SDM, CVPR, ECCV, and NeurIPS.

• Yinan Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at Rensselaer Polytechnic Institute. He received the B.S. degree in Electrical Engineering and Automation from Xi’an Jiaotong University in 2017, the M.S. in Electrical Engineering from Columbia University in 2019, and the Ph.D. in industrial and Systems Engineering from Virginia Tech in 2022. His research interests include data analytics and machine learning techniques in quality control of advanced manufacturing systems. He is the recipient of FTC Early Career Award, 10 Best Paper/Poster/Featured Article Awards, and two Best Ph.D. Dissertation Awards. He was the Mary and Joseph Natrella Scholar from ASA. He is a guest editor of the ASME Journal of Computing and Information Science in Engineering (JCISE) and the co-chair of the Data Analytics and Information Systems (DAIS) track at the 2025 IISE Annual Conference. He was co-organizer for the Symposium in Manufacturing Science and Engineering Conference (MSEC) 2023 and workshops at SIAM International Conference on Data Mining (SDM) 2023 and 2024. He serves as an active Board Director in IISE Data Analytics and Information Systems (DAIS) division.

Previous workshop

• DS2-MH Workshop at SDM23
• DS2-MH Workshop at SDM24

For questions regarding this workshop, please contact us at: wangy88@rpi.edu