The Data Science Transdisciplinary Area of Excellence (TAE) organizes and sponsors different kinds of events, which are described below. Explore events in the menu on the right hand side of the page. You can find webcasts of some talks on the "Webcasting" page. Subscribe to our Google Calendar to keep in touch.
Data Salon
The Data Salon is our signature event. It is an informal gathering where researchers on campus share ongoing data-related work with the data science community, with the objective of communicating with those outside of their immediate field. We then open discussion to all attendees with the goal of developing and identifying strategies, methods and related questions of interest to the researcher and data scientists. The concept of a "salon" is for a researcher to present problems and research opportunities in a domain-independent fashion that invites contributions and collaborations from different disciplines, rather than an evaluation of the merits of a specific result, as would be the case for a conventional research seminar.
Invited Speaker Series
For our Invited Speaker Series, we invite leaders in data science from off campus, including researchers, administrators, executives and policy makers from universities, institutes and organizations, to share their recent research developments or their insights on the data science movement.
Other Events
We organize events such as faculty-student mixers, workshops, lectures and data competitions.
We also sponsor/endorse seminars and colloquiums organized by various departments and units on campus. These events are often not initiated by the Data Science TAE, but by the individual departments or units.
Contact us if you would like to lead a discussion in a Data Salon event, nominate an invited
speaker or request our sponsorship of a seminar that your department/unit is organizing.
All Events
12:00pm - 1:00pm
AD 148
Yu “Chelsea” Jin
Systems Science and Industrial Engineering
Friday, April 26, 2024, 12:00-1:00 PM
Couper AD-148 (with lunch served) Special Location
Zoom Option: https://binghamton.zoom.us/j/92622837791
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Abstract
Domain-informed Artificial Intelligent (AI) leverages expert knowledge from specific domains to enhance model performance and interpretability. In this Data Salon, a decomposition-guided AI modeling framework for nonstationary time series forecasting and physics-informed machine learning for predicting desired properties in advanced manufacturing and healthcare applications will be introduced. These techniques harness domain knowledge to guide the model training process, ensuring that the AI systems not only capture underlying patterns in the data but also adhere to domain-specific constraints and principles.
About the speakers: Yu “Chelsea” Jin is an assistant professor of Systems Science and Industrial Engineering at Binghamton University. She received her Ph.D. in Industrial Engineering from the University of Arkansas – Fayetteville in May 2020. She also earned her M.E. degree in Manufacturing Engineering from the University of Michigan at Ann Arbor in 2015 and B.S. degree in Network Engineering from the Department of Information Science and Technology at Jinan University (Guangzhou, China) in 2014. Her research focuses on both cutting-edge techniques and methodologies, especially machine learning and artificial intelligence methods, to enhance advanced manufacturing systems within the context of Industry 4.0 and to realize digital twins/factories. Her research has been sponsored by the Transdisciplinary Area of Excellence Seed Grant, Integrated Electronics Engineering Center, Innovation Association, and Zebra. The research findings of her group have been published in IISE Transactions, ASME Journal of Manufacturing Science and Engineering, Rapid Prototyping Journal, etc. She received the IISE Gilbreth Memorial Fellowship in both 2018 and 2019; the Kuroda Graduate Fellowship in Engineering, the Graduate Research Award in 2019, and the Outstanding Graduate Student Award in 2020 from the University of Arkansas. She has been an active member of IISE, INFORMS, ASME and Alpha Phi Mu. She has been a board member of IISE DAIS Division since May 2022 and an officer of INFORMS QSR Fund Raising Committee since May 2023. She also served as a reviewer for IISE Transactions, ASME Journal of Manufacturing Science and Engineering, IEEE Transactions on Automation Science and Engineering, Journal of Intelligent Manufacturing, etc.
