Ana Kupresanin Tapped to Lead Berkeley Lab's Scientific Data Division
September 13, 2023
By Carol Pott
Ana Kupresanin, associate director of the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL), has been selected to serve as the next division director for Lawrence Berkeley National Laboratory’s (Berkeley Lab) Scientific Data Division (SciData) in the Computing Sciences Area (CSA). Her appointment will be effective October 9, 2023. The announcement follows an extensive international search.
“We are so pleased that Kupresanin is joining Berkeley Lab to lead the SciData Division,” said lab director Mike Witherell. “Her experience co-leading a world-class R&D organization in the national lab system, and her excellent leadership across the Department of Energy (DOE) research complex makes her an ideal candidate to further advance our ability to harness big data for the future.”
Kupresanin will be leading the SciData Division, which is dedicated to catalyzing groundbreaking discoveries through cutting-edge data science methodologies, technologies, and infrastructures in collaboration with science domain experts. Its researchers and engineers harness the power of advanced scientific instruments, allowing scientists to explore the microscopic and cosmic realms while dealing with unprecedented volumes of data. Machine learning (ML) integrated with high performance computing (HPC) is employed to automate model derivation, feature identification, and experiment control. Additionally, SciData builds data infrastructure, from data transformation pipelines to data management tools, emphasizing FAIR data principles and cybersecurity. Efforts also encompass security, software engineering, and sustainability, ensuring reliability and usability across scientific domains. SciData emphasizes fostering partnerships with domain scientists spanning various fields and addressing data-analysis challenges for present and future experimental, observational, and simulation data.
Kupresanin’s work at LLNL focused on collaborating with scientists and engineers to analyze data and develop statistical methods for problems in diverse fields such as climate modeling, nuclear forensics, stockpile stewardship, and uncertainty quantification.
“I am very pleased that Kupresanin is joining us and taking on the leadership of SciData,” said Jonathan Carter, associate lab director for the CSA. “Ana has proven herself to be a collaborative and effective leader and has the skills to grow the reach and influence of SciData. As a member of the CSA management team, I’m confident that she will bring her deep expertise to steer future programs such as AI for science and R&D related to the Integrated Research Infrastructure.”
Kupresanin’s primary research is dedicated to crafting advanced statistical and ML models that seamlessly integrate real-world variability and probabilistic behavior. Her aim is to rigorously quantify uncertainty within engineering and physics models, forging a path towards more accurate and reliable insights in these fields. She received her Ph.D. in Statistics from Arizona State University, M.S. in Applied Statistics from the University of Pittsburgh, and B.S. in Mathematics from the University of Zagreb, Croatia. During graduate school, she did an applied statistics internship at Los Alamos National Laboratory and found she enjoyed the interdisciplinary nature of the work.
“I liked looking at the data that people were collecting in engineering and physics disciplines; it is an interesting combination of observational data, data from experiments, and a lot of data from modeling and simulation efforts,” said Kupresanin. “You have to analyze all these data together to get a clear picture and a better understanding of the physics phenomena that you want to study.”
After completing her M.S., Kupresanin taught statistics and mathematics at Arizona State University. When she finished her Ph.D. in 2009, she took an opportunity to return to lab life, joining LLNL as a statistician.
“I really like the lab environment,” said Kupresanin. “Given Livermore’s mission, I started to work with physicists on questions related to stockpile stewardship that I could help answer with statistics and also using statistical methods to extract as much information as we could from very costly experiments. Coming from academia, Livermore was a playground for a statistician because you get to learn about different scientific disciplines by looking at the data and talking to experts in their fields.”
For her next career step, Kupresanin is focused on the future of data and the foundational role that it will play in scientific research in the years to come. “The SciData division is a dream organization for a statistician because you need to integrate science and technology through data, math, statistics, and computing,” said Kupresanin. “SciData advances data science methods from collection through description to interpretation. What the division does is prerequisite for ML and AI. It includes the foundational building block of data infrastructure and management of the whole data lifecycle. This is critical and enables the reproducibility and credibility of the results that come from scientists using ML, AI, and data science methods.”
The team science model and the breadth of perspectives at Berkeley Lab also attracted Kupresanin. “In a scientific research organization, great advances are made through teamwork and collaboration, and diversity of perspectives and ideas is necessary, even foundational, to breakthrough science,” said Kupresanin. “Creating a diverse workforce is just the first step. You want to cultivate a rich, diverse community where everyone is treated fairly and respectfully. Equity, equal opportunity, and access go hand-in-hand with that. All of these concepts have to be lived and modeled for others to ensure that they are not just ideas but are deeply embedded into the fabric of all we do.”
Kupresanin is currently a council member of the Data Science Institute at LLNL and an ambassador/mentor for Women in Data Science. She is a program chair-elect for Statistics in Defense and National Security section of the American Statistical Association (ASA). She also served as chair of the ASA UQ Interest Group, technical program committee member for DOE’s AI for Science, Security, and Energy workshops, and co-chair of DOE’s Advancing Fusion with Machine Learning workshop. She has received two NNSA Defense Programs Awards of Excellence.
About Computing Sciences at Berkeley Lab
High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and large-scale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab’s Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines.