Computational mathematician Julian Andrej joined LLNL’s Center for Applied Scientific Computing (CASC) in 2019 as a postdoctoral researcher after completing his electrical engineering degree at Kiel University in Germany. “I took an unusual road to get here,” says Julian, who at the time was both using and contributing to MFEM and SUNDIALS open-source software through the GitHub hosting platform.
“I had this revelation,” he continues. “I made the connection that the software applications I was working on were both developed at the Laboratory, and I thought it would be the right place for me to work, but being a foreign national, I wasn’t sure I was well-suited for the position.” In the end, he took a chance — one that has set him on a career path where he can apply his expertise to real-world scientific problems.
Although Julian’s degree is in electrical engineering, his emphasis was on the programming and mathematics sides of the discipline. At the Laboratory, he splits his time between work on MFEM and a related project to develop codes for next-generation, high performance computing hardware.
For Julian, the work he conducts at the Laboratory is fulfilling. “I know that every part of my work is contributing to a mission, and I can see a clear traceable path of its impact,” he says. “I get to solve new problems that nobody else has solved, and that’s why I’m here. It’s challenging work, but seeing its real-world application makes all the effort worth it.”
Stephanie Brink wasn't exposed to high-performance computing (HPC) during her undergraduate program. After a summer internship at Lawrence Livermore and her immersive experience at the annual Supercomputing Conference, Stephanie understood the range of HPC opportunities.
Today, Stephanie is a computer scientist in the Center for Applied Scientific Computing specializing in performance analysis and optimization, power-aware HPC and low-level power management and control. “If we can improve the amount of time it takes for us to do more science, we can make better advancements in our fields and the mission overall,” she says.
Solving complex computing problems is just one thing that keeps Stephanie motivated. “I love having the flexibility to work in different roles. If I want to be a developer, I can do that; if I want to mentor students, I can do that,” she says. Stephanie takes full advantage of those opportunities, mentoring high school girls who are considering STEM careers, serving on the Laboratory’s Asian Pacific American Council scholarship committee and participating in the Supercomputing Conference’s Student Cluster Competition committee.
Stephanie says, “There’s always something new to learn, especially when working with cutting-edge technology in HPC. Being involved in a handful of areas means that no two days are alike.”
Stephanie has a Ph.D. and M.S. in computer science from the University of Oregon and a B.S. in computer science from the University of the Pacific.
Stefanie Guenther appreciates the application-driven aspect of the research at LLNL. “I enjoy seeing my work being used for larger project goals and enabling exciting, innovative technologies,” she says.
Stefanie’s current research is in optimal control and optimization methods that are deployed efficiently on HPC machines. She is focused mainly on optimal quantum control, which aims to reduce errors of logical operations on Livermore’s quantum computing testbed, QuDIT. Stefanie is motivated by the direct impact her work has on this specific Lab application but also more broadly for the quantum science community.
The draw of advanced technology is complemented by the collaborative environment at LLNL, where continuous learning and knowledge exchange thrive. “Working at LLNL exposes you to many different advanced research technologies,” Stefanie says. “It is an excellent place to constantly learn new things and contribute new findings within your area of expertise. People here are similarly eager to learn from you, meaning that your knowledge and viewpoints are valued and appreciated.”
Stefanie joined the Lab as a postdoc in 2019, after a three-month appointment as a visiting researcher during her Ph.D. studies at Rheinisch-Westfälische Technische Hochschule Aachen University, in Germany.
Nisha Mulakken is one of a growing number of “boomerang” employees — she left the Lab to work in industry and returned several years later, drawn back by the Lab’s strong mission focus and real-world applications that improve society and human life.
Today, Nisha is both a line manager and a project leader supporting several biosecurity projects, mostly focused on designing assays for pathogen detection, a skill she learned entirely on the job. In a recent project, Nisha designed a system, Meta2DB, for showcasing curated metagenomic data and standardized metadata from over 10,000 samples processed through LLNL’s metagenomic classification pipeline. The system, once released to the public, will enable researchers to compare microbiome features and their impact on multiple human diseases across diverse study designs in a way that is near impossible to do currently. Unique analysis tools, including sophisticated machine learning models developed at LLNL, will also be available for download.
“LLNL has provided me with opportunities to get involved with exciting new technical areas and to grow my skills in a nurturing, collaborative environment,” Nisha says. “Every year, I take on projects that are different from things I’ve done before. I can work on the same team for a long time without ever getting bored.”
Dr. Jayson “Luc” Peterson is the associate program leader for data science, within the Space Science and Security Program at Lawrence Livermore National Laboratory, where he is responsible for the leadership and development of LLNL’s broad portfolio of projects at the intersection of data science and outer space.
