Timo Bremer

Portrait of Timo Bremer
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  • Email
    bremer5@llnl.gov
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    (925) 422-7365
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Peer-Timo Bremer is a computer scientist specializing in scientific machine learning, large-scale data analysis, and visualization. He holds a joint appointment at Lawrence Livermore National Laboratory (LLNL)’s Center for Applied Scientific Computing (CASC) and at the University of Utah, where he serves as Associate Director for Research of the Center for Extreme Data Management, Analysis and Visualization (CEDMAV). Bremer earned his Ph.D. in Computer Science from the University of California, Davis in 2004 and received an M.S. (Diploma) and B.S. in Mathematics/Computer Science from Leibniz University Hannover, Germany. He joined LLNL as a research scientist in 2006 after a postdoctoral fellowship at the University of Illinois at Urbana-Champaign. At LLNL, he currently leads the Machine Intelligence Group in CASC and in 2023 was appointed Associate Director of the laboratory’s AI Innovation Incubator, reflecting his leadership role in developing AI and data analytics capabilities.

Bremer’s research focuses on applying advanced machine learning and data analysis techniques to scientific problems in the physical and life sciences. He has extensive expertise in topological data analysis, high-dimensional data exploration, uncertainty quantification, and interpretable machine learning. For example, he has developed new topological methods for understanding complex simulations and has worked on visualization tools for large-scale data (including volume modeling and virtual reality interfaces). Bremer has been a co-Principal Investigator on multiple multidisciplinary projects, such as a Department of Energy initiative to use high-performance computing and AI for traumatic brain injury research (ACTIV-TBI), and he has contributed to lab-wide efforts in cognitive computing, advanced manufacturing, and high-dimensional sampling. He also sits on the council of LLNL’s Data Science Institute, promoting data science research across the laboratory. Bremer’s work has resulted in numerous high-impact publications and several awards (including multiple Best Paper Awards in visualization and data analysis), underscoring his contributions at the intersection of machine learning, visualization, and scientific computing.

 

Ph.D., Dept. of Computer Science, University of California, Davis Major: Computer Graphics; Minor: Mathematics.

Diploma (M.S.) University of Hannover, GermanyMajor: Mathematics; Second Major: Computer Science; Minor: Neuro Anatomy.

Pre-Diploma (B.S.) University of Hannover, Germany Major: Mathematics; Second Major: Computer Science.

Below is a list of recent peer-reviewed publications authored or co-authored by Peer-Timo Bremer, focusing on the last five years. It includes journal articles and high-profile conference papers, with titles, co-authors, venues, and year of publication.

Recent Peer-Reviewed Journal Articles (2020–2024)

  • Bimodal visualization of industrial X-ray and neutron computed tomography data. Xuan Huang, Haichao Miao, Hyojin Kim, et al. (including P.-T. Bremer). IEEE Transactions on Visualization and Computer Graphics, 31(4): 2196–2210 (2025)dblp.org.

  • MapperTrac: A massively parallel, portable, and reproducible tractography pipeline. Lanya T. Cai, Joseph Moon, Paul B. Camacho, et al. (including P.-T. Bremer). Neuroinformatics, 22(2): 177–191 (2024)file-gfazqxf9xdh9ecawa8ijol.

  • AVA: Towards autonomous visualization agents through visual perception-driven decision-making. Shusen Liu, Haichao Miao, Zhimin Li, Matthew Olson, Valerio Pascucci, and P.-T. Bremer. Computer Graphics Forum, 43(3): e15093 (2024)file-gfazqxf9xdh9ecawa8ijol.

  • 2022 Review of data-driven plasma science. Rushil Anirudh, Rick Archibald, M. Salman Asif, Markus M. Becker, Sadruddin Benkadda, P.-T. Bremer, et al. IEEE Transactions on Plasma Science, 51(7): 1750–1838 (2023)file-gfazqxf9xdh9ecawa8ijol.

  • A flexible proton beam imaging energy spectrometer (PROBIES) for high-repetition-rate or single-shot high energy density experiments (invited). D. A. Mariscal, B. Z. Djordjević, Rushil Anirudh, P.-T. Bremer, et al. Review of Scientific Instruments, 94(2): 023507 (2023)file-gfazqxf9xdh9ecawa8ijolfile-gfazqxf9xdh9ecawa8ijol.

  • Predicting membrane orientational states of RAS and RAS-RAF based on the lipid environment using deep learning. Fikret Aydin, Konstantia Georgouli, Gautham Dharuman, James N. Glosli, Timothy S. Carpenter, Felice C. Lightstone, Helgi I. Íngólfsson, P.-T. Bremer, and Harsh Bhatia. Biophysical Journal, 122(3)(Suppl 1): 506a (2023)file-gfazqxf9xdh9ecawa8ijol.

