Workshop 1

IEEE CASE 2019 Workshop on Data Science for Engineering Automation

Preliminary Workshop Program

Goal:  Data science plays a critical role in automation science and engineering. The relevance and presence of data science will only be stronger in automation in the years to come. With data science being a broad field, one unique aspect is the interface and organic integration of operations research methods and statistical models in advancing statistical learning and artificial intelligence and broadening the applications of automation in several crucial engineering areas.

A group of researchers primarily working in the field of industrial and systems engineering and operations research have been developing various data science methodologies with positive impact on automation science and engineering, and these methods cover a broad array of engineering application areas, ranging from manufacturing, energy, material, to healthcare.

This workshop is to present the start-of-the-art methods and developments from these data science researchers and demonstrate how they impact the theory and practice of automation science and engineering. It is a good opportunity for these researchers to interact with automation audience and practitioners, potentially collaborating to address research challenges in automation with data science relevance. The format of the workshop is to have each presentation followed by ample time of discussion. The workshop is to be concluded by a panel discussion looking into future roadmap.

Title:                [Data Science for Engineering Automation]

Organizers:  [Yu Ding], [Mike and Sugar Barnes Professor]

                            [Department of Industrial and Systems Engineering, Texas A&M University]

                            E-mail: [yuding@tamu.edu]

                           Phone: +[1] – [979-777-7155]

                          [Kaibo Liu], [Assistant Professor]

                           [Department of Industrial and Systems Engineering, University of Wisconsin - Madison]

                           E-mail: [kliu8@wisc.edu]

                          Phone: +[1] – [608-890-3546]

Time:               August 22, 2019, Thursday

Abstract:       Data science plays a critical role in automation science and engineering. The relevance and presence of data science will only be stronger in automation in the years to come. With data science being a broad field, one unique aspect is the interface and organic integration of operations research methods and statistical models in advancing statistical learning and artificial intelligence and broadening the applications of automation in several crucial engineering areas. This workshop is to present the start-of-the-art methods and developments from this unique aspect and demonstrate how these methods and developments impact the theory and practice of automation science and engineering.

Format:          One day; presentation/discussion sessions plus a panel discussion.

Workshop Agenda

8:15-8:30   am

Registration and speakers check-in

8:30-9:00   am

Yu Ding,  Texas A&M University, “Remark on Data Science for Engineering Systems”

9:00-10:00   am

Kamran Paynabar,  Georgia Institute of Technology, “Data Science for Manufacturing Automation”

10:00-10:30 am

Morning break

10:30-11:30   am

Chiwoo Park,  Florida State University, “Data Science for Automating   Material Discovery”

11:30-12:30   pm

Kaibo Liu,  University of Wisconsin-Madison, “Data Science for Degradation Systems”

12:30-1:30   pm

Lunch Break

1:30-2:30   pm

Eunshin Byon, University of Michigan, “Data Science for Wind

Energy Automation”

2:30-3:30   pm

Shuai Huang, University of Washington, “Data Science for Healthcare Automation”

3:30-4:00   pm

Afternoon break

4:00-5:00   pm

Panel Discussion

Moderator:  

Kaibo Liu, University of Wisconsin – Madison

Panelists:  

Ken Goldberg, University of California, Berkeley

Spyros Reveliotis, Georgia Institute of Technology

Kazu Saitou, University of Michigan

Michael  Y. Wang, Hong Kong University of Science and Technology