Abstract: Sub-grid turbulence is challenging to resolve in climate models; therefore, it is parameterized. Traditionally, turbulent parameterizations have relied on physics-based and equation-based approaches. However, ad hoc and uncertain components in these parameterizations introduce uncertainty in future climate predictions. Recently, data-driven techniques have emerged as an alternative for modeling sub-grid fluxes. I will demonstrate the use of machine learning to model vertical turbulent fluxes in the ocean surface boundary layer and its impact on reducing biases in NOAA's Geophysical Fluid Dynamics Laboratory ocean climate model.

I will show how neural networks, trained to predict the eddy diffusivity profile from high-fidelity yet computationally expensive turbulence schemes, enhance the vertical mixing scheme in the climate model. These networks replace ad hoc components while maintaining the conservation principles of the standard ocean model equations. The enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and modestly improving tropical upper-ocean stratification in ocean-only global simulations. Furthermore, simplified equations that can replace the neural networks show similar improvements but with lower computational cost and better interpretability. They point to structural deficiencies in the baseline parameterization. This work is one of the first successful applications of machine learning to improve a sub-grid parameterization of turbulent mixing in ocean climate models.

IACS Seminar Speaker: Aakash Sane, Princeton University

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97764942108?pwd=MzCWupCe3L9mKdrgfO2bJg3GBbvXuf.1
Meeting ID: 977 6494 2108
Passcode: 519324

Abstract: Much like other AI for Science domains, polymer design poses significant challenges. It requires grounding in empirical data and physical laws, precise handling of domain-specific structured representations, and compositional reasoning over multiple interacting constraints--all while working with limited data.

To address these limitations, we introduce PolyBench, a large-scale benchmark comprising over 125K polymer design and analysis tasks grounded in verified experimental and synthetic data. PolyBench includes tasks created from a wide range of data sources and presents diverse structural, property-driven, and synthesis-oriented reasoning problems. Tasks in PolyBench are organized from simple to complex analytical reasoning problems, enabling generalization tests and includes diagnostic probes to evaluate model capabilities. Additionally, to support effective domain alignment, we propose a knowledge-augmented reasoning distillation framework that enriches the dataset with structured chain-of-thought supervision derived from expert-informed reasoning strategies.

Small language models (7B-14B parameters) trained on PolyBench substantially outperform comparably sized baselines and, in many cases, exceed the performance of larger closed-source frontier models on polymer reasoning tasks, while also demonstrating improved transfer to external polymer benchmarks. Last, we conduct a diagnostic study that reveals a compositionality gap: despite strong performance on decomposed sub-questions, models struggle to integrate multiple interacting constraints and intermediate reasoning steps, highlighting fundamental limitations in current scientific language models.

Speaker: Dikshya Mohanty

Location: NCS 115/Online

Zoom: https://stonybrook.zoom.us/j/94746001760?pwd=BCAd8gu7cXLn3PXM6kkbh11V6r0Mr7.1
Meeting ID: 947 4600 1760 Passcode: 987917

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.
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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.

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.

Abstract: This presentation will begin by outlining key challenges facing the modern power grid and summarizing our group's research efforts to address them. It will then discuss how AI and machine learning are reshaping the grid modernization. The major focus of the talk will highlight a range of AI/ML applications we have developed in recent years to enhance grid operation, planning, control, and security.

Biography: Meng Yue is currently leading the Grid Modernization and Security Group in the Interdisciplinary Science Department at Brookhaven National Laboratory (BNL). He received his Ph. D. from Michigan State University in electrical engineering. His major research interests include power system modeling, simulation, and control, and applications of AI/ML- and quantum machine learning and quantum computing in operation, planning, and security of the future grid.

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.

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

Abstract: Modern decision-making increasingly relies on complex data, imperfect models, and limited domain expertise--yet decisions must still be made with confidence and accountability. This talk presents a research perspective on visual analytics as a bridge between data, models, and human judgment. Through three case studies spanning public-health risk analysis, multivariate scientific visualization, and causal model auditing with large language models, I will show how interactive visualization can reveal structure in high-dimensional data, support reasoning under uncertainty, and help humans critically assess both statistical and AI-generated explanations. Together, these examples illustrate how visual analytics enables users not only to explore data, but to form, challenge, and refine beliefs that underpin scientific and societal decisions.

Bio: Klaus Mueller received his Ph.D. in Computer Science from The Ohio State University in 1998. He is a Professor in the Department of Computer Science at Stony Brook University and a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. He currently serves as the Acting Chair of the Department of Technology and Society at Stony Brook. From 2012 to 2015, he was the Founding Chair of the Computer Science Department at SUNY Korea, where he also served as Vice President for Academic Affairs and Finance for two years.
His research interests span visual analytics, explainable AI, machine learning and data science, human-centered responsible AI, fairness, belief modeling and personalized communication, virtual and augmented reality, and computational and medical imaging. Dr. Mueller received the U.S. National Science Foundation Early Career Award in 2001, the SUNY Chancellor's Award for Excellence in Scholarship and Creative Activity in 2011, and the Meritorious Service Certificate and Golden Core Award of the IEEE Computer Society in 2016. In 2018, he was inducted into the U.S. National Academy of Inventors.
To date, he has authored more than 300 peer-reviewed journal and conference papers, which have been cited over 15,000 times. He is a frequent speaker at international conferences, has organized or participated in 18 tutorials, chaired the IEEE Visualization Conference in 2009, served as elected Chair of the IEEE Technical Committee on Visualization and Computer Graphics (VGTC) from 2012-2015, and was Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics from 2019-2022. He is a Fellow of the IEEE.

Location: NCS 120
Stony Brook University Northern California Alumni Chapter - Institute for AI-Driven Discovery and Innovation Panel

Join us for a Northern California Alumni and Friends luncheon followed by a panel discussion, celebrating the Institute for AI-Driven Discovery and Innovation, moderated by Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences.

Panel Discussion with:
Richard Bravman '78, Chief Strategy Officer, Affinity Solutions
Jalal Mahmud, PhD '08, Master Inventor, IBM Watson
Reza Raji '86, CEO, Xenio Systems
Andrew Protter, PhD '83, Chief Scientific Officer, Auansa Inc.

Moderated by:
Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences

Click here for more information and to register.