Jae-Seung Yeom is a computer scientist at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. His current research focuses include:

• Intelligent orchestration of scientific workflows over heterogeneous compute resources and platforms
• Convergence of the Cloud and HPC in supporting scientific workflows
• Autonomous multi-scale simulations using ML
• Managing data-dependence in scientific workflows

Additionally, since joining the Lab in July 2014, he has been working on various research projects:

• Scaling the performance of deep learning on HPC platforms
• Detecting objects of interest in radiographic images
• Modeling the multi-scale evolutionary dynamics of RNA virus populations using optimistic/reversible parallel discrete event simulations
• Large scale agent-based epidemiological simulation with co-evolving networks of dynamically interacting populations
• Data-driven modeling and optimizing the performance of solver libraries for sparse linear systems

He contributes to Flux, a hierarchical workload management system for scientific computing workloads, and LBANN (Livermore Big Artificial Intelligence), a deep learning training tool kit for HPC.

Jae-Seung obtained his Ph.D. from Virginia Tech in May 2014, and M.S. from Carnegie Mellon University in 2003. He holds B.S. degrees in Astronomy and Computer Science obtained from Yonsei University, South Korea.

• H. Devarajan, L. Pottier, K. Velusamy, H. Zheng, I. Yildirim, O. Kogiou, W. Yu, A. Kougkas,X. Sun, J. Yeom, and K. Mohror, "DFTracer: An Analysis-Friendly Data Flow Tracer for AI-Driven Workflows," The International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Nov 2024.
• H. Devarajan, I. Lumsden, C. Wang, K. Georgouli, T. Scogland, J. Yeom, and M. Taufer, "DYAD: Locality-aware Data Management for Accelerating Deep Learning Training," IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Nov 2024.
• A. Dey, A. Dhakal, T. Islam, J. Yeom, T. Patki, A. Bhatele, A. Movsesyan, and D. Nichols, “Relative Performance Prediction using Semi-Supervised Learning,” IEEE International Workshop on Deep Analysis of Data-Driven Applications (DADA), July 2024.
• I. Lumsden, H. Devarajan, J. Marquez, S. Brink, D. Boehme, O. Pearce, J. Yeom, and M. Taufer, "Empirical Study of Molecular Dynamics Workflow Data Movement: DYAD vs. Traditional I/O Systems," IEEE International Workshop on High Performance Computational Biology (HiCOMB), May 2024.
• D. Nichols, A. Movsesyan, J. Yeom , A. Sarkar , D. Milroy , T. Patki , and A. Bhatele, "Predicting Cross-Architecture Performance of Parallel Programs," IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2024.
• T. Patki, D. Ahn, D. Milroy, J. Yeom, J. Garlick, M. Grondona, S. Herbein, and T. Scogland, "Fluxion: A Scalable Graph-Based Resource Model for HPC Scheduling Challenge," The 18th Workshop on Workflows in Support of Large-Scale Science (WORKS23), Nov. 13, 2023.
• K. Georgouli, J. Yeom, R. C. Blake and A. Navid, "Multi-scale models of whole cells: progress and challenges," Front. Cell Dev. Biol., Sec. Morphogenesis and Patterning, Vol 11, Nov 7, 2023.
• D. Milroy et al., “One Step Closer to Converged Computing: Achieving Scalability with Cloud-Native HPC,” Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC, 2022.
• J. Luc Peterson et al., “Enabling machine learning-ready HPC ensembles with Merlin," Future Generation Computer Systems, Vol. 131, Issue C, Jun 2022.
• J. Yeom, K. Georgouli, R. Blake, and A. Navid, "Towards Dynamic Simulation of a Whole Cell Model," ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB), Aug, 2021.
• S. Jacobs, B. Van Essen, D. Hysom, J. Yeom, T. Moon, R. Anirudh, J. Thiagaranjan, S. Liu, P-T. Bremer, J. Gaffney, T. Benson, P. Robinson, L. Peterson, and B. Spears, "Parallelizing Training of Deep Generative Models on Massive Scientific Datasets, " IEEE International Conference on Cluster Computing (CLUSTER), Nov 2019.
• S. Liu et. al, "Scalable Topological Data Analysis and Visualization for Evaluating Data-driven Models in Scientific Applications, " IEEE Transactions on Visualization and Computer Graphics, Aug 2019.
• J. Thiagarajan, R. Anirudh, B. Kailkhura, N. Jain, T. Islam, A. Bhatele, J. Yeom, and T. Gamblin, "PADDLE: Performance Analysis using a Data-driven Learning Environment," IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2018.
• J. Yeom, T. Kostova-Vassilevska, P. Barnes, D. Jefferson, and T. Oppelstrup, "Exploratory Modeling and Simulation of the Evolutionary Dynamics of Single-stranded RNA Virus Populations," IEEE Intl Workshop on High Performance Computational Biology (HiCOMB), May 2017.
• A. Bhatele, J. Yeom, N. Jain, C. Kuhlman, Y. Livnat, K. Bisset, L. Kale, and M. Marathe, "Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks," to appear at IEEE/ACM Intl Symposium on Cluster, Cloud and Grid Computing (CCGrid), SCALE challenge finalist, May 2017.
• J. Yeom, J. Thiagarajan, A. Bhatele, G. Bronevetsky and T. Kolev, “Data-dependent Performance Modeling of Linear Solvers for Sparse Matrices,” Intl Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 2016.
• A. Marathe, H. Gahvari, J. Yeom, and A. Bhatele, "LibPowerMon: A Lightweight Profiling Framework to Profile Program Context and System-Level Metrics," IEEE IPDPS Workshops 2016.
• N. Jain, A. Bhatele, J. Yeom, M. Adams, F. Miniati, C. Mei, and L. Kale, "Charm++ & MPI: Combining the Best of Both Worlds," IEEE IPDPS, May, 2015.
• L. Wesolowski, R. Venkataraman, A. Gupta, J. Yeom, K. Bisset, Y. Sun, P. Jetley, T. Quinn and L. Kale, "TRAM: Optimizing Fine-grained Communication with Topological Routing and Aggregation of Messages," Intl Conf. Parallel Processing (ICPP), Sept. 2014.
• J. Yeom, A. Bhatele, K. Bisset, E. Bohm, A. Gupta, L. Kale, M. Marathe, D. Nikolopoulos, M. Schulz, and L. Wesolowski, "Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters," IEEE IPDPS, May, 2014.
• K. Bisset, M. Alam, J. Bassaganya-Riera, A. Carbo, S. Eubank, R. Hontecillas, S. Hoops, Y. Mei, K. Wendelsdorf, D. Xie, J. Yeom, and M. Marathe, "High-Performance Interaction-Based Simulation of Gut Immunopathologies with ENISