University Libraries Presents: The SBU Libraries AI Club hosts their third AI Club Icebreaker for new students during WOW. Come see several projects the AI Club has been working on, how you can participate, and how you can join us!

Location: Melville Library Galleria

Register 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.

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

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

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.
Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, their giga-pixel size creates two intertwined bottlenecks: computational inefficiency, as prohibitive GPU memory makes standard end-to-end (E2E) training infeasible, and label inefficiency, as expert annotation is tedious and expensive. This dissertation confronts both challenges through novel architectures, training paradigms, and self-supervised learning methods for efficient WSI analysis.
To improve computational efficiency, this dissertation first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework training only a few prompts to guide large pre-trained models, reducing trainable parameters, memory, and training time. Third, this work proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy in 1D models. Fourth, it presents Locally Bi-directional Mamba (LBMamba), whose hardware-aware local backward scan integrates bi-directional scanning into a single forward pass, improving the throughput-performance trade-off of Mamba models.
To improve label efficiency, this dissertation proposes a precise location based matching strategy for self-supervised dense contrastive learning, which allows a local patch in one augmented view to match multiple overlapping patches in another, producing more accurate correspondences and superior features for dense prediction tasks like segmentation and detection. Additionally, to better scale multi-channel cell imaging modalities, this dissertation introduces ChannelSFormer, a channel-agnostic vision transformer that disentangles spatial and channel-wise reasoning through divided attention and channel class token, enabling effective representation learning across variable channel configurations in both self-supervised and supervised settings.
In summary, this dissertation presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.

Speaker: Jingwei Zhang

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/93175806292?pwd=xbtxnQyYGoThz5B1DyJxJxPF9lxiJE.1
Meeting ID: 931 7580 6292
Passcode: 314091

As generative AI (GenAI) continues to reshape the educational landscape, educators must critically examine its implications for course design. How can we adapt our courses to ensure meaningful learning in a post-GenAI world? How can we harness its potential while mitigating risks to student learning? This seminar explores the evolving role of GenAI in higher education, emphasizing learner-centered teaching practices--such as backward design, transparency, and active learning--as essential strategies for navigating both the opportunities and challenges posed by GenAI. We will examine how GenAI disrupts traditional models of teaching and assessment, highlighting course design choices that intentionally promote deep learning and critical thinking in this new era.

Speaker Bio: Dr. Lourdes Alemán is an Associate Director at MIT's Teaching and Learning Lab (TLL). She earned her Ph.D. in Biology from MIT, studying RNA interference (RNAi) with Professor Phil Sharp. She later completed a postdoc in curriculum innovation with Professor Graham Walker's HHMI MIT Education Group. As a postdoc and research scientist, she helped develop software tools for teaching experimental design and data analysis, including collaborations with the MIT-Haiti Initiative. Before joining TLL, she worked at MIT's Open Learning, supporting MIT faculty in blended and online education. At TLL, Lourdes trains graduate students and postdocs in college-level teaching, advises faculty on classroom innovation, and previously designed and taught a hands-on biology module on novel antibiotic discovery for first-year students. She has served on university committees focused on mentoring and advising. Drawing from her experiences as a Cuban immigrant student, she developed MIT's first curriculum on growth mindset and co-founded Flipping Failure, a campus-wide initiative for students to share their stories of academic challenges and the strategies they have used to overcome them.

IACS and the Dept of Ecology and Evolution invite you to a PRODiG+ Fellowship Seminar.

Computational Approaches to Understanding and Forecasting Biodiversity Responses to Global Change

Summary: Advancing biodiversity forecasting requires developing computational approaches that better represent ecological complexity. In this talk, I will discuss how my research integrates field observations, predictive modeling, machine learning, and spatiotemporally explicit environmental data to develop more mechanistic and reliable forecasting tools.

Speaker: Dr. Anna Thonis, a postdoctoral researcher in NYU's Winchell Lab and Founder and Co-Chair of the IUCN SSC Anoline Lizard Specialist Group, studies how anthropogenic change reshapes the distributions of reptiles. Combining field and quantitative modeling methods, her work focuses on the ecology and conservation of Puerto Rican and urban Anolis lizards. Her website can be accessed here.

Location: IACS Seminar Room, and via Zoom.

Zoom: https://stonybrook.zoom.us/j/97023584426?pwd=HrnrYeYJfVUhaS8iOKjWF3Tbh8HYS1.1&jst=2
ID: 97023584426
Passcode: 241762
2026 MIC Symposium on Multilingualism | Featured Talk

Searching for Common Ground in Human-AI Interaction (What Gen AI Can't Do)

Abstract: For many of us, the alien invasion of LLMs has been unsettling, at best. Who knew that monstrous quantities of text and compute power could simulate human discourse processing? But despite claims about emergent theory-of-mind in reasoning models, LLMs remain stochastic parrots: they do not collaborate, project co-presence, form conceptual pacts, accurately signal commitment to what they say, or accrue common ground. This lack of pragmatic competence is clear from our recent studies of narrative and referential communication by AI vs. humans.

Speaker: Susan Brennan is SUNY Distinguished Professor at Stony Brook University in Cognitive Science within the Department of Psychology, with affiliations in Computer Science and Linguistics. Her research uses eye-tracking, experimental, behavioral, and observational methods to study the psycholinguistics of communication (verbal, nonverbal, and multimodal), adaptive language use, and the human use of technology, including spoken dialogue systems. She currently directs the NSF-funded Research Traineeship project, Detecting and Addressing Bias in Data, Humans, and Institutions, a collaboration among PhD. programs in Cognitive Science, Social & Health Psychology, Computer Science, Applied Math & Statistics, Linguistics, Political Science, Economics, Africana Studies, and Women's Studies. This traineeship supports students' AI and bias-related convergent research projects that include assessing potential for LLVMs (Large Language-Vision Models) to act as interlocutors in spontaneous dialogue as well as collaborating with the Innocence Network about AI tools. She holds a Ph.D. in cognitive psychology from Stanford University, an M.S. in visual studies from MIT (from what is now known as the MIT Media Lab), and a B.A. from Cornell University in cultural anthropology. She has worked in industry at Atari Research, Hewlett-Packard Labs, and Apple Computer, as well as at NSF as a program officer for the Graduate Research Funding Program.

Faculty Response Panel:
  • Jiwon Hwang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
  • Rose Tirotta-Esposito (Director, Center for Excellence in Learning and Teaching)
  • Lav Varshney (Director, AI Innovation Institute)
  • Josh Zhang (Associate Professor, Sociology and Institute for Advanced Computational Science)




Location: Humanities 1006