You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk 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.

At our Oct 7 Mixer, BNL's newly minted interim director, John Hill will be present to give opening remarks and kick us off on a new year of impactful scientific AI collaborations.

Abstract: Weather extremes and strong seasonal-to- subseasonal variability pose growing challenges to urban populations, infrastructure, and energy systems. Yet, most cities remain data deserts: routine weather observations are sparse, with stations concentrated at airports rather than within the urban core. This lack of coverage limits our ability to monitor and predict fine-scale urban weather patterns precisely where they matter most. We present a new AI-driven framework for optimal sensor placement and urban weather monitoring. Unlike traditional approaches, our method leverages physics- based simulations together with Bayesian experimental design principles, but does so using a computationally efficient variational inference strategy that makes large-scale optimization tractable. This allows us to guide sensor networks in a way that minimizes information loss while capturing spatiotemporal variability at city scales. Applied to Phoenix, Arizona, our framework outperforms random sensor placement strategies, especially when only a limited number of sensors can be deployed. Importantly, the same AI models that guide sensor placement also function as a real-time nowcasting tool, providing urban weather information over the entire domain, beyond sensor locations. Together, these capabilities offer a scalable pathway to reduce urban data deserts, enhance monitoring of weather extremes, and improve resilience planning for energy, transportation, and public health systems.

Biography: Dr. Katia Lamer is an atmospheric scientist and the Director of the Center for Multiscale Applied Sensing at Brookhaven National Laboratory. Originally from Canada, she earned her B.S. and M.S. in Atmospheric and Oceanic Sciences from McGill University and a Ph.D. in Meteorology from Penn State University. Her research focuses on atmospheric boundary layer processes and remote sensing technologies, with a strong emphasis on data science. At Brookhaven, she is known for her work with the CMAS mobile observatories and its facility that connect fundamental atmospheric science to real-world applications, improving weather prediction, environmental monitoring, and urban climate resilience. Her work has been featured in public outlets such as New Scientist and Wired. Dr. Lamer also serves as an invited member of the World Meteorological Organization's Data Assimilation and Observing Systems Working Group, and the American Meteorological Society's Boundary Layer and Turbulence Committee. puting, communications and sensing, all enabled by AI.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624 | Passcode: 558449

The Future Histories Studio will host Young Maeng, an artist and professor at California State University, Fresno, for a talk exploring the intersection of artificial intelligence (AI) and traditional painting, examining how two seemingly disparate fields can converge to create new artistic expressions.

The lecture is part of the Future History Studio series at Stony Brook University, a platform dedicated to examining the evolving relationship between technology, art, and society.

Young will discuss her innovative approach to expanded painting, an integration of AI-generated images and traditional techniques such as Korean ink and acrylic painting. Through this fusion, she visualizes complex philosophical and ethical questions about the coexistence of humans, nature, and AI companion robots. The lecture will highlight the broader implications of AI in the art world, touching on how AI technologies challenge conventional notions of creativity and human-centric perspectives in art.

Speaker Bio:

Young Maeng is an artist and professor at California State University, Fresno, whose work explores the intersection of artificial intelligence (AI) and traditional painting techniques such as Korean ink and acrylic.

Maeng's innovative approach to expanded painting blends AI technology with traditional methods to visualize complex philosophical and ethical questions surrounding the coexistence of humans, nature, and AI companion robots.

Location: Future Histories Studio
Register here: https://www.eventbrite.ca/e/ai-and-painting-tickets-1021050809457?aff=oddtdtcreator

What can you learn from over seven years' worth of Twitter bios? Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of SBU's Institute for AI-Driven Discovery and Innovation, will tell us.

Presenting work done with collaborators Jason Jones, Dakota Handzlik, and Xingzhi Guo, Dr. Skiena will discuss what the team learned about how people portray themselves on social media through their political identities and job status. He'll also show us what you can predict about a person based on their self-description.

If you have a disability and are requesting accommodations in order to fully participate in this event, please email libraryevents@stonybrook.edu or call 631-632-7100.

Register now: https://library.stonybrook.edu/library-events/stem-speaker-series-measuring-self-identity/

Title: Cyberinfrastructure for forward prediction and inversion estimation with uncertainty quantification

Seminar Speaker: Dr. Mengyang Gu, Assistant Professor, Department of Statistics and Applied Probability, University of California, Santa Barbara

Abstract: In this talk, we introduce four useful tools for forward prediction and inversion estimation. The first tool is the parallel partial Gaussian process surrogate model for emulating expensive computer simulations with massive coordinates. The tool is implemented in the RobustGaSP package available in R, MATLAB, and Python, for predicting both scalar- and vector-valued outputs with uncertainty assessment. The second tool is implemented in the RobustCalibration package, which handles Bayesian data inversion or model calibration by one or multiple types of experimental observations. A unique feature of the package is the inclusion of fast surrogate models of both scalar- and vector-valued computer simulations that bypass the expensive simulation in one line of code. The third tool is implemented in the AIUQ package, available in both R and MATLAB. In this approach, we show that differential dynamic microscopy, a scattering-based analysis tool that extracts dynamical information from microscopy videos, is equivalent to fitting the temporal auto-covariance in Fourier space, based on a latent factor model we construct. We develop a more efficient estimator and reduce the computational cost to pseudolinear order with respect to the number of observations without approximation, by utilizing the generalized Schur algorithm for the Toeplitz covariance. In the last tool, we developed a new method called the inverse Kalman filter, which enables fast matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with a linear computational cost. These new approaches outline a wide range of applications that include emulating expensive simulation at molecular-, meso- and macro-scales, active learning with error control, nonparametric estimation of particle interaction functions, and data inversion from microscopy and velocity fields.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606285496?pwd=2yJYSG6lx8gMPiibzgAIBQtKHIjuHV.1
Meeting ID: 160 628 5496
Passcode: 472506
Abstract: Machine learning (ML) systems fueled by neural networks have entered our daily lives and led to scientific breakthroughs, but many open questions remain. After a nod toward the question of rigor with ML and recent progress, I'll turn to the theory of neural networks. I will argue that understanding neural networks inevitably leads to ideas from field theory (FT), which was already realized in the simplest case in the 1990s, and I will review some essential FT-for-NN results. I will then propose that the connection might be more general, an NN-FT correspondence of sorts, with neural networks providing a way to define a field theory. I'll end with comments on known results including the origin of interactions and various symmetries, but I will also list some open questions. The apparent non-sequitur in the title will be used as a rhetorical device to explore where we are and where we'd like to go.

https://scgp.stonybrook.edu/calendar/full-calendar
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.