  • A biology-informed similarity metric for simulated patches of human cell membrane. Harsh Bhatia, Jayaraman J. Thiagarajan, Rushil Anirudh, Jayram Thathachar, Tomas Oppelstrup, Helgi I. Íngólfsson, Felice C. Lightstone, and P.-T. Bremer. Machine Learning: Science and Technology, 3(3): 035010 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • A visual comparison of silent error propagation. Zhimin Li, Harshitha Menon, Kathryn Mohror, Shusen Liu, Luanzheng Guo, P.-T. Bremer, and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics, 28(1): 1–15 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • Data-driven model for divertor plasma detachment prediction. Ben Zhu, Menglong Zhao, Harsh Bhatia, Xue-Qiao Xu, P.-T. Bremer, William Meyer, Nami Li, and Thomas Rognlien. Journal of Plasma Physics, 88(5): 895880504 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • Exploring CRD mobility during RAS/RAF engagement at the membrane. Kien Nguyen, César A. Lopez, Chris Neale, et al. (including P.-T. Bremer). Biophysical Journal, 121(19): 3630–3650 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • Accelerating the rate of discovery: Toward high-repetition-rate HED science. T. Ma, D. Mariscal, Rushil Anirudh, P.-T. Bremer, B. Z. Djordjevic, et al. Plasma Physics and Controlled Fusion, 63(10): 104003 (2021)file-gfazqxf9xdh9ecawa8ijol.

  • Efficient and flexible hierarchical data layouts for a unified encoding of scalar field precision and resolution. D. Hoang, B. Summa, Harsh Bhatia, Peter Lindstrom, Pavol Klacansky, William Usher, P.-T. Bremer, and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics, 27(2): 603–613 (2021)file-gfazqxf9xdh9ecawa8ijol.

  • Suppressing simulation bias in multi-modal data using transfer learning. Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, P.-T. Bremer, et al. Machine Learning: Science and Technology, 3(1): 015035 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • Machine learning driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins. Helgi I. Íngólfsson, Chris Neale, Timothy S. Carpenter, Rebika Shrestha, César A. López, et al. (including P.-T. Bremer). Proceedings of the National Academy of Sciences (PNAS), 119(1): e2113297119 (2022)file-gfazqxf9xdh9ecawa8ijolfile-gfazqxf9xdh9ecawa8ijol.

  • Enabling machine learning–ready HPC ensembles with Merlin. J. Luc Peterson, Ben Bay, Joe Koning, et al. (including P.-T. Bremer). Future Generation Computer Systems, 131: 255–268 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • Vector field decompositions using multiscale Poisson kernels. Harsh Bhatia, Robert M. Kirby, Valerio Pascucci, and P.-T. Bremer. IEEE Transactions on Visualization and Computer Graphics, 27(9): 3781–3793 (2021)file-gfazqxf9xdh9ecawa8ijol.

  • Towards replacing physical testing of granular materials with a topology-based model. Aniketh Venkat, Attila Gyulassy, Graham Kosiba, Amitesh Maiti, Henry Reinstein, Richard Gee, P.-T. Bremer, and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics, 28(1): 76–85 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • (Preprint) Models out of line: A Fourier lens on distribution shift robustness. Sara Fridovich-Keil, Brian Bartoldson, James Diffenderfer, Bhavya Kailkhura, and P.-T. Bremer. arXiv preprint arXiv:2207.04075 (2022)file-gfazqxf9xdh9ecawa8ijol.

  • The case for optimized edge-centric tractography at scale. Joseph Y. Moon, Pratik Mukherjee, Ravi K. Madduri, Amy J. Markowitz, Lanya T. Cai, Eva M. Palacios, Geoffrey T. Manley, and P.-T. Bremer. Frontiers in Neuroinformatics, 16: 728621 (2022)file-gfazqxf9xdh9ecawa8ijol.

 

Recent Conference Publications (2020–2024)

  • A virtual environment for collaborative inspection in additive manufacturing. Vuthea Chheang, Brian T. Weston, Robert W. Cerda, Brian Au, Brian Giera, P.-T. Bremer, and Haichao Miao. CHI 2024 Extended Abstracts (ACM Conference on Human Factors in Computing Systems), 2024file-gfazqxf9xdh9ecawa8ijol.

  • Cross-GAN auditing: Unsupervised identification of attribute-level similarities and differences between pretrained generative models. Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, P.-T. Bremer, and Weng-Keen Wong. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7981–7990, 2023file-gfazqxf9xdh9ecawa8ijol.

