Han-Wei Shen
Professor, Dept. of Computer Science & Engineering, The Ohio State University
Tuesday, Sep 23, 2025, 3:00-4:00 PM ET (1:00-2:00 PM MT)
In-person at Bldg. 101 LR-D* with VTC to Boulder 1-4072
Online at: Zoom Gov (email seminar chairs for link to talk)
Add this talk to your calendar: https://inet.nist.gov/calendar/ics/2315031
A video of this talk will be made available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page. It will be taken down from NISTube after 12 months at which point it can be requested by emailing the ACMD Seminar Chair.
Abstract: In this talk, I will discuss our recent developments on using machine learning for scientific data analysis and visualization, with special focuses on visualization surrogates and compact representations for scientific data. I will first discuss how to construct visualization surrogates that can help streamline the visualization and analysis of large-scale ensemble simulations and facilitate the exploration of their immense input parameter space. Different approaches for constructing such visualization surrogates such as image space and object space approaches will be discussed. Then I will discuss how neural networks can be used to extract succinct representations from scientific data for rapid exploration and tracking of features.
Bio: Han-Wei Shen is a Full Professor at The Ohio State University, and currently serves as the Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics. He is also currently a program director at NSF. He is a member of IEEE VGTC Visualization Academy, and was the chair of the steering committee for IEEE SciVis conference from 2018-2020. His primary research interests are visualization, artificial intelligence, high performance computing, and computer graphics. Professor Shen is a winner of National Science Foundation's CAREER award and US Department of Energy's Early Career Principal Investigator Award. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View, California.
Host: Simon Su
Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)
*Safety Precaution: The hallway leading from the Courtyard to the exit closest to B-111 and B-113 will be used by contractors to move debris, machinery, and other supplies, as well as will be heavily trafficked by the contractors throughout the process. Be aware of the safety precautions posted during this time.
Note: Visitors from outside NIST must contact Meliza Lane at least 24 hours in advance.
