Over the past decade, Artificial Intelligence (AI) has made stunning advances, from mastering language to solving the structure of proteins. These breakthroughs arise from more than forty years of work in neural networks, where ideas from neuroscience have inspired solutions in AI. In this lecture, Anthony Zador, MD, PhD, will explore how reverse engineering the brain's computations has driven progress in both fields, and how this back-and-forth between neuroscience and AI is set to grow even stronger -- with brain-inspired designs driving new AI advances while AI tools transform our understanding of how the brain works.

Speaker:
Dr. Zador works at the intersection of neuroscience and artificial intelligence. He is the Alle Davis Harris Professor of Biology at Cold Spring Harbor Laboratory, where he served as Chair of Neuroscience. He was named one of Foreign Policy's 100 Leading Global Thinkers and is a recipient of the Brain Research Foundation Fellowship, the Gill Symposium Transformative Investigator Award, and the Allen Distinguished Investigator Award.

Watch online at stonybrook.edu/live

The Vedanta Forum is devoted to one of humanity's oldest and most profound pursuits -- thinking. Thinking about who we truly are: the one that remains constant through childhood and old age, through waking, dream, and deep sleep. Thinking about the source and cause of creation, and its relationship to what inheres in us.

Across history, such thinking, both meditative and scientific, has been aimed at these questions. The ancient Upanishads proclaimed, Tat Tvam Asi -- Thou Art That -- revealing the non-dual identity of the individual and the ultimate reality. Centuries later, modern scientists such as Schrödinger and Bohr echoed similar intuitions about the unity of existence.

Over time, many philosophical approaches, traditions, and interpretive schools have arisen from such inquiry, each offering unique perspectives. The Forum will:

  • Focus on universal approaches and traditions and examine their teachings,

  • Foster comparative studies, and

  • Explore the practical benefits to society from such thinking,

through scholarly studies, dialogue, and debate also promoting accessibility to all qualified seekers. Additionally, the Forum will explore how these reflections can enrich life, education, and even technology.

Location: NCS 120 (New Computer Science), Engineering Dr, Stony Brook, NY 11794.

The program is available at: https://www.vedantaforum.org/events/program

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.

#1 How to train your Scientific Chatbot by Alexandr Prozorov, Post-Doctoral Research Associate


Abstract: RHIC is closing its 25-year run with ~1 EB of data and decades of hard-won know-how that risk drifting into obscurity. The RHIC Data & Analysis Preservation Plan (DAPP) pilots an AI assistant that lets physicists talk to RHIC in natural language--searching internal notes, code, workflows, and docs, and pointing to runnable, containerized analyses. Built on Retrieval-Augmented Generation(RAG) with a Model Context Protocol orchestration layer, the system indexes heterogeneous, experiment-specific content and enforces role-aware access
for public vs. collaboration-restricted materials. Takeaway: domain-adapted AI can turn a legacy exabyte into reproducible answers, training assets, and new discovery paths.

Biography: Alexandr Prozorov is a postdoc from Czech Technical University in Prague working in STAR experiment. Fascinated by AI

#2 Quantum AI: Atoms, Cavities and Learning by Raman Kumar, Post-Doctoral Research Associate, Instrumentation Department

Abstract: The Instrumentation Department (IO) in the Discovery Technologies directorate at BNL is engaged in exploring various aspects of quantum systems research. One of the main goals of our group's effort is in developing neutral atom-cavity array platforms for remote entanglement generation and distributed quantum processing. This platform promises to herald truly scalable quantum computing systems and open new paradigms for networking and sensing. In this talk, I will explain our group's research and the role AI is playing in unlocking new insights with two examples. The first application of AI is in fabrication process prediction of micro-cavity structures. The second application revolves around role of AI in quantum error detection and correction in modern quantum computing systems.

Biography: Dr. Raman Kumar is a postdoctoral research associate in the IO department at BNL working with Dr./Prof. Sebastian Will (Columbia U.). Kumar obtained his Ph.D. degree in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign. Prior to joining BNL in Nov 2024, Kumar worked as a postdoc at the City College in New York working on topological photonic quantum sensing using NV centers in diamond. Kumar and Will combined have an extremely wide moat and expertise in a variety of different areas which include Ultra cold atoms and molecules, quantum optics, quantum condensed matter, nanofabrication, semiconductor devices and advanced electromagnetics. Their areas of research interest include scalable quantum computing, communications and sensing, all enabled by AI.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting https://bnl.zoomgov.com/j/1607892208?pwd=MSjxN5btSeToZsQMwEQzCCbBo5h58V.1

Meeting ID: 160 789 2208
Passcode: 753871

Jerome Liang, PhD 

Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science 

Co-Director of Research 

Department of Radiology 


Artificial intelligence, machine learning and computer-aided diagnosis in cancer Imaging 

February 11, 2021 

12:00pm - 1:00pm 

Virtual Seminar - Zoom 

https://stonybrook.zoom.us/j/98155629970?pwd=YzRvcnJnTlNTT1E5ak1oZEJvWTZHQT09 

Meeting ID: 981 5562 9970 

Passcode: 950410 

Host: 

Wei Zhao, PhD 

Professor of Radiology and Biomedical Engineering 

Educational Objectives  

Upon completion, participants should be able to:  

(1) Learn different medical image representations of cancer attributes, such as heterogeneity, high tendency to grow, etc.  

