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
AI + Music Seminar - The meeting will consist of introductions and organizational discussions, aimed at understanding participants' interests. We'll discuss what the seminars can focus on going forward.
CSE 656 Seminar in Computer Vision 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.

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.



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1


Dates: 

Wednesday, March 3, 2021 - 6:00pm to 7:30pm

Location: 

Zoom - contact events@cs.stonybrook.edu for Zoom info.

Event Description: 

Women in Computer Science (WiCS), the Society of Women Engineers (SWE), and the Stony Brook Robotics Team (SBRT) are collaborating to host an event called Inspiring Women in STEM Academia: A Community Dialogue to address the lack of female representation in STEM academia. 
 

All are invited to attend so they may gain a better understanding of the challenges faced by their female colleagues and hear perspectives on how they can offer support in the workplace. Given the shockingly disproportionate number of female professionals in STEM academia, we feel that this event would be extremely beneficial for male faculty to listen to and amplify their voices.

It will begin with a discussion panel consisting of Stony Brook professors and faculty who will provide valuable insight into the issue. From there, we will split into smaller discussion groups where student and faculty attendees will be able to voice their opinions, hear about the thoughts/experiences of others, and participate in an engaging discussion with panelists.

The event will be held on March 3rd from 6:00 - 7:30 PM on Zoom.
 

The following Stony Brook faculty will be panelists:

Dr. Aruna Balasubramanian - Computer Science Professor, WiCS Advisor, WPhD Advisor

Dr. Xinwei Mao - Civil Engineering Assistant Professor

Urszula Zalewski - Director of Experiential Learning, Career Center Advisor (Healthcare)

Dr. Heather Lynch - Ecology and Evolution Professor, Lynch Lab for Quantitative Ecology

Karen Kernan - URECA Director, Simons Summer Research Program Director

Dr. Eszter Boros - Chemistry Assistant Professor, Boros Lab

Dr. Maria Nagan - Chemistry Lecturer, Nagan Research Lab

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. 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.

We meet every other Tuesday at noon in CDSD's Training Room (building 725, room 2-124) 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.

Abstract: Identifying model Hamiltonians is a vital step toward creating predictive models of materials. W​e combined Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic X-ray scattering (RIXS) spectra within the single atom approximation. To evaluate the efficacy of our method, we tested it on experimental RIXS spectra for several materials and demonstrated that it can reproduce results obtained from hand-fitted parameters to a precision similar to expert human analysis while providing a more systematic mapping of parameter space. Our work provides a key first step toward solving the inverse scattering problem to extract effective multi- orbital models from information-dense RIXS measurements, which can be applied to a host of quantum materials.

Biography: Marton Lajer is a postdoctoral researcher at the Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory. Marton​ obtained his PhD in theoretical physics at the Eotvos Lorand University, Hungary, in 2021. He was a​ junior research fellow at the Wigner Research Centre for Physics in Budapest before joining BNL in September 2022. His background spans various analytical and performance-critical numerical methods, mostly in the context of low- dimensional quantum field theories and quantum many-body systems. His research currently focuses on incorporating AI-enhanced methods to various problems in inelastic spectroscopy.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

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

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.

CSE 600 Seminar Series | Fall 2025


Abstract: Virtual worlds are prevalent in applications ranging from entertainment, healthcare, retail, to workforce training. With the demand for virtual content growing exponentially, the market for such content is valued at over $200 Billion, which is accelerating the need for advanced computational solutions. In this talk, I will focus on a key challenge in virtual content creation: simulating autonomous agents.
I begin by overviewing this problem domain, through the lens of a physics-based dynamics simulation, which enables the simulation of thousands of agents at interactive rates with GPU programming, achieving a level of performance previously unattainable.
Next, I'll present our recent results in Deep Reinforcement Learning for multi-agent navigation, which enable refined, reward-based strategies to control agent movement. We demonstrate how these techniques can simulate realistic crowds, with broad applications in pedestrians, robots, and swarms. Lastly, I conclude my talk by discussing our lab's work-at-large and the wide range of research opportunities in this emerging area.

Speaker: Tomer Weiss is a professor with New Jersey Institute of Technology since 2020. He received the best student, presentation, and best paper awards in various ACM SIGGRAPH conferences for his work on simulating multi-agent crowds. He was also a finalist in both ACM SIGGRAPH Thesis Fast Forward, and the ACM SIGGRAPH Asia Doctoral Symposium in 2018. He received his PhD in computer science from UCLA in 2018. His research interests include multi-agent dynamics, scene understanding, and interactive visual computing.

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.

AI and Edge Processing Co-Design for Radiation Detectors

Abstract: Artificial Intelligence (AI) offers exciting new opportunities for enhancing the performance of radiation detectors, ultimately leading to improved physics outcomes. Furthermore, with the explosive growth in data rates being seen by next-generation radiation detectors, deployment of AI algorithms at the edge by embedding intelligence within or near the detector front-end can be transformative. Such integration enables real-time data filtering, noise suppression, feature extraction, and adaptive control, while reducing downstream bandwidth and power consumption. This talk will cover three efforts that bring AI to the forefront of detector technology. First, we demonstrate how AI-based algorithms can be used for position reconstruction in virtual Frisch-grid (VFG) detectors by compensating for charge transport distortions and detector non- uniformities, leading to significantly enhanced fidelity in imaging of gamma-ray interactions. Second, we present a smart readout application specific integrated circuit (ASIC) that combines digital signal processing with co-designed artificial neural networks to enable on-chip regression and classification of detector signals, while meeting stringent constraints on accuracy, speed, and area. Finally, we introduce our recent efforts related to the development of electro-photonic processing architectures that integrate CMOS electronics and silicon photonics for near-sensor AI acceleration. These architectures aim to leverage cross-disciplinary co-design from algorithms to hardware, to achieve low latency and energy-efficient processing of detector data.

Biography: Dr. Prashansa Mukim is an early-career researcher in the Instrumentation Department at BNL, where she works on the design of front-end electronics for extreme environments and the development of co-design methodologies for novel processing modalities and beyond-CMOS technologies. Prior to joining BNL, she was a post-doctoral researcher at the National Institute of Standards and Technology (NIST) in Maryland, where she focused on characterizing the properties of CMOS circuits at cryogenic temperatures and applications of spintronic devices for neuromorphic computing. She received her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2021.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1608585935?pwd=UemgEkqijfNf3vIJIGuOa2MdjsunaT.1

Meeting ID: 160 858 5935
Passcode: 076033