This workshop is intended for researchers, practitioners, students, and industry professionals in AI, robotics, machine learning, human-robot interaction, and related fields.

Workshop Overview:

Instead of learning from data alone, an embodied AI system learns through its movements, sensors, and interactions with the environment. This form of active, experience-based learning, informed by ongoing self-evaluation of its own abilities, enables embodied AI systems to adapt on the fly, understand context rather than just commands, and collaborate with humans in more natural and trustworthy ways.

Workshop Goals:

  1. Foster interdisciplinary dialogue across AI, robotics, and cognitive science.
  2. Identify key challenges and future research directions in embodied intelligence.
  3. Examine the role of embodiment in advancing toward AGI.

This workshop is Invitation-only. Please email Dr. IV Ramakrishnan (ram@cs.stonybrook.edu) to attend.

Read the announcement: https://mcusercontent.com/237207911c0fd4c1f78dd8524/files/070dec2e-a2f5-143e-0fe2-c4ebecdb5193/Embodied_AI_Workshop_Invitation_.pdf

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.
The International Neuroethics Society (INS) Speaker Series on AI & Consciousness

Abstract: Colln Allen and I noted in our 2008 book Moral Machines: Teaching Robots Right From Wrong, that consciousness, a theory of mind, sociability, situational awareness and embodiment are all supra-rational (beyond reason) capabilities that contribute to making ethical decision Whether any of these can be fully instantiated in machines remains an open question. Nevertheless, moral decision making in the digital age will require an evolution in and refinement of specific skills for both humans and for AI. I call one of these evolutions in moral decision making capabilities tradeoff ethics and another a silent ethics. Aspects of this social, and not just technological evolution, will require research by neuroscientists.

Speaker Bio: Wendell Wallach has an international reputation as an expert on the ethics and governance of emerging technologies, particularly artificial intelligence and biotechnologies. He is also senior advisor to The Hastings Center and a scholar at the Yale University Interdisciplinary Center for Bioethics where he chaired Technology and Ethics studies for eleven years. Wallach's latest book, a primer on emerging technologies, is entitled, A Dangerous Master: How to keep technology from slipping beyond our control. He co-authored (with Colin Allen) Moral Machines: Teaching Robots Right From Wrong. Wallach has been referred to as, a Godfather of AI Ethics.

Pre-register here (required): https://umaryland.zoom.us/meeting/register/sPpiR_drR4-9JYDhI2NhJg

Ready for Round Two? Dr. Zach Justus Returns! Join us on October 30, 2025, in the SBU Hilton Garden Inn. Buckle up your curiosity for a high-energy morning session with the engaging Dr. Zach Justus as we navigate how GenAI is reshaping not just how we teach, but what we teach. With real talk and questions that hit hard like Are students learning what we think we're teaching? This is your chance to rethink your program's true destination. Whether you're looking to pick up a few takeaways or chart a new direction entirely, this symposium is your space to explore, reflect, and act.

Check-in and breakfast will begin at 8:30 a.m. in order to begin our program promptly at 9:00 a.m.

Registration will remain open until October 15 or until the event reaches capacity. If closed, please contact educationaleffectiveness@stonybrook.edu to request a spot on the waitlist.

Join us as we celebrate this year's Brook & Beyond Challenge finalists.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.
Abstract: Graphs are a universal language of science. Molecules, materials, quantum systems, and knowledge bases can all be naturally represented as graphs. This talk explores how graph-based artificial intelligence is emerging as a powerful engine for scientific discovery. Using molecular design as a guiding example, we examine how modern graph AI enables machines not only to analyze complex scientific structures but also to generate new ones. We will discuss graph neural networks for learning predictive models of molecular properties, graph generative models for constructing novel chemical structures, and emerging multimodal graph-language models that support inverse design and synthesis planning. Together, these advances make graph AI more scalable, interpretable, and data-efficient--key capabilities for real-world scientific discovery. As artificial intelligence enters the era of foundation models, the next frontier lies in multimodal reasoning. Scientific knowledge is not purely textual; it is expressed through structures, code, and experimental data. By integrating graph representations with large language models, we move toward AI systems that can reason across multiple modalities and engage with scientific knowledge in its native forms. Looking ahead, we envision AI systems that behave less like tools and more like collaborators in the scientific process--generating hypotheses, designing candidate structures, planning experiments, interpreting results, and iteratively refining ideas through cycles of success and failure. In this vision, multimodal and agentic AI will enable scientists to explore vast and previously inaccessible design spaces, accelerating breakthroughs across domains ranging from drug discovery and materials innovation to software systems and quantum technologies.

