The AI Community will be hosting our very first Datathon💡📊

Ready to turn data into groundbreaking insights? 🧠

Compete in our Datathon, where you'll analyze real-world data 📈 and share innovate solutions in these tracks:

🏫 Student Life

🌱 Environment & Sustainability

💉 Health & Wellness

💰 Finance & Economics

Whether you're a data pro or just starting out, this is your chance to network, learn, and win exciting prizes! 🏆🎉 Bring your creativity 🧩 collaborate with fellow students 🧑‍🤝‍🧑 and gain hands-on experience showcasing your analytical skills 💻

Submissions will be judged by professors 🧑‍🏫 so take this chance to impress them!

There will be free food ☕ and games 🎲 to fuel your brain and imagination! Don't miss out--register now and unleash the power of data! 🔥✨

Registration Form: https://forms.gle/6XYMfmhyAByzFpxz5

Time: Friday (4/4) 10:30am - 5pm ⏰

Location: Bauman Center 📍

Fall 2026, Wednesdays 2 to 3:20 pm, NCS 220 and Zoom link to be announced soon.

The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).

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 Ph.D. 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. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome!
University Libraries Presents:
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools first hand, not just as users, but as critical investigators.
Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

RSVP on SBEngaged

Location: Melville Library, Central Reading Room, Lab B

Abstract: The development of embodied AI has largely focused on scaling data and computational power, often at the cost of energy efficiency. In contrast, biological intelligence achieves remarkable adaptability with minimal resources, inspiring a shift toward neuromorphic AI, an approach that mimics the structure and dynamics of biological neural systems. In this talk, I will explore the promises and challenges of neuromorphic computer vision from three key perspectives: algorithms, robot actions, and data. First, I will discuss algorithmic advances, including continuous visual hull reconstruction, continuous-time human motion field estimation, and unsupervised independent motion segmentation. Next, I will illustrate how neuromorphic vision enables agile robotic actions by leveraging event-based perception for real-time decision-making. Finally, I will address challenges in training data-driven models with event data, highlighting strategies to enhance data availability and efficiency. By integrating these elements, neuromorphic AI paves the way for energy-efficient, high-performance embodied intelligence in dynamic real-world environments.

Speaker Bio: Ziyun (Claude) Wang is a fifth-year Ph.D. student in the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania, advised by Professor Kostas Daniilidis. His research focuses on developing algorithms for neuromorphic computer vision and integrating them with real hardware to enable agile perception in embodied AI systems. Prior to his Ph.D., he worked at the Samsung AI Center New York, where he developed 3D reconstruction techniques for robotic applications and earned three patents. He also contributed to the Apple Vision Pro team, enhancing user comfort for AR glasses. His research work has been recognized at major computer vision, robotics, and machine learning venues including the AAAI Conference on Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), International Conference on Learning Representations (ICLR), Conference on Computer Vision and Pattern Recognition (CVPR) workshops, and IEEE Robotics and Automation Letters (R-AL), with an oral presentation at ECCV placing in the top 2.7%. His research aims to drive the development of next-generation bio-inspired AI systems, enabling more efficient, adaptive, and intelligent embodied perception.
Presented by Stony Brook University Department of Biomedical Informatics and Long Island Network for Clinical and Translational Science (LINCATS).

The seminar aims to empower participants with the knowledge and skills necessary to harness AI effectively in clinical practice and research. It will equip attendees with practical insights, case studies, and interactive discussions led by experts in both AI and medicine, fostering a collaborative environment where attendee can explore how to overcome barriers and maximize the potential of AI in transforming modern healthcare delivery.

All Stony Brook Audiences Welcome.
Please note: This exciting event is open to all Stony Brook Faculty/Staff/Students. While the overarching theme for this event is the application of AI in medicine, the event is designed to bridge the professional practice gap that exists between cutting-edge AI research and its practical implementation in clinical settings, While AI holds immense promise for transforming healthcare delivery, many physicians and researchers lack the foundational knowledge and practical skills needed to effectively integrate AI into their daily practices.

THIS CONFERENCE IS FOR STONY BROOK UNIVERSITY & HOSPITAL FACULTY/STAFF & STUDENTS ONLY.


Registration link: https://cme.stonybrookmedicine.edu/continuing-medical-education/conferences/235/bench-to-bedside-understanding-the-practical-application-of-ai-in-medicine-2024/10/17/2024

FOR QUESTIONS
joseph.cesaria@stonybrookmedicine.edu
mary.saltz@stonybookmedicine.edu

