Presenter
Brian van Essen

Biography
Brian Van Essen is the Informatics Group leader and a Computer Scientist at 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. 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.
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. 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.
Presentations
Paper
Algorithms
Benchmarks
Deep Learning
Machine Learning
Parallel Programming Languages, Libraries, and Models
Scalable Computing
Sparse Computation
TP
Workshop
AI
Bioinformatics
Cancer
Computational Biology
Programming Systems
W
Paper
Data Analytics
MPI
Performance
Resiliency
Resource Management
State of the Practice
TP
Chair of Sessions
Paper
Applications
Data Management
Deep Learning
GPUs
Machine Learning
Performance
Scalable Computing
TP