He joined LLNL in 2011 as a member of the inertial confinement fusion program at the National Ignition Facility and was part of the team that first achieved ignition and net energy gain from nuclear fusion in December 2022. At LLNL he has worked across several programs, from inertial confinement fusion and stockpile stewardship to COVID-19 pandemic response and space science and security.
His technical contributions in these areas span hydrodynamics, radiation transport, modeling and simulation (mod/sim), experimental design, digital engineering, uncertainty quantification, verification and validation, data analytics, high-frequency and high-performance computing and machine learning. He currently leads the ICECap project, which aims to bring machine learning-enhanced digital design to exascale supercomputers.
Luc holds doctorate and master’s degrees in Astrophysical Sciences (Plasma Physics) from Princeton University and a bachelor’s degree in physics and science, technology and society from Vassar College.
Luc’s hobbies include spending time camping, hiking, Airstreaming, gaming, ferrying around his children, long distance running, cooking, playing softball and volunteering with his church and participating in a book club he began over 15 years ago. Occasionally, he finds time to sleep.
Rob Rieben was hooked after visiting the Laboratory during a summer institute while a physics undergrad at the University of California, Riverside. He joined Lawrence Livermore as a graduate research fellow in 2000 and progressed to a postdoctoral research position after earning his Ph.D. “You will never be bored or run out of new and exciting things to learn and apply when working here. This is truly a place where team-based ‘big science’ happens every day,” he says.
A computational physicist by training, Rob now leads a large multi-physics code project in the Weapons Simulation and Computing program. He recently won a Gold Award from the Strategic Deterrence Principal Directorate for sustained contributions in advancing next-generation simulation and computing.
Between applying state-of-the-art computation and numerical methods, having access to some of the world’s most powerful high-performance computing platforms and collaborating with some of the best minds in the field, Rob is excited to face the next challenges his work may bring. “What keeps me here is the never-ending set of interesting problems to solve and the constant learning and collaborative team-based scientific work required to solve them,” he says.
Rob holds a Ph.D. in engineering/applied science from the University of California, Davis and B.S. in physics from the University of California, Riverside.
AI Innovation Incubator
A key Lawrence Livermore initiative seeks to advance AI and ML for applied science at scale. With these tools and our data-rich environment, we’re advancing AI-driven simulations in a range of application areas to: design new molecules for pandemic response and energetic materials, integrate complex multimodal data to protect the nation's critical infrastructure and increase the power of predictive models in climate studies and inertial confinement fusion, among others.
El Capitan
When deployed in 2024, El Capitan will be NNSA’s first exascale supercomputer, with the processing power of more than 2 exaflops, or 2 quintillion (1018) calculations per second. El Capitan will usher in a new era of predictive and experimental capability, producing high-fidelity 3D simulations beyond what’s possible now with the other supercomputers. With El Capitan, LLNL will apply this enhanced realm of simulations to several critical mission spaces including stockpile stewardship, weapon modernization and more.
Multiphysics Simulation Codes
We develop and utilize a suite of multiphysics simulation codes that address nuclear security and inertial confinement fusion, among other missions, and support defense projects such as the design of modern conventional weapons and protective equipment for soldiers. These simulation codes employ a range of physics regimes from solids to plasmas under extreme conditions and must be able to run on multiple advanced HPC architectures with efficient solvers and numerical algorithms.
CASC
Center for Applied Scientific Computing
CASC enables collaboration with the broader computer, computational and data science communities in academia, industry and government.
DSI
Data Science Institute
The Data Science Institute (DSI) acts as a hub for all data science activity at the Laboratory such as areas of artificial intelligence, big-data analytics, computer vision, machine learning, predictive modeling, statistical inference, uncertainty quantification and more.
HPCIC
High Performance Computing Innovation Center
HPCIC facilitates LLNL’s external engagements in advanced computing with academic, government and industry partners who can benefit from using Livermore’s open-source software and expertise to solve their challenges.
LCC
Livermore Computing Complex
Livermore Computing Complex (LCC) is one of the world’s dominant high-performance computing (HPC) centers. LCC is key to the Laboratory’s stockpile stewardship mission, helping to ensure the safety, reliability and effectiveness of the nation’s nuclear weapons.
Quantum Design and Integration Testbed (QuDIT)
The quantum testbed offers a state-of-the-art research environment for developing quantum processors and algorithms. Working collaboratively in the testbed’s codesign environment, quantum computing experts work side-by-side with domain experts to explore mission-relevant applications of quantum computing. In addition to this focus on quantum software development, researchers use the testbed’s stable system to study the full quantum stack, including materials, devices, classical hardware and the quantum-to-classical interface. The testbed also plays a key role in efforts to develop staff expertise in quantum information systems.
Skyfall
Skyfall is a cyber–physical hardware-in-the-loop test bed that connects real equipment with high-performance computers (HPC) to enable co-simulation of complex systems at scale to analyze the impacts of natural hazards and cyber-attacks and to increase resiliency.
