Peer-Timo holds a shared appointment at Lawrence Livermore National Laboratory's (LLNL's) Center for Applied Scientific Computing (CASC), focusing on large-scale data analysis and visualization, and at the University of Utah, serving as Associate Director for Research of the Center for Extreme Data Management Analysis and Visualization (CEDMAV). His research interests include large-scale machine learning, data analysis, visualization, medical image analysis, topology, volume modeling, and virtual reality.

Peer-Timo joined LLNL in December 2006. Prior to that, he was a post-doctoral research associate at the University of Illinois, Urbana-Champaign. He received a Ph.D. in computer science in 2004 from the University of California, Davis, and an M.S. and B.S. in mathematics/computer science from the Leipniz University, Hannover, Germany in 2000 and 1997, respectively.

Peer-Timo is the co-Principal Investigator (PI) of several projects, including a Department of Energy (DOE) project (ACTIV-TBI) that seeks to apply DOE's high performance computing capabilities to accelerate new treatments for traumatic brain injury. He also is the co-PI for LLNL initiatives in cognitive computing, applying advanced computing to biological research, high-dimensional sampling, and variable precision computing, among others. Peer-Timo sits on the Council of LLNL's Data Science Institute, whose mission is to enable excellence in data science research and applications across the Laboratory's core missions.

PROGRAM COMMITTEE LEADERSHIP AND PARTICIPATION

• Co-Chair Workshop on Visual Performance Analytics VPA 2014/2015/2016 conjuction with ACM/IEEE Supercomputing
• Co-Chair TopoInVis 2013 InternationalWorkshop on Topological Methods in Data Analysis and Visualization
• Organizer of the Foundations of Topological Analysis Workshop in affiliation with IEEE Vis 2010
• CEBDA 2018
• EGPGV 2018
• ISC High Performance Computing 2018
• Computer Graphics International 2013, 2014, 2015
• IEEE EuroVis 2012, 2013, 2014, 2017, 2018
• IEEE Visualization 2011, 2012, 2013, 2014
• ReVisE 2009 Refactoring Visualization from Experience
• TopoInVis 2009, 2011, 2013, 2015, 2017 International Workshop on Topological Methods in Data Analysis and Visualization
• NASAGEM New Advances in Shape Analysis and Geometric Modeling 2007

PROFESSIONAL MEMBERSHIPS

• Association for Computing Machinery (ACM)
• Institute of Electrical and Electronics Engineers (IEEE)

RECENT REFEREED PUBLICATIONS (BOOK CHAPTERS, JOURNAL ARTICLES, AND CONFERENCE PROCEEDINGS)

• B. Kailkhura, J. J. Thiagarajan, C. Rastogi, P. V. Varshney, and P.-T. Bremer. A spectral approach for the design of experiments: Design, analysis and algorithms. Journal of Machine Learning Research, 2018.
• B. K. Spears, J. Brase, P.-T. Bremer, B. Chen, J. Field, J. Gaffney, M. Kruse, S. Langer, K. Lewis, R. Nora, J. L. Peterson, J. J. Thiagarajan, B. Van Essen, and K. Humbird. Deep learning: A guide for practitioners in the physical sciences. Physics of Plasmas, 25(8):080901, 2018.
• D. Hoang, P. Klacansky, H. Bhatia, P.-T. Bremer, P. Lindstrom, and V. Pascucci. A study of the trade-off between reducing precision and reducing resolution for data analysis and visualization. IEEE Trans. Vis. Comp. Graph., 2018.
• A. Gyulassy, P.-T. Bremer, and V. Pascucci. Shared-memory parallel computation of morse-smale complexes with improved accuracy shared-memory parallel computation of morse-smale complexes with improved accuracy. IEEE Trans. Vis. Comp. Graph., 2018.
• H. Bhatia, A. Gyulassy, V. Lordi, J. Pask, V. Pascucci, and P.-T. Bremer. Topoms: Comprehensive topological exploration for molecular and condensed-matter systems. Journal of Comp. Chem., 2018.
• G. Kindlmann, C. Chiw, T. Huynh, A. Gyulassy, P.-T. Bremer, and J. Reppy. Rendering and extracting extremal features in 3d fields. Comput. Graph. Forum, 2018.
• H. Bhatia, N. Jain, A. Bhatele, Y. Livnat, V. Pascucci, and P.-T. Bremer. Interactive investigation of traffic congestion on fat-tree networks using treescope interactive investigation of traffic congestion on fat-tree networks using treescope. Comput. Graph. Forum, 2018.
• S. Liu, J. J. Thiagarajan, and P.-T. Bremer. Exploring high-dimensional structure via axis-aligned decomposition of linear projections. Comput. Graph. Forum, 2018.
• R. Anirudh, H. Kim, J. J. Thiagarajan, K.A. Mohan, K. Champley, and P.-T. Bremer. Lose the views: Limited angle CT reconstruction via implicit sinogram completion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
• S. Petruzza, S. Treichler, V. Pascucci, and P.-T. Bremer. Babelflow: An embedded domain specific language for parallel analysis and visualization. In Proc. IEEE International Parallel & Distributed Processing Symposium, 2018.
• H. I. Ingolfsson, T. S. Carpenter, H. Bhatia, P.-T. Bremer, S. J. Marrink, and F. C. Lightstone. Computational lipidomics of the neuronal plasma membrane. Biophysical Journal, 113(10):2271–2280, 2017.
• S. Liu, D. Maljovec, B. Wang, P.-T. Bremer, and V. Pascucci. Visualizing high-dimensional data: Advances in the past decade. IEEE Trans. Vis. Comp. Graph., 2017.
• S. Liu, P.-T. Bremer, J. Thiagarajan, V. Srikumar, B. Wang, Y. Livnat, and V. Pascucci. Visual exploration of semantic relationships in neural word embeddings. IEEE Trans. Vis. Comp. Graph., 2017.
• W. Usher, P. Klacansky, F. Federer, P.-T. Bremer, A. Knoll, A. Angelucci, and V. Pascucci. A virtual reality visualization tool for neuron tracing. IEEE Trans. Vis. Comp. Graph., 2017.
• M. Ong, H. Bhatia, A. Gyulassy, E. Draeger, V. Pascucci, P.-T. Bremer, V. Lordi, and J. Pask. Complex ion dynamics in carbonate lithium-ion battery electrolytes. Physiccal Chemeisty C, 121(12), 2017.
• A. Gimenez, T. Gamblin, I. Jusufi, A. Bhatele, M. Schulz, P.-T. Bremer, and B. Hamann. Memaxes: Visualization and analytics for characterizing complex memory performance behaviors. IEEE Trans. Vis. Comp. Graph., 2017.
• R. Anirudh, B. Kailkhura, J. Thiagarajan, and P.-T. Bremer. Poisson disk sampling on the grassmannnian: Applications in subspace optimization. In Proc. IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.
• J. Miller, J. Thiagarajan, P.-T. Bremer, N. Honda, D. Stern, and R. Mifflin. Data-driven metric learning for history matching. In Proc. SPE Reservoir Simulation Conference, 2017.
• J. Miller, J. Thiagarajan, P.-T. Bremer, N. Honda, D. Stern, and R. Mifflin. Data-driven metric learning for history matching. Nov 2016.
