DSI Council

Brian joined LLNL in 2014 as a postdoctoral researcher and is the principal investigator of an LDRD Strategic Initiative on digital twins for virtual inspection of advance manufacturing as well as technical lead on several advanced analytics and additive manufacturing projects. Giera’s research interests include additive manufacturing, digital twins, machine learning, in situ process monitoring, computational materials science, molecular dynamics, electrophoretic deposition and hyperspectral imaging—all with a focus on developing and applying physics-based and machine learning models to a variety of advanced manufacturing systems. Giera has a passion for LLNL’s diversity, equity, and inclusion efforts and works closely with the Strategic Diversity group to advocate for neuro-diverse and hearing or visually impaired individuals. He holds a B.S. in Chemical Engineering from Purdue University and a Ph.D. in Chemical Engineering from the University of California, Santa Barbara.

Cindy leads the Intelligent Detection, Exploitation, and Analysis Group in LLNL’s Global Security Computing Applications Division, where her research interests include using machine learning for automatic target recognition and detection in unconventional types of imagery. Cindy works collaboratively to develop, implement, and promote LLNL’s strategic vision in data science, both internally and externally.

Peer-Timo holds a shared appointment at the Center for Applied Scientific Computing and the University of Utah. His research interests include large-scale machine learning, data analysis, medical image analysis, topology, and visualization.

Barry is the PI for several machine learning research projects devoted to the development of new neural network learning algorithms that address recurring challenges in scientific and security applications such as multimodal data and the scarcity of labeled data.

Eric is the Director of the Center for Engineered Materials and Manufacturing at Lawrence Livermore National Laboratory, where he directs research activities and maps strategic directions in the areas of advanced materials and manufacturing. At LLNL, Eric leads teams that invent novel materials and manufacturing technologies, with focus on creating designer architectures for chemical, mechanical, thermal, and functional properties for applications in the fields of defense, climate, transportation, energy, aerospace, human health, and others. Eric is a recipient of the Presidential Early Career Award in Science and Engineering (2016) and he leads a team that was honored with the Department of Energy Secretary’s Achievement Award (2019). Eric has co-authored over 90 peer-reviewed technical publications that have collectively received over 16,000 total citations. He has also been awarded over 50 U.S. patents. Eric has a Ph.D. in Materials Science and Engineering from the University of Illinois at Urbana-Champaign (2009) and dual B.S. degrees in Chemistry and Mathematics from St. Norbert College (2003).

Ryan’s research is on networked autonomous systems, including algorithms for decentralized detection/estimation and cooperative behaviors for multi-agent systems. He leads multiple projects in emerging technical areas such as collaborative autonomy and adversarial AI.

Bhavya is a Staff Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His primary area of expertise is in developing “Safe and Trustworthy AI” for applications in scientific research and national security. A Senior Member of IEEE, Bhavya has received several prestigious awards, including the All University Doctoral Prize from Syracuse University (2017), the Deputy Director for S&T Excellence in Publication Award from LLNL (2019 and 2024), and multiple best paper awards. Additionally, he was honored with the LLNL Early and Mid-Career Recognition Award in 2024. Currently, he is leading projects aimed at improving the safety of large language models, with the goal of building trust in AI technologies across the DOE/NNSA mission space.

Goran works in LLNL’s Computational Engineering Division. and served as director of the Data Science Summer Institute from 2018 to 2022.

Nisha’s research lies at the confluence of biology, computer science, and statistics. Her work in LLNL’s Bioinformatics Group includes enhancing the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2, as well as analyzing mutations in SARS-CoV-2 proteins to support future discovery of therapeutic compounds.

Michael leads a team within LLNL's Physics Division in science exploitation of the Large Synoptic Survey Telescope (LSST) for studying dark energy, dark matter, and the solar system. He serves as PI on internal and external projects in the areas of data analytics, computation, and sensor development.

Michael served as DSI Director from 2018 to 2023. He is the PI for several internal and external projects related to overhead imagery, machine learning, data compression, and system development. He also serves as Associate Program Leader for the Collection Systems Innovation program in Global Security.

Daniel is the PI on projects that combine simulation, real-world experiments, data-driven methods, and decision science to accelerate scientific discovery and provide novel solutions. His expertise includes agent-based modeling, stochastic optimization, deep reinforcement learning, and active learning.

Ana, formerly of LLNL, is currently division director for Lawrence Berkeley National Laboratory’s (Berkeley Lab) Scientific Data Division (SciData) in the Computing Sciences Area (CSA). 

Dan leads the Machine Intelligence Group in LLNL’s Center for Applied Scientific Computing. His research focuses on Bayesian inference, causal inference, experimental design, statistical computing, and probabilistic programming.