Brian is the Informatics Group leader and a Computer Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory (LLNL). He is actively pursuing research in large-scale deep learning for scientific domains and training deep neural networks using high-performance computing systems. He is the project leader for the Livermore Big Artificial Neural Network (LBANN) open-source deep learning toolkit. Additionally, he co-leads an effort to mapping these scientific, data-intensive, and machine learning applications to Neuromorphic architectures. His research interests also include developing new Operating Systems and Runtimes (OS/R) that exploit persistent memory architectures, including distributed and multi-level non-volatile memory hierarchies, for high-performance, data-intensive computing.

Dr. Van Essen joined LLNL in October of 2010 after earning his Ph.D. in Computer Science and Engineering from the University of Washington in Seattle, where he studied architectural techniques for improving the energy efficiency of Coarse-Grained Reconfigurable Arrays. He also holds a M.S in Computer Science and Engineering from the University of Washington, a M.S in Electrical and Computer Engineering from Carnegie Mellon University, and a B.S. in Electrical and Computer Engineering from Carnegie Mellon University.

Prior to his graduate studies, Brian co-founded two startups in the area of reconfigurable computing and worked as a verification engineer at Cisco Systems.

Sam Ade Jacobs, Nikoli Dryden, Roger Pearce, and Brian Van Essen. “Towards Scalable Parallel Training of Deep Neural Networks ,” in Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments (MLHPC '17), pages 1-8, Nov. 2017.

Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, and Matthew Baughman. “CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research,” in Proc. Computational Approaches for Cancer @ SC 2017, Nov. 2017.

Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, David P. Widemann, Adam T. Moody, Brian C. Van Essen, and Qinru Qiu. 2017. A spike-based long short-term memory on a neurosynaptic processor. In Proceedings of the 36th International Conference on Computer-Aided Design (ICCAD '17). IEEE Press, Piscataway, NJ, USA, 631-637.

Swann Perarnau, Judicael A. Zounmevo, Matthieu Dreher, Brian C. Van Essen, Roberto Gioiosa, Kamil Iskra, Maya B. Gokhale, Kazutomo Yoshii and Pete Beckman, “Argo NodeOS: Toward Unified Resource Management for Exascale,” to appear in 31th IEEE International Parallel & Distributed Processing Symposium, May 2017.

Md Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann and Tarek M. Taha, “Convolutional Sparse Coding on Neurosynaptic Cognitive System,” in Proceedings of International Conference on Neural Networks, May 2017.

Md Zahangir Alom, Brian Van Essen, Adam T. Moody, David Peter Widemann and Tarek M. Taha, “Quadratic Unconstrained Binary Optimization (QUBO) on Neuromorphic Computing System,” in Proceedings of International Conference on Neural Networks, May 2017.

Nikoli Dryden, Sam Ade Jacobs, Tim Moon, Brian Van Essen. “Communication quantization for data-parallel training of deep neural networks,” in Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments (MLHPC '16), pages 1-8, Nov. 2016.

Brian Van Essen, Hyojin Kim, Roger Pearce, Kofi Boakye, Barry Chen. “LBANN: Livermore Big Artificial Neural Network HPC Toolkit,” in Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments (MLHPC '15), pages 5:1-6, Nov. 2015.

Kark Ni, Roger Pearce, Eric Wan, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Chen. “Large-scale deep learning on the YFCC100M dataset,'' CoRR, abs/1502.03409, 2015.

Ming Jiang, Brian Van Essen, Cyrus Harrison, Maya Gokhale, “Multi-threaded streamline tracing for data-intensive architectures,” in IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), pages 11-18, Nov. 2014.

Brian Van Essen, Henry Hsieh, Sasha Ames, Roger Pearce, Maya Gokhale. “DI-MMAP--a scalable memory-map runtime for out-of-core data-intensive applications,” Cluster Computing, 18(1):15-28, March 2013.

Brian Van Essen, Henry Hsieh, Sasha Ames, Maya Gokhale. “DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications,” in the 1st International Workshop on Data-Intensive Scalable Computing Systems (DISCS-2012), Nov. 16 2012, Salt Lake City, UT. LLNL- CONF-583953

Dries Kimpe, Kathryn Mohror, Adam Moody, Brian Van Essen, Maya Gokhale, Rob Ross and Bronis R. de Supinski. “Integrated In-System Storage Architecture for High Performance Computing,” in the Proceedings of the 2nd International Workshop on Runtime and Operating Systems for Supercomputers (ROSS '12), June 29 2012, Venice, Italy.

Brian Van Essen, Roger Pearce, Sasha Ames, Maya Gokhale. “On the role of NVRAM in data-intensive architectures: an evaluation,” in the 26th IEEE International Parallel & Distributed Processing Symposium, May 21-25 2012, Shanghai, China.

Brian Van Essen, Chris Macaraeg, Ryan Prenger, Maya Gokhale. “Accelerating a random forest classifier: multi-core, GP-GPU, or FPGA?,” to appear in the 20th Annual International IEEE Symposium on Field-Programmable Custom Computing Machines, April 29 2012 - May 1 2012, Toronto, Canada.

Brian Van Essen, Robin Panda, Aaron Wood, Carl Ebeling, and Scott Hauck. “Energy-efficient specialization of functional units in a Coarse-Grained Reconfigurable Array,” in the 19th ACM/SIGDA International Symposium on Field-Programmable Gate arrays, Feb. 27 2011 - Mar. 1 2011.

Brian Van Essen, Robin Panda, Carl Ebeling, and Scott Hauck. “Managing Short-lived and Long-lived Values in Coarse-Grained Reconfigurable Arrays,” in Proceedings of 2010 IEEE International Conference on Field Programmable Logic and Applications, Aug. 31 2010 - Sept. 2 2010.

Brian Van Essen, Aaron Wood, Allan Carroll, Stephen Friedman, Robin Panda, Benjamin Ylvisaker, Carl Ebeling, and Scott Hauck, "Static Versus Scheduled Interconnect in Coarse-Grained Reconfigurable Arrays,” in Proceedings of 2009 IEEE International Conference on Field Programmable Logic and Applications, pages 268-275, Aug. 31 2009-Sept. 2 2009.

Stephen Friedman, Allan Carroll, Brian Van Essen, Benjamin Ylvisaker, Carl Ebeling, and Scott Hauck, "SPR: an architecture-adaptive CGRA mapping tool,” In Proceedings of 2009 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Feb. 2009, 191-200.

Benjamin Ylvisaker, Brian Van Essen, and Carl Ebeling. "A Type Architecture for Hybrid Micro-Parallel Computers,” In Proceedings of 2006 IEEE Symposium on Field-Programmable Custom Computing Machines, April 2006.

orcid.org/0000-0002-2281-7500