(2) Learn how computer (machine) can be trained (or programmed) to recognize the image representations.  

(3) Learn how artificial intelligence can drive the machine learning to maximize the performance of computer-aided diagnosis (CADx).  

Disclosure Statement  

In compliance with the ACCME Standards for Commercial Support, everyone who is in a position to control the content of an educational activity provided by the School of Medicine is expected to disclose to the audience any relevant financial relationships with any commercial interest that relates to the content of his/her presentation.  

 

The speaker, Jerome Liang, PhD, the planners; and the CME provider have no relevant financial relationship with a commercial interest (defined as any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients), that relates to the content that will be discussed in the educational activity.  

 

CONTINUING MEDICAL EDUCATION CREDITS  

The School of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.  

 

The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should only claim credit commensurate with the extent of their participation in the activity.  

 

Should you be logging in Zoom by using your tablet or mobile device, please be sure to add your Full Name and/or Email for CME credit. 

The Antonija Prelec Memorial Committee in collaboration with Stony Brook University Libraries are very excited to bring you the 2019 Prelec Memorial Lecture! This year, we are pleased to announce our speaker is Patricia Flatley Brennan, RN, PhD, Director of the National Library of Medicine.

No registration required. Find more information here.
CSE 600 Seminar Series | Fall 2025


Abstract: Vision-language models that see and describe the world are now part of our daily lives, from internet search and accessibility tools to content generation and automatic moderation. However, as these models grow and become more widely used, their limitations have also become increasingly visible. In particular, it has been shown that these models are unable to reliably perform complex tasks that require abstraction and compositional reasoning. For example, they struggle to decompose an image or text into entities, attributes, and relations, and then reason over new combinations of these elements. As a result, we see generated content full of hallucinations, privacy leaks in images, and different types of biases in the model outputs.In this talk, I will outline a research agenda that aims to build trustworthy vision-language models in the age of generative AI. I will begin with compositional reasoning: how natural language inference can be used to decompose complex instructions and captions into atomic, verifiable statements, improving both evaluation and model behavior on tasks that require multi-step reasoning. I will then discuss how synthetic data and simulated environments can be used to train more reliable models, and how they can also stress-test models beyond standard benchmarks, revealing when models drop attributes, break object relations, or fail under distribution shifts. I will also share recent work on using hallucination correction as a signal to improve video-language alignment, and on privacy-preserving image understanding for blind and low-vision users. I will conclude with possible ways we can systematically probe, debug, and repair these models, turning synthetic perception into something we can trust in real-world deployments.



Speaker: Paola Cascante-Bonilla is a tenure-track Assistant Professor in the Department of Computer Science at Stony Brook University (SUNY). Before that, she was a Postdoctoral Associate at the University of Maryland Institute for Advanced Computer Studies (UMIACS), developing methods and metrics related to trustworthy machine learning. She received her Ph.D. in Computer Science at Rice University in 2024, working on Computer Vision, Natural Language Processing, and Machine Learning.Her research focuses on developing systems that enable compositional reasoning and common-sense inference through vision and language, while tackling issues such as cultural biases, data distribution, explainability, and trustworthy AI. Additionally, Cascante-Bonilla creates simulated environments for embodied agents to learn in a safe, controlled setting, aiming to facilitate effective collaboration and problem-solving for complex tasks by leveraging the implicit knowledge of large-scale pre-trained deep learning models.
Cascante-Bonilla is the recipient of the Ken Kennedy Institute SLB Graduate Fellowship (2022/23), she was selected as a Future Faculty Fellow by Rice's George R. Brown School of Engineering (2023) and as a Rising Star in EECS (2023).
Location: NCS 120
Abstract Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life- like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506 https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09 Bio Professor Henry Liu is a professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at the University of Michigan Transportation Research Institute and the Director for the Center for Connected and Automated Transportation (USDOT Region 5 University Transportation Center). Prof. Liu conducts interdisciplinary research at the interface between civil and mechanical engineering. Specifically, his scholarly interests concern traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles. He has published more than 100 refereed journal papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019) in the prestigious Transportation Research journal. Professor Liu and his work have been widely recognized in public media for promoting smart transportation innovations. He has appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor Liu was invited to testify on national transportation research agenda in front of the US House Subcommittee on Research and Technology. Professor Liu has nurtured a new generation of scholars, and some of his PhD students and postdocs have joined first class universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the managing editor of Journal of Intelligent Transportation Systems.
Abstract: Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.

In this dissertation, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (e.g., audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend the network to capture full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity Gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. Last but not least, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.

Location: NCS 220 or Zoom