  • Single model uncertainty estimation via stochastic data centering. Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy, and P.-T. Bremer. In Advances in Neural Information Processing Systems (NeurIPS), 2022file-gfazqxf9xdh9ecawa8ijol.

  • High-quality progressive alignment of large 3D microscopy data. Aniketh Venkat, Duong Hoang, Attila Gyulassy, P.-T. Bremer, Frederick Federer, Alessandra Angelucci, and Valerio Pascucci. In Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 1–10, 2022file-gfazqxf9xdh9ecawa8ijol.

  • Accurate calibration of agent-based epidemiological models with neural network surrogates. Rushil Anirudh, J. J. Thiagarajan, P.-T. Bremer, T. Germann, S. Y. Del Valle, and F. Streitz. In Proceedings of ICML 2022 Workshop on Healthcare AI and COVID-19, 2022file-gfazqxf9xdh9ecawa8ijol.

  • Machine learning–powered mitigation policy optimization in epidemiological models. Rushil Anirudh, J. J. Thiagarajan, P.-T. Bremer, T. Germann, S. Y. Del Valle, and F. Streitz. In Proceedings of ICML 2022 Workshop on Healthcare AI and COVID-19, 2022file-gfazqxf9xdh9ecawa8ijol.

  • Virtual inspection of additively manufactured parts. Pavol Klacansky, Haichao Miao, Attila Gyulassy, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, and P.-T. Bremer. In Proceedings of IEEE Pacific Visualization Symposium (PacificVis), pp. 81–90, 2022file-gfazqxf9xdh9ecawa8ijol.

  • Understanding a program’s resiliency through error propagation. Zhimin Li, Harshitha Menon, Kathryn Mohror, Shusen Liu, Luanzheng Guo, P.-T. Bremer, and Valerio Pascucci. In Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 362–373, 2021file-gfazqxf9xdh9ecawa8ijol.

  • Distributed Merge Forest: A fast and scalable approach for topological analysis at scale. Xuan Huang, Pavol Klacansky, Steve Petruzza, Attila Gyulassy, P.-T. Bremer, and Valerio Pascucci. In Proceedings of the ACM International Conference on Supercomputing (ICS), pp. 367–377, 2021file-gfazqxf9xdh9ecawa8ijol.

  • Portable and composable flow graphs for in situ analytics. Sergei Shudler, Steve Petruzza, Valerio Pascucci, and P.-T. Bremer. In Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 63–72, 2021file-gfazqxf9xdh9ecawa8ijol.

  • Generalizable coordination of large multiscale workflows: Challenges and learnings at scale. Harsh Bhatia, Francesco Di Natale, Joseph Y. Moon, et al. (including P.-T. Bremer). In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Article SC ’21, 2021file-gfazqxf9xdh9ecawa8ijol.

  • Building calibrated deep models via uncertainty matching with auxiliary interval predictors. J. J. Thiagarajan, Bhavya Venkatesh, Puneet Sattigeri, and P.-T. Bremer. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 34: 6005–6012, 2020file-gfazqxf9xdh9ecawa8ijol.

  • A statistical mechanics framework for task-agnostic sample design in machine learning. Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, and P.-T. Bremer. In Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 11925–11935, 2020file-gfazqxf9xdh9ecawa8ijol.

 

 

  • 2022 – Best Paper Award, IEEE Pacific Visualization Conference (PacificVis)

  • 2022 – LLNL Directorate Excellence in Publication Award (Computing Directorate, DDS&T)

  • 2020 – LLNL Directorate Excellence in Publication Award (DDS&T)

  • 2019 – LLNL Directorate Excellence in Publication Award (DDS&T)

  • 2019 – Best Paper Award, ACM/IEEE Supercomputing Conference (SC19)

  • 2018 – LLNL Early Mid-Career Recognition Award

  • 2018 – LLNL Directorate Excellence in Publication Award (DDS&T)

  • 2018 – Best Paper Award, EuroVis (EG/VGTC EuroVis 2018)

  • 2014 – Winner, IEEE VGTC Graphical Visualization Contest (Visualization Challenge), with $500 prize

  • 2012 – Best Paper Award, IEEE Symposium on Large Data Analysis and Visualization (LDAV)f

  • 2011 – Best Paper Award, IEEE Pacific Visualization Conference (PacificVis)

  • 2008 – DOE OASCR Visualization Award (Outstanding Visualization Award at the SciDAC 2008 Conference)

  • 2006 – Best Application Paper Award, IEEE Visualization Conference 2006

  • 2002 – Lawrence Livermore National Laboratory Student Employee Graduate Research Fellowship (competitive research fellowship)

  • 1998 – Award for Outstanding Academic Achievement, Leibniz University Hannover (with monetary scholarship)