Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.
Dr. Yongjun Zhang
Assistant Professor of Sociology, Departments of Sociology and AAAS
Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.
Location: SBS Level 4- Sociology Reading Room
Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, the giga-pixel size of these images presents profound technical challenges, creating two intertwined bottlenecks: computational inefficiency and label inefficiency. The immense data scale makes standard end-to-end (E2E) training of deep neural networks infeasible due to prohibitive GPU memory requirements, while the reliance on expert pathologists for annotations makes obtaining high-quality labeled data a tedious and expensive process. This proposal confronts these dual challenges by developing a series of novel model architectures, training paradigms, and self-supervised learning methods designed to create a more efficient and effective framework for WSI analysis.
To improve computational efficiency, this proposal first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework that reduces the number of trainable parameters, memory consumption, and training time by fine-tuning only few prompts to guide large pre-trained models. Third, this work advances the efficient architecture on WSIs by developing novel State-Space Models (SSMs). It proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy inherent in 1D models. Fourth, to address the inefficiency of multi-directional scans in Mamba models, including 2DMamba, it presents Locally Bi-directional Mamba (LBMamba), which introduces a novel, hardware-aware local backward scan that integrates bi-directional scan into a single forward pass, significantly improving throughput performance trade-off. Lastly, it proposes an extension to the LBMamba, warp-level Bi-directional Mamba (WLBMamba) that extends the thread-level bidirectional scan to warp-level bidirectional scan that further improves the throughput performance trade-off.
To improve label efficiency, this proposal proposes a Precise Location-based Matching strategy for self-supervised dense contrastive learning. By allowing a local patch in one augmented view to match multiple overlapping patches in another, creates a more accurate correspondence, leading to superior feature representations for dense prediction tasks like segmentation and detection.
In summary, this proposal presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.
Speaker: Jingwei Zhang
Location: Old Computer Science Room 2114
Zoom: https://stonybrook.zoom.
Meeting ID: 951 8790 3649 | Passcode: 488916
- Describe how bias enters AI systems across the development pipeline, using aesthetic facialevaluation as a case study, and identify mitigation strategies applicable at each stage.
- Apply the PROBAST+AI risk-of-bias framework to critically appraise the methodologicalquality of a published prediction model using regression or machine learning methods.
- Compare the discriminative performance of multiple modeling architectures against anexisting clinical risk calculator on a large benchmarking dataset.
- Recognize the complementary roles of clinical expertise and informatics methodology indefining clinically meaningful AI project objectives, predictors, and evaluation criteria.
Speaker: Dr. Anisha R. Kumar, MD Clinical Assistant Professor, Surgery and Biomedical Informatics, Stony Brook University School of Medicine
Location: MART Building, Room 7M-0602 (7th Floor)
Remote Access: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09Meeting ID: 95617197636 | Passcode: 924293
Abstract: Materials used in extreme environments, such as high temperatures, irradiation, and stress, often fail due to rapid defect generation and microstructural evolution, and traditional approaches cannot explore the vast design space needed for next-generation alloys. I will present a machine learning framework powered by massive computing that links individual atomic motion to microstructural evolution. Neural network kinetics models trained on first-principles data map vacancy barrier spectra and capture correlated diffusion in multicomponent alloys, revealing design strategies to suppress radiation damage. At larger scales, simulations uncover dislocation patterning and distinguish between confined and extended slip bands, offering new insight into collective dislocation motion and deformation instabilities. By integrating AI-driven modeling, large-scale computing, and experimental validation, my research goal is to accelerate the discovery of damage-tolerant materials and advance fundamental understanding of defect physics in extreme environments.
Speaker Bio: Penghui Cao is an Associate Professor in Mechanical and Aerospace Engineering at the University of California, Irvine, with a joint appointment in Materials Science and Engineering. He received his PhD in mechanical engineering from Boston University and subsequently worked as a Postdoctoral Associate in the Department of Nuclear Science and Engineering at the Massachusetts Institute of Technology from 2014 to 2018. Dr. Cao's research focuses on understanding the fundamental mechanisms that govern radiation responses and microstructure evolution in materials, and on developing advanced alloys for high-performance nuclear energy systems. His lab advances computational and modeling algorithms, integrates advanced manufacturing techniques to tailor microstructures, and leverages state-of-the-art electron microscopy to characterize and assess underlying mechanisms. He is the recipient of the DOE Early Career Research Program Award and the UCI Samueli School's Mid-Career Award for Faculty Excellence in Research.
Location: Institute for Advanced Computational Science, Seminar Room
*This seminar will be held in-person and online. Zoom link below*
Join Zoom Meeting: https://stonybrook.zoom.us/j/96410717491?pwd=3WGMwbLYNMSbI2IF160VXkvv2JmCQ1.1
Meeting ID: 964 1071 7491
Passcode: 399333
Bio: Nathan Urban is the group leader of the Optimal Experimental Design & Uncertainty Quantification group in the Applied Mathematics Department at Brookhaven National Laboratory's Computing & Data Sciences directorate (CDS). He holds a Ph.D. in theoretical condensed matter physics from Penn State, and has previously held research positions at Los Alamos National Laboratory, Princeton, and Penn State. His research interests include Bayesian inference and spatiotemporal statistics, probabilistic prediction and forecasting, multi-model / model-form / model structural uncertainty quantification, reduced order modeling, scientific machine learning and hybrid physical-data driven modeling, in-situ/streaming data analysis at scale, information fusion, decision making under uncertainty and optimal experimental design, and integrated multiscale computational frameworks for decision support.
Location: IACS Seminar Room
Lunch will be provided