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

Abstract:
People shift their visual attention to gather and prioritize information from their surroundings, helping them navigate complex environments. Understanding these attentional shifts involves decoding the features that guide where attention is directed (spatial areas of focus) and when attention shifts (timing). Decoding these processes can aid applications from interface design to medical diagnosis. However, prior models have not fully explored the underlying factors addressing these aspects. In this dissertation, we study the factors that guide visual attention across diverse image types, spanning natural images, graphic design documents, and whole slide images (WSIs) of cancer tissues, while also predicting visual attention based on these factors.
First, we propose a method to quantify object recognition uncertainty as a factor influencing spatio-temporal attention (where and when) in natural images. We found that it plays a larger role than bottom-up saliency in guiding visual attention. Second, we analyze graphic design documents such as webpages, comics, posters, mobile UIs, etc., which differ from natural images in that they are designed to convey specific messages or elicit desired viewer response. We propose a unified and interpretable deep learning model that predicts both static and dynamic visual attention behavior (addressing where and when) by integrating document layout and content saliency as factors, enhancing attention prediction performance. Finally, in the domain of digital pathology, we investigate pathologists' attention during their examination of giga-pixel WSIs of prostate cancer with an objective to aid in the development of computer-assisted pathology training and clinical decision support systems. Using a digital microscope interface, we collected the largest known dataset of pathologist attention, which allows us to study the factors that guide their spatial and temporal attention patterns (where and when) and develop predictive models. Our study explores key factors guiding their attention, including magnification, slide staining, the nature of the diagnostic task, and their expertise. Motivated by this analysis, we propose deep learning models to solve two tasks: 1) predicting pathologist attention via spatial (heatmaps) and spatio-temporal (scanpaths) models, and 2) inferring pathologist expertise level, both essential technical components towards developing an AI-assisted pathology training pipeline.

Speaker:
Souradeep Chakraborty

Location: New Computer Science Bldg., Room 220

Zoom Link: https://stonybrook.zoom.us/j/9755288447?pwd=TW95T2xqOUZjRnlqcnVFcUQvN0JMdz09
Meeting ID: 975 528 8447
Passcode: 338037




Abstract: Trustworthy AI deployment in high-stakes domains requires systems that are fair, private, robust, and controllable as they scale. Yet these demands are often pursued through ad-hoc approaches, lacking a systematic understanding of the inherent trade-offs between competing objectives. We add fairness regularizers and hope bias decreases. We train on massive datasets and hope the model learns the underlying logic of how concepts combine, rather than memorizing statistical shortcuts. We encrypt data and hope the resulting computational overhead remains manageable. But hope isnot a science.
In this talk, I argue that what trustworthy AI lacks is not better heuristics but a deeper science of what these properties fundamentally cost and what is achievable. Before we can fix a system, we must map the terrain: what trade-offs are unavoidable, what regions of performance areunreachable, and how far current methods fall from what is actually achievable. My research builds this map across fairness, privacy, robustness, and controllability, following a common methodology: diagnose where models fail, characterize the fundamental limits any method must obey, and design systems that approach those limits. I will present this framework, its extension to scientific applications where we replace statistical constraints with physical laws to ensure AI systems remain grounded in reality, and a vision for scaling these principles to the rapidly expanding ecosystem of composed and interacting AI systems.


Bio: Dr. Vishnu Boddeti is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, where he leads the Human Analysis Lab (HAL). His research develops mathematical frameworks for trustworthy AI, spanning fairness, privacy, robustness, and physics-informed learning, with an emphasis on characterizing fundamental limits and building systems that achieve them. His work has been supported by NSF, NIST, DARPA, ONR, Ford, and others, and recognized with a Meta Research Award (2021). His research has been featured on the cover of Nature, recognized as an Editor's Highlight in Nature Communications, and received multiple best paper awards, including the 2024 IEEE-CCF Cloud Computing Best Paper Award and the TMLR Outstanding Certification Finalist (2023). He serves as Senior Area Editor for IEEE Transactions on Information Forensics and Security and completed his PhD in ECE from Carnegie Mellon University in 2012.

Location: NCS 120

International Love Data Week is a global event dedicated to celebrating data in all its forms. This year, Stony Brook University is excited to celebrate Love Data Week with a series of 30-minute webinars aimed to promote proficiency with data, showcase innovative data projects, and foster a community of data enthusiasts across campus. Hosted by the Division of Educational & Institutional Effectiveness and facilitated by the Office of Educational Effectiveness, we invite all SBU faculty, staff and students to join in the festivities, learn from colleagues in our campus community, and fall in love with the power of data!

Learn more here.


Abstract: The rapid growth of observational data presents unprecedented opportunities to enhance both the predictability and mechanistic understanding of Earth systems. However, fully harnessing big Earth data needs computational frameworks that bridge the gap between physics-based models and machine learning. In this talk, I will first demonstrate how AI methods can significantly improve the prediction of environmental systems. Despite their predictive accuracy, machine learning models often lack physical interpretability, limiting their ability for scientific inquiry. To address this, I will introduce the developed hybrid, differentiable modeling framework that unifies physical models with machine learning in an end-to-end trainable system. This framework autonomously learns from large observations while maintaining physical clarity. The machine learning components can be seamlessly embedded into physical backbones to assimilate multi-source data, support automatic parameterization, and represent uncertain processes. I will showcase applications of this framework in simulating and understanding the terrestrial water cycle and its interactions with ecosystems at continental and global scales. This talk will highlight how differentiable modeling not only improves the modeling ability in both data-rich and data-scarce scenarios, but also provides a systematic pathway to enhancing model structures, deciphering uncertain physical relations, and facilitating knowledge discovery in Earth system sciences.


IACS Seminar Speaker: Dapeng Feng, Stanford Univeristy

Location: IACS Seminar Room