Bio: Jie Chen is an interdisciplinary researcher working at the intersection of computing and mathematics, with a current focus on foundation models and AI agents for scientific discovery. His research integrates machine learning, statistics, scientific computing, and numerical linear algebra, with contributions spanning graph neural networks, multimodal graph LLMs, graph structure learning, scalable Gaussian processes, graph coarsening, and matrix functions. He is widely recognized for transformative contributions to graph-based deep learning and large-scale statistical modeling, and for bridging theory with real-world scientific and engineering applications. Dr. Chen has led externally funded, multi-institutional research programs supported by Shell, Evonik, and the U.S. Department of Energy, with applications in materials discovery, financial forensics, and power system resilience. He previously served as a Senior Research Scientist and Manager at IBM Research and the MIT-IBM Watson AI Lab, and as a Postdoctoral Fellow at Argonne National Laboratory. He has published extensively in top-tier AI, statistics, and applied mathematics venues, and his work has been recognized by multiple IBM Outstanding Technical Achievement Awards and the SIAM Student Paper Prize. He earned his Ph.D. in Computer Science from the University of Minnesota and his B.S. in Mathematics with honors from Zhejiang University.

Location: NCS 120
Time: May 5, 2022, Thursday, 02:00 PM Eastern Time (US and Canada)
Place: New Computer Science (NCS) Room 220, and Zoom

Zoom link: https://stonybrook.zoom.us/j/95948672934?pwd=d3ZDcUJkK3VweFBDVWhIVDhtaFU2Zz09
Meeting ID:  959 4867 2934
Passcode:  082036

Title:  Generative Adversarial Learning using Optimal Transport

Abstract: 

Generative Adversarial Learning (GAL) aims to learn a target distribution in an adversarial manner. A Generative Adversarial Network (GAN) is a concrete implementation of GAL using a discriminator and a generator that play a min-max game. GANs have been used in many machine learning and computer vision applications. However, GANs are known to be hard to train, mainly because a min-max saddle point optimization problem needs to be solved in GAL. In this thesis, I investigate several methods to improve generative adversarial learning using Optimal Transport (OT). 

Previous Wasserstein GANs (WGANs) do not compute the correct Wasserstein distance to train the discriminator. To address this problem, I propose WGAN-TS that uses the L1 transport cost and computes the correct Wasserstein distance to train the discriminator. To ensure the local convergence of WGANs, I propose WGAN-QC that adopts the quadratic transport cost. I prove that WGAN-QC not only computes the correct Wasserstein distance but also converges to a local equilibrium point. To compute the Wasserstein distance over the whole dataset, I propose to use Semi-Discrete Optimal Transport (SDOT) to match noise points and the real images during GAN training. To measure the quality of an SDOT map, I use the Maximum Relative Error (MRE) and the L_1 distance between the target distribution and the transported distribution obtained by an OT map. I propose statistical methods to estimate the MRE and the L_1 distance. I propose an efficient Epoch Gradient Descent algorithm for SDOT (SDOT-EGD). To deal with the 2D special case of GAL, I propose to use OT to learn 2D distributions. In particular, I adopt OT to match persistent diagrams in training a topology-aware GAN and learn density maps in the crowd counting task. Finally, I use OT and the topological maps of the crowd to improve the crowd counting performance and propose a topology-based metric to measure the quality of the crowd density maps.

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.

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.

AI-Driven Physics-Informed Phase Retrieval from a Single X-ray

Abstract: X-ray phase-contrast imaging enables the visualization of weakly absorbing or low-contrast structures and plays an important role in materials, biological, and energy research. Conventional X-ray holography and phase-retrieval techniques typically require multiple intensity measurements acquired at different propagation distances to recover phase information, increasing acquisition time, radiation dose, and experimental complexity. In this work, we present an AI-driven, physics-informed approach for phase retrieval using only a single X-ray intensity measurement. The method adapted a generative neural network as an inverse reconstruction engine, with physical models of X-ray wave propagation embedded directly into the optimization process. This allows phase and absorption information to be recovered from a single hologram without relying on paired, unpaired, or simulated training datasets. By combining physical constraints with self-supervised AI reconstruction, the approach achieves stable and quantitative results across a wide range of imaging conditions. The results demonstrate how physics-informed AI can reduce experimental requirements and enable data-efficient, automated phase retrieval for next-generation X-ray imaging workflows.

Biography: Xiaogang Yang is a computational scientist in the Data Analysis & Workflow Integration group at NSLS-II, focusing on AI development for X-ray imaging, data analysis, and automated workflows. He earned his PhD from Delft University of Technology, completed his postdoctoral research at Argonne National Laboratory, and previously held a tenured position at PETRA III (DESY).

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.

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.