You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room
Speakers
Maria Zawadowicz, EBNN--ML for Atmospheric Aerosol Research
Mohammad Atif, CDS--An Extensible Digital Twin Framework
Guang Zhao, CDS--Pareto Prompt Optimization
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1
Meeting ID: 161 528 9117
Passcode: 991382
Iwao Ojima, Director, ICB&DD
Ivet Bahar Chair, Organizing Committee
Dima KozakovCo-Chair, OrganizingCommittee
There will be poster sessions on projects conducted in the ICB&DD member's laboratories aswell as other laboratories in the area. Awards will be given to the best three posters.
Please see the link for the registration and poster sessions in:
https://www.stonybrook.edu/commcms/icbdd/https://forms.gle/
Abstract: This talk shows how machine learning can address challenges in Astrophysics. We specifically focus on black hole simulations and supernova observations. First, we present a super-resolution technique for black hole simulations that avoids the need for high-resolution labels by leveraging the Hamiltonian and momentum constraints from general relativity. This method reduces constraint violations by one to two orders of magnitude. Next, we introduce Maven, a multimodal foundation model for supernova science. Using contrastive learning to align photometric and spectroscopic data, Maven achieves state-of-the-art results in classification and redshift estimation by pre-training on synthetic data and fine-tuning on real observations.
Bio: Thomas Helfer is a computational physicist specializing in deep learning and physics. Currently based at the Institute for Advanced Computational Science at Stony Brook University, Thomas was previously a postdoctoral fellow at Johns Hopkins and did his PhD with Eugene Lim at King's College in London. In his work, he looks to bridge topics; in his PhD, he bridged theoretical particle physics and gravitational waves. Now, in his postdoctoral work, he aims to find novel applications of deep learning in astrophysics.
*please note: this seminar will be held in a hybrid format*
Location: IACS Seminar Room OR Join Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 986 1763 0652
Passcode: 882994
The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.
Join University Libraries for an engaging panel discussion where we delve in and learn about the impacts of artificial intelligence on the 2024 US elections! Panelists are Paige Lord, Tom Costello, and Musa al-Gharbi. The discussion will be moderated by Library Dean, Karim Boughida. Co-sponsored by the Office of Diversity, Inclusion, and Intercultural Initiatives.
Please RSVP for Democracy in the Digital Age: AI's Influence on 2024 Elections here.
For more information, and registration, visit the official website.