AI Seminar: Computational Pathology: Deep Learning, Classification and
Predicting the Future  - Joel Saltz

Abstract:  Pathologists have been looking at tissue through microscopes since the 1800s.  During each pathologist's career,  he or she views slides having  roughly 1,000,000,000,000 cells. Deep learning methods are rapidly being developed to assimilate the huge amount of information walked inside of tissue images and to use this information to predict outcomes and responses to treatments.

Stony Brook is a leader in this type of multi-disciplinary work. I will provide an overview of Stony Brook computational Pathology efforts and articulate how these have the potential to create biomedical advances as well as to drive development of new computer science. 


Bio: Dr. Joel Saltz is a leader in research on advanced information technologies for large scale data science and biomedical/scientific research. He has developed innovative pathology informatics methods, including: the first published whole slide virtual microscope system; pioneering pathology computer-aided diagnosis techniques; and methods for decomposing pathology images into features and linking those features to cancer omics, response to treatment and outcome. He has broken new ground in big data through development of the filter-stream based DataCutter system, the map-reduce style Active Data Repository and the inspector-executor runtime compiler framework. He has also been an active contributor in clinical informatics, having developed
predictive models for hospital readmissions, point of care laboratory testing quality assurance systems, decision support systems for electrophoresis interpretation and graphical user interfaces to support clinical data warehouse queries. Dr. Saltz has been a pioneer in establishing the field of biomedical informatics; he founded and built two highly successful departments of biomedical informatics, one at Ohio State University and one at Emory University. In 2013, he came to Stony Brook as Vice President for Clinical Informatics and Founding Department Chair of Biomedical Informatics - to create a living laboratory for biomedical informatics and to create a third unique biomedical informatics department dually housed in the School of Medicine and the College of Engineering. Dr. Saltz is trained both as a computer scientist and as a physician through the MSTP program at Duke University. He has deep experience in computer science, having served on the computer science faculties at Yale University and the University of Maryland. He completed his residency in clinical
pathology at Johns Hopkins University and he is a practicing, board-certified clinical pathologist. 
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.

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

Abstract: At XTX Markets, we view algorithmic trading as one of the most compelling real-world frontiers for deep learning and foundation models. Every day, our systems generate forecasts for tens of thousands of financial instruments and execute over $300B in global trading volume: fully automated, with no discretionary human intervention. This domain combines massive data scale with high noise, adversarial dynamics, and frequent regime shifts, making it both scientifically challenging and commercially impactful. For machine learning researchers, it serves as a rigorous proving ground where advances in time-series modeling, large-scale optimization, representation learning, and foundation models can translate directly into measurable real-world outcomes. This talk will provide a high-level overview of our research agenda, infrastructure, and key open challenges at the intersection of large-scale AI and quantitative finance.

Speaker: Dr. Zhangyang Atlas Wang is the Research Director at XTX Markets, one of the world's leading high-frequency trading firms. He founded and leads the firm's AI Lab in New York City, focused on developing large-scale foundation models for financial time series and market data, powered by XTX's proprietary AI infrastructure. He is currently on leave from his position as the Temple Foundation Endowed Associate Professor at The University of Texas at Austin. His academic research has received numerous awards, and he has mentored a broad network of Ph.D. students and postdoctoral researchers. Many of his alumni now hold tenure-track faculty positions (eight to date) or senior research roles in industry (nineteen and counting). For more information about his group and alumni, please visit: https://www.vita-group.space/team.

Location: NCS 120

Refreshments will be served after the seminar in the first-floor atrium.



The 39th Annual AAAI Conference on Artificial Intelligence will be held from February 25 to March 4, 2025 in Philadelphia, Pennsylvania, USA. More information can be found here.

The next AI Institute seminar speaker will be Chao Chen of Biomedical Informatics, on Monday November 29 at noon via zoom:

https://stonybrook.zoom.us/j/96233844681?pwd=aVVsUnIzMWJDMHRqVXcrQU5HMjFVQT09

He will be talking on the Detection of Trojan Attacks to Deep Neural Networks - A Topological Perspective, with his abstract and bio below.


Abstract: Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, i.e., samples with special trigger injected and labels altered. To identify a Trojaned model at deployment is challenging, due to limited access to the training data. We propose to identify Trojaned neural networks using methods from topological data analysis. In particular, we propose to (1) inspect high-order topological features of the neuron interactions and (2) reverse engineer the injected triggers using a topological loss. These approaches take different angles and reveal insights into the behavior of neural networks when their strong memorialization power is exploited maliciously. The work has been accepted to NeurIPS'21. I will also briefly mention other research directions from my group, including incorporating topological information into deep image analysis, topology-inspired graph neural networks, and robust training of neural networks with label noise. These works have been published in ICLR, ICML, NeurIPS, ECCV, ICCV and AAAI in recent years.
Bio: Dr. Chao Chen is an assistant professor of Biomedical Informatics at Stony Brook University. His research interests span topological data analysis (TDA), machine learning and biomedical image analysis. He develops principled learning methods inspired by the theory from TDA, such as persistent homology and discrete Morse theory. These methods address problems in biomedical image analysis, robust machine learning, and graph neural networks from a unique topological view. His research results have been published in major machine learning, computer vision, and medical image analysis conferences. He is serving as an area chair for MICCAI, AAAI, CVPR and NeurIPS.
Abstract: AI has achieved remarkable advancements in image recognition and natural language processing. However, its applications in Earth and environmental sciences are still emerging. Unprecedented data from satellites, sensors, and in-situ measurements oIers new opportunities to improve physics-based models and forecasts of environmental systems with AI and to gain deeper insights into these phenomena. Extreme systems, such as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, we will explore some of these challenges and showcase AI architectures designed to address them. We will use specific examples of forecasting dust storms, precipitation extremes, flash floods, and drought events in the Middle East. Finally, we will discuss a different AI approach for studying sinkhole formation in the Dead Sea.

Speaker: Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Israel


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ID: 98731258879
Passcode: cJjGQJqP
University Libraries Present: AI as Author? New Considerations When Evaluating Sources.
In this workshop, librarian Christine Fena will review some ways AI is being integrated into published work within the worlds of news and scholarly publication, and discuss how this might impact how to evaluate and understand sources during the research process.
10/2 12:30-1:30 pm on Zoom.
Register via link: https://stonybrook.campuslabs.com/engage/event/10460202

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.

Uncertainty-Aware Adaptation of LLMs for Protein-Protein Interaction Analysis

Abstract: Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature; yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence- calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research.

Biography: Sanket is a research staff member in the Applied Mathematics department within the Computing and Data Sciences Directorate at Brookhaven National Laboratory. Previously, he was the Amalie Emmy Noether Postdoctoral Fellow in the same department. He earned his Ph.D. in Statistics from Michigan State University.. Sanket's research interests span Bayesian statistics, uncertainty quantification (UQ), deep learning, Markov Chain Monte Carlo (MCMC), variational inference, and sparsity methods. He also focuses on dimensionality reduction, surrogate modeling, hybrid physical-data driven models, and active learning, with applications across climate science, materials science, and life sciences.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605691898?pwd=xC7GebG7Kvzxa4AjPSIxJw7e9IZtoY.1

Meeting ID: 160 569 1898
Passcode: 303888