Sam AdeJacobs

Computer Scientist
Center for Applied Scientific Computing
Email: jacobs32@llnl.gov
Phone: +19254223819

Sam Ade Jacobs is a computer scientist and a project lead at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). He received his Ph.D. in Computer Science from Texas A&M University. Sam’s broad research experiences, expertise, and interests include scalable deep learning, parallel computing, large-scale graph analytics, and robot motion planning.

Sam's current work focuses on developing new scalable algorithms for large scale neural architecture search and training with applications in image and natural language processing, drug design, high energy physics and other scientific applications. Sam’s work represents significant advances to the broader field of computational sciences as well as to (big) data science by providing and maintaining unique data analytics capabilities. His approaches and tools are designed to operate on massive supercomputers at LLNL and are tested on those machines.

Selected Publications

Arpan Jain, Tim Moon, Tom Benson, Hari Subramoni, Sam Ade Jacobs, Dhabaleswar K Panda, Brian Van Essen. “SUPER: SUb-Graph Parallelism for TransformERs”, to appear in 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS), May 17-21, 2021.

Sam Ade Jacobs, Tim Moon, Kevin McLoughlin, Derek Jones, David Hysom, Dong H. Ahn, John Gyllenhaal, Pythagoras Watson, Felice C. Lightstone, Jonathan E. Allen, Ian Karlin, Brian Van Essen. “Enabling Rapid COVID-19 Small Molecule Drug Design Through Scalable Deep Learning of Generative Models”, to appear as finalist for the Supercomputing 2020 (SC20) Gordon Bell Special Prize in the The International Journal of High Performance Computing Applications (IJHPCA), Nov. 2020.

Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears, “Parallelizing Training of Deep Generative Models on Massive Scientific Datasets,” In Proc. of IEEE Cluster, Alberqueue, NM, USA, September 2019

Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer, “Scalable Topological Data Analysis and Visualization for Evaluating Data-Drive Modes in Scientific Application,” In IEEE Trans. on Visualization and Computer Graphics, August 2019

Sam Ade Jacobs, Nikoli Dryden, Roger Pearce, Brian Van Essen. “Towards Scalable Parallel Training of Deep Neural Networks,” In Proc. of Machine Learning in HPC Environments, Denver, CO, USA, November 2017

Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen, “Communication Quantization for Data-Parallel Training of Deep Neural Networks,” In Proc. of Machine Learning in HPC Environments, Salt Lake City, UT, USA, November 2016

Erika Sy, Sam Ade Jacobs, Aldo Dagnino, Yu Ding, “Graph-Based Clustering for Detecting Frequent Patterns in Event Log Data,” In IEEE Conf. on Automation and Science & Engineering, Fort Worth, TX, USA, August 2016

Sam Ade Jacobs, Aldo Dagnino, “Large-Scale Industrial Alarm Reduction and Critical Events Mining using Graph Analytics on Spark,” In IEEE Int. Conf. on Big Data Computing Service and Application, Oxford, UK, April 2016

Adam Fidel, Sam Ade Jacobs, Shishir Sharma, Nancy Amato, Lawrence Rauchwerger, “Using Load Balancing to Scalably Parallelize Motion Planning Algorithms,” International Parallel and Distributed Systems Conference (IPDPS), Phoenix, AZ, USA, May 2014.

Sam Ade Jacobs, Kasra Manavi, Juan Burgos, Jory Denny, Shawna Thomas, Nancy Amato, “A Scalable Method for Parallelizing Sampling-Based Motion Planning Algorithms” IEEE Int. Conf. on Robotics and Automation (ICRA), St. Paul, MN, USA, May 2012.

Sam Ade Jacobs, Nancy Amato, “From Days to Seconds: Scalable Parallel Algorithms for Motion Planning” In Supercomputing 2011 (SC’11) Companion Proceedings, Seattle, WA, USA, Nov 2011.