Professor Petar M. Djuric, SUNY Distinguished Professor and Savitri Devi Bangaru Professor in Artificial Intelligence at Stony Brook University, has been selected as a plenary speaker at the upcoming 23rd IEEE Statistical Signal Processing Workshop (SSP 2025). The event will be held from June 8-11, 2025, in Edinburgh, Scotland, and is one of the premier international forums for the latest advances in statistical signal processing.

Professor Djuric's plenary talk, titled Quantifying causal relationships: Dynamic strengths, attributions, and confounders, will take place on June 10 from 9:00 AM to 10:00 AM EST. His presentation addresses foundational challenges in data-driven causality, proposing novel methodologies for quantifying causal strength in both static and dynamic systems, with special attention to latent confounders and attribution analysis.

This work has broad implications across disciplines including healthcare, economics, and climate science--areas where causal understanding drives critical decisions and innovations.

Professor Djuric has been a long-standing leader in the fields of machine learning and signal and information processing. After receiving his Ph.D. from the University of Rhode Island, he joined the faculty at Stony Brook University, where he served as Chair of the Department of Electrical and Computer Engineering from 2016 to 2023. He is also the founding Editor-in-Chief of the IEEE Transactions on Signal and Information Processing Over Networks and a Fellow of IEEE, EURASIP, AAIA, and AIIA.

Early bird registration for the workshop is open until April 30, 2025. For more information, visit the official SSP 2025 website.

Submit an abstract celebrating research, new discoveries and achievements in medicine and science!

We encourage faculty, nurse practitioners, post-doctoral fellows, fellows, residents, medical students, graduate students and undergraduate students to submit an abstract. Original research, case reports and case series are welcome.

Abstract submission deadline: FEBRUARY 7, 2025

For more details, visit here.

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

CSE 600 Seminar Series | Fall 2025

Speakers: Jessica Roman, Simone Binneman, Michalis Polychronakis

Abstract:

Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden in the data we already have. The key lies in using AI not as a black-box predictor, but as a tool for interpreting data through its underlying physical process.

I will demonstrate how AI, when integrated with the physics of light propagation, can serve as a computational lens to overcome fundamental limitations in fields ranging from biomedicine to astrophysics. Specifically, I will showcase two compelling applications: non-invasive imaging through scattering biological tissues, and detecting faint exoplanets against the overwhelming brightness of their host stars.

These methods represent a departure from traditional learning-based approaches that rely on fitting models to training labels and hoping for generalization. Instead, with physics-informed strategies that decode how light propagates, we can transform raw measurements into scientifically meaningful insights--without requiring costly hardware upgrades or human-annotated datasets. Finally, I will outline future directions for combining AI with physical principles, enabling us to unlock more phenomena once considered hidden and accelerating discoveries in healthcare, astronomy, and beyond.

Short Bio:

Brandon Y. Feng is a Postdoctoral Associate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and a Visiting Scientist at the Harvard-Smithsonian Center for Astrophysics. His research bridges artificial intelligence and physics to expand the limits of human and machine vision. He develops AI-driven methods that reveal hidden patterns in complex visual data, driving breakthroughs in areas such as exoplanet detection and imaging through scattering tissues. His work has been published in top venues, including Science Advances, CVPR, ICCV, ECCV, and NeurIPS, and has been featured in Science.org, New Scientist, and Phys.org. He holds a Ph.D. in Computer Science from the University of Maryland, along with a B.A. in Computer Science and Statistics and an M.S. in Statistics from the University of Virginia.