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.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580

AI on Campus: Your Thoughts, Your Future

Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:

  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?

  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?

  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)

Dates/Times:

  • Wednesday, 2/4 at 2pm

  • Thursday, 2/5 at 12pm

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

Don't worry if you can't attend! You can still share your thoughts via video in our AI Zoom Room or via email: rose.tirotta-esposito@stonybrook.edu.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)

Abstract: Sea ice is crucial to Earth's climate, Arctic communities, and ecosystems, yet climate change is driving significant losses, threatening polar stability. Quantifying the long-term impacts of a declining sea ice cover requires tools which improve climate-timescale prediction and bring new understanding of climate interactions. In this talk, I discuss how meeting this challenge requires a multi-disciplinary approach. Climate models, while essential, suffer from systematic biases due to missing or inaccurate physics, leading to uncertainty in future projections. I show how data assimilation (DA) offers a statistical framework for integrating satellite observations with climate models to quantify systematic sea ice model errors. Using convolutional neural networks (CNNs), we can learn these errors based on the model's atmospheric, oceanic, and sea ice conditions--what I term a state-dependent representation of the error. This approach enables real-time corrections to subsequent model simulations, which systematically reduces global sea ice biases. I highlight key successes and challenges in developing this hybrid ML+climate modeling framework, including transfer learning to enhance online generalization of ML models, and new methods for integrating Python-based ML frameworks with Fortran climate model code. Finally, I introduce GPSat, a scalable Gaussian process-based tool for reconstructing complete sea ice fields from sparse satellite altimetry data. Together, the DA+ML framework and GPSat offer future opportunities for improving targeted model physics errors for more robust climate simulation.


IACS Seminar Speaker: William Gregory, Princeton University

Location: IACS Seminar Room
Abstract: The capacity to adapt machine learning models to various contexts, information, and objectives is particularly valuable. In this thesis, I focus on developing Class Conditional Guided Models. These are models that can be adaptively biased towards a class of interest via a conditional input. My primary focus lies in the efficiency of these models. They are constructed to require training only once, with the ability to quickly and conveniently adapt during testing time without necessitating fine-tuning or retraining.
Firstly, I propose RelationVAE, a novel generative model designed for few-shot scenarios, utilizing the prior knowledge of class similarity relationships. RelationVAE is designed to condition on the embeddings of the neighbor classes (i.e. classes with similarity relationships), to generate more reliable samples by making them more similar to the neighbor class. This enables adaptation of the generative model to the provided prior knowledge about class relationships.
As a second focus, I introduce scGAN, a shadow segmentation technique that enables adaptation to varying shadow distributions in different testing environments. scGAN is designed to condition on a sensitivity parameter, a scalar, to control the amount of the shadow detected. In the testing phase, the parameter is set to appropriate values, allowing the model to quickly adapt to specific test environments.
In my third contribution, I propose S-SEG, a methodology for fine-grained counting allowing adaptation to different granularities of fine-grained classes. In fine-grained problems, the distinction between classes is subtle and inconsistent across images, leading to variations in the granularity of the target class from one image to another. S-SEG is designed to be conditioned on an additional input, the sensitivity parameter, to control the granularities of the target class during inference.
My fourth contribution is a text-to-image synthesis method which allows controlling the number of the generated objects of a target class. I propose to generate an intermediate condition, the density map, which reflects the number of objects, together with their layout. This intermediate condition is used to effectively guide the generative model to generate objects with accurate counts.

Speaker: Vu Nguyen

Zoom: https://stonybrook.zoom.us/j/97114455337?pwd=Z4rB9dWcstlahUIs8PRrvQ9b2ZK2Df.1
Meeting ID: 971 1445 5337
Passcode: 272300
The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.  
What comes after today's large language models and deep neural networks? Join the Computing Community Consortium (CCC) for a virtual 30-min community chat led by David Jensen, CCC Council Member and lead author of the new CCC whitepaper, Envisioning Possible Futures for AI Research. Jensen will explore paradigm-shifting AI Research Futures like Neuro-Symbolic, Embodied, Multi-Agent, and Quantum AI, and then open the floor to the audience for an engaging Q&A discussion.

Register here.
Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration
The 20th International Conference on Emerging Technologies for a Smarter World (CEWIT 2025)

The Innovation Edge: Harnessing AI for the Future
Exploring Generative AI, Agentic AI, and Frontier Technologies Revolutionizing Healthcare, Defense, Energy, FinTech, and Beyond

Organized by the New York State Center of Excellence in Wireless and Information Technology (CEWIT) at Stony Brook University, our international conference is a destination for researchers, innovators and entrepreneurs, across borders and disciplines. CEWIT2023 conference attracted over 150 industry and academic participants worldwide. Over twenty-three presenters took the podium in breakout sessions and engaging panel discussions.

Continuing the tradition since the inception of our conference in 2003, CEWIT2025 will be a premier forum for presentations of cutting-edge research as well as the exchange and transfer of emerging technologies and innovative applications. We are expecting renowned speakers, presenters and panelists from industry, academia and government, beginning with a series of plenary presentations & a keynote, and followed by several conversational panels - all for an audience ready to network!


Location: The Center of Excellence in Wireless and Information Technology (CEWIT), Stony Brook University

Event Details: Visit CEWIT2025 site to learn more about the event

Questions/Concerns: CEWIT Conference Team at 631-216-7114 or info@cewit.org