• A. Landge, P.-T. Bremer, A. Gyulassy, and V. Pascucci. Notes on the distributed computation of merge trees on cw-complexes. In Topological Methods in Data Analysis and Visualization IV. Springer International Publishing, 2016.
• A. Gyulassy, A. Knoll, C. Kah, B. Lau, Wang, M. E. Bremer, L. A. Papka, V. Curtiss, and V. Pascucci. Morse-smale analysis of ion diffusion in ab initio battery materials simulations. In Topological Methods in Data Analysis and Visualization IV. Springer International Publishing, 2016.
• P.-T. Bremer. Adapt – adaptive thresholds for feature extraction. In Topological Methods in Data Analysis and Visualization IV, Mathematics and Visualization. Springer International Publishing, 2016.
• B. Kailkhura, J. Thiagarajan, and P.-T. Bremer. Stair blue noise sampling. ACM Trans. on Graph., 2016.
• D. Niu, P.-T. Bremer, P. Lindstrom, B. Hamann, Y. Zhou, and C.Zhang. Two-dimensional shape retrieval using the distribution of extrema of laplacian eigenfunctions. The Visual Computer, pages 1–18, 2016.
• S. Liu, P.-T. Bremer, J. Thiagarajan, B. Wang, B. Summa, and V. Pascucci. Grassmannian atlas: A general framework for exploring linear projections of high-dimensional data. Comput. Graph. Forum, 2016.
• P.-T. Bremer, A. Gruber, J. Bennett, A. Gyulassy, H. Kolla, J. Chen, and R.W. Grout. Identifying turbulent structures through topological segmentation. Com. in App. Math. and Comp. Sci., 11(1):37–53, 2016.
• K. E. Isaacs, T. Gamblin, A. Bhatele, M. Schulz, B. Hamann, and P.-T. Bremer. Ordering traces logically to identify lateness in message passing programs. IEEE Transactions on Parallel and Distributed Systems (TPDS), 27(3):829–840, 2016.
• W. Widanagamaachchi, Y. Livnat, P.-T. Bremer, S. Duvall, and V. Pascucci. Interactive visualization and exploration of patient progression in a hospital setting‘. In Proc. Workshop on Visual Analytics in Healthcare, 2016.
• H. Nguyen, L. Wei, A. Bhatele, T. Gamblin, D. Boehme, M. Schulz, K.-L. Ma, and P.-T. Bremer. Vipact: A visualization interface for analyzing calling context trees. In Proc. Third Workshop on Visual Performance Analysis (VPA), 2016.
• W. Widanagamaachchi, A. Jacques, B. Wang, E. Crosman, P.-T. Bremer, V. Pascucci, and J Horel. Exploring the evolution of pressure-perturbations to understand atmospheric phenomena. In IEEE Pacific Visualization Symposium (PacificVis), 2017.
• D. Bohme, T. Gamblin, P.-T. Bremer, O. Pearce, and M. Schulz. General-purpose performance introspection for hpc software stacks. In Proc. ACM/IEEE Conference on Supercomputing (SC16), 2016.
• H. Bhatia, A. G. Gyulassy, V. Pascucci, M. Bremer, M. T. Ong, V. Lordi, E. W. Draeger, J. E. Pask, and P.-T. Bremer. Interactive exploration of atomic trajectories through relativeangle
• distribution and associated uncertainties. In 2016 IEEE Pacific Visualization Symposium (PacificVis), pages 120–127, 2016.
• I. Rodero, M. Parashar, A. G. Landge, S. Kumar, V. Pascucci, and P.-T. Bremer. Evaluation of in-situ analysis strategies at scale for power efficiency and scalability. In 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pages 156–164, 2016.
• C. Christensen, S. Liu, G. Scorzelli, J.-W. Lee, P.-T. Bremer, and V. Pascucci. Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization. In Proc. IEEE Symposium Large-Scale Data Analysis and Visualization, 2016.
• A. Bhatele, N. Jain, Y. Livnat, V. Pascucci, and P.-T. Bremer. Analyzing network health and congestion in dragonfly-based supercomputers. In Proc. IEEE International Parallel & Distributed Processing Symposium, 2016.
• B. Kailkhura, J. Thiagarajan, P.-T. Bremer, and P. K. Varshney. Theoretical guarantees for poisson disk sampling using pair correlation function. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
• B. Kailkhura, J. Thiagarajan, P.-T. Bremer, and P. K. Varshney. Impact of spectral sampling techniques on surrogate modeling. In Proc. SIAM Conf. on Uncertainty Quantification, 2016.
• Q. Sun, M. Romanus, T. Jin, H. Yu, P.-T. Bremer, S. Petruzza, S. Klasky, and M. Parashar. In-staging data placement for asynchronous coupling of task-based scientific workflows. In 2016 Second International Workshop on Extreme Scale Programming Models and Middlewar (ESPM2), pages 2–9, Nov 2016.
• M. Schulz, A. Bhatele, D. Bohme, P.-T. Bremer, T. Gamblin, A. Gimenez, and K. Isaacs. A Flexible Data Model to Support Multi-domain Performance Analysis, pages 211–229. Springer International Publishing, 2015.
• A. Gyulassy, H. Bhatia, P.-T. Bremer, and V. Pascucci. Computing accurate morse-smale complexes from gradient vector fields. In Janine Bennett, Fabien Vivodtzev, and Valerio Pascucci, editors, Topological and Statistical Methods for Complex Data, Mathematics and Visualization, pages 205–218. Springer Berlin Heidelberg, 2015.
• D. Maljovec, S. Liu, B. Wang, D. Mandelli, P.-T. Bremer, V. Pascucci, and C. Smith. Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study. Reliability Engineering & System Safety, 2015.
• A. Gyulassy, A. Knoll, K. Lau, B. Wang, P. Bremer, M. Papka, L. Curtiss, and V. Pascucci. Interstitial and interlayer ion diffusion geometry extraction in graphitic nanosphere battery materials. IEEE Trans. Vis. Comp. Graph., PP(99):1–1, 2015.
• S. Liu, B. Wang, J. Thiagarajan, P.-T. Bremer, and V. Pascucci. Visual exploration of high-dimensional data through subspace analysis and dynamic projections. Comput. Graph. Forum, 2015.
• H. Bhatia, B. Wang, G. Norgard, V. Pascucci, and P.-T. Bremer. Local, smooth, and consistent Jacobi set simplification. Computational Geometry, 48(4):311–332, 2015.
• P.-T. Bremer, D. Maljovec, A. Saha, B. Wang, J. Gaffney, B. Spears, and V. Pascucci. Nddav: N-dimensional data analysis and visualization. Computing and Visualization in Science, 17(1):1–18, 2015.
• A. Bhatele, A. R. Titus, J. J. Thiagarajan, N. Jain, T. Gamblin, P.-T. Bremer, M. Schulz, and L. V. Kale. Identifying the culprits behind network congestion. In Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE International, pages 113–122, May 2015.
• B. Husain, A. Gimenez, J. A. Levine, T. Gamblin, and P.-T. Bremer. Relating memory performance data to application domain data using an integration api. In Proceedings of the 2nd Workshop on Visual Performance Analysis, VPA ’15, pages 5:1–5:8, 2015.
• H. Kim, J. Thiagarajan, and P.-T. Bremer. A randomized ensemble approach to industrial ct segmentation. In Proc. ICCV 2015, 2015.
• W. Widanagamaachchi, P. Klacansky, H. Kolla, J. Chen, A. Bhagatwala, V. Pascucci, and P.-T. Bremer. Tracking features in embedded surfaces: Understanding extinction in turbulent combustion. In Proc. IEEE Symposium Large-Scale Data Analysis and Visualization, 2015.
• S. Liu, D. Maljovec, B. Wang, P.-T. Bremer, and V. Pascucci. Visualizing High-Dimensional Data: Advances in the Past Decade. In Proc. Eurographics Conference on Visualization (EuroVis) - STARs, 2015.
• K. Isaacs, A. Bhatele, J. Lifflander, D. Bohme, T. Gamblin, M. Schulz, B. Hamann, and P.-T. Bremer. Recovering logical structure from charm++ event traces. In Proc. ACM/IEEE Conference on Supercomputing (SC15), 2015.
• S. Liu, D. Maljovec, B. Wang, P.-T. Bremer, and V. Pascucci. Visualizing high-dimensional data: Advances in the past decade. In Proc. Eurographics Conference on Visualizatoin (EuroVis 15) State of the Art Report. 2015.
• A. Bhatele, A. Titus, J. Thiagarajan, N. Jain, T. Gamblin, P.-T. Bremer, M. Schulz, and L. Kale. Identifying the culprits behind network congestion. In Proc. IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2015.

• 2018 LLNL Early Mid-Career Recognition Award
• 2018 DDS&T Excellence in Publication Award
• 2014 VGTC GTC Visualization Contest
• 2012 IEEE Symposium on Large Data Analysis and Visualization: Best paper award
• 2011 IEEE Pacific Visualization Conference: Best paper award
• DOE OASCR Visualization Award: Annual SciDac Conference 2008, Seattle
• 2006 IEEE Visualization Conference 2006: Best application paper award
• 2002 Accepted into the Student Employee Graduate Research Fellowship program of the Lawrence Livermore National Laboratory
• 1998 Award for Outstanding Academic Achievement