Seminar Series

Our seminar series features talks from innovators from academia, industry, and the Lab. These talks provide a forum for thought leaders to share their work, discuss trends. and stimulate collaboration. These monthly seminars are held onsite and virtually. Recordings are posted to a YouTube playlist.

Using Data Science to Advance the Impact of Vascular Digital Twins in Medicine

Amanda Randles
Amanda Randles | Assistant Professor | Duke University

The recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next-generation drug delivery systems. Building a detailed, realistic model of human blood flow, however, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and the behavior of suspended bodies such as red blood cells. Combining physics-based modeling with data science approaches is critical to addressing open questions in personalized medicine. In this talk, I will discuss how we’re building and using high-resolution digital twins of patients’ vascular anatomy to inform the treatment of a range of human diseases. I will present the data challenges we run into and identify key areas where data science can play a role in advancing the work.

Dr. Amanda Randles is the Alfred Winborne Mordecai and Victoria Stover Mordecai Assistant Professor of Biomedical Sciences and Biomedical Engineering at Duke University. Focusing on the intersection of HPC, ML, and personalized modeling, her group is developing new methods to aid in the diagnosis and treatment of a diseases ranges from cardiovascular disease to cancer. Amongst other recognitions, she has received the NIH Pioneer Award, the NSF CAREER Award, and the ACM Grace Hopper Award. She was named to the World Economic Forum Young Scientist List and the MIT Technology Review World’s Top 35 Innovators under the Age of 35 list and is a Fellow of the National Academy of Inventors. Randles received her PhD in Applied Physics from Harvard University as a DOE Computational Graduate Fellow and NSF Fellow.

Calling the Shot: How AI Predicted Fusion Ignition Before It Happened

portraits of Kelli and Luc
Left: Kelli Humbird | Design Physicist | LLNL
Right: Luc Peterson | Associate Program Leader | LLNL

At 1:03am on December 5, 2022, 192 laser beams at the National Ignition Facility focused 2.05 megajoules of energy onto a peppercorn-sized capsule of frozen hydrogen fuel. In less time than it takes light to travel 10 feet, the laser crushed the capsule to smaller than the width of a human hair, vaulting the fuel to temperatures and densities exceeding those found in the sun. Under these extreme conditions, the fuel ignited and produced 3.15 megajoules of energy, making it the first experiment to ever achieve net energy gain from nuclear fusion. Nuclear fusion is the universe’s ultimate power source. It drives our sun and all the stars in the night sky. Harnessing it would mean a future of limitless carbon-free, safe, clean energy. After several decades of research, fusion breakeven at NIF brings humanity one step closer to that dream. Yet, the shot that finally ushered in the Fusion Age was not actually that surprising. A few hours before the experiment, our physics team used an artificial intelligence model to predict the outcome of the experiment. Our model, which blends supercomputer simulations with experimental data, indicated that ignition was the most likely outcome for this shot. As such, hopes were high that something big was about to occur. In this talk, we discuss the breakthrough experiment, nuclear fusion, and how we used machine learning to call the shot heard around the world.

Dr. Kelli Humbird’s work focuses on machine learning (ML) discovery and design for inertial confinement fusion and integrated hohlraum design. During her time at the Lab, she has worked in stockpile certification, technical nuclear forensics, ML accelerators for multiphysics codes, and ML analysis for the spread of COVID-19 during the first year of the pandemic. The common thread throughout much of her work is the application of ML to scientific problems with sparse data.

Dr. Jayson “Luc” Peterson is the Associate Program Leader for Data Science within LLNL’s Space Science and Security Program, where he is responsible for the leadership and development of a broad portfolio of projects at the intersection of data science and outer space. He also leads the ICECap and Driving Design with Cognitive Simulation projects, which aim to bring ML-enhanced digital design to exascale supercomputers.