Valerio Pascucci
Director, Center for Extreme Data Management Analysis and Visualization (CEDMAV)
Actions
Valerio Pascucci is the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV) of the University of Utah. Valerio is also a Faculty member of the Scientific Computing and Imaging Institute, a Professor of the School of Computing, University of Utah, and a DOE Laboratory Fellow of the Pacific Northwest National Laboratory.
From Beamtime to Insight: An Open NSDF Pattern for Real-Time Adaptive Deformation Mapping
Autonomous “closed-loop” experiments are becoming essential at large facilities, but they require reusable software patterns to move data across trusted boundaries, analyze results in real time, and safely steer instruments.
ORNL and the National Science Data Fabric (under the leadership of the University of Tennessee and the University of Utah) are developing and deploying an adaptive strain-mapping workflow for X-ray scattering on wire-arc additive-manufactured parts at the CHESS Structural Materials Beamline. Experimental data stream through the National Science Data Fabric (NSDF) to computing resources, where INTERSECT@ORNL’s Distributed Active Learning (DIAL) builds a surrogate model and recommends the next measurement locations; the beamline executes them, and the loop repeats.
This talk presents the end-to-end architecture, integration points, and potential “gotchas” (latency, metadata, provenance, and operator controls) so others can replicate the pattern for autonomous experiments and AI-ready pipelines across domains.
FASTMAP: A Petascale HPC Cyberinfrastructure for Real-Time Terapixel Inference in Surgical Oncology
The persistent clinical problem of positive tumor margins in cancer surgery presents a computational grand challenge: to process up to 2 TB of microscopic imaging data within 10 minutes in the operating room, providing the surgeon with actionable information in real time.
We address this challenge with FASMP, a human-centered high-performance computing cyberinfrastructure designed to train ML models on datasets of hundreds of terrabytes of medical imaging data. We address the extreme data requirements with a multiscale, cache-oblivious data model that mitigates I/O bottlenecks, a transformer architecture that drastically reduces trainable parameters, and the integration of Topological Data Analysis to ensure robustness.
User-optimized, task-specific labeling tools enable the creation of effective annotations quickly, improving training and closing the validation loop with medical professionals.
Ultimately, FASTMAP serves as the computational engine for an imaging platform, enabling real-time, automated cancer detection in the operating room. This work fundamentally transforms surgical outcomes for cancer patients by providing immediate, data-driven feedback during the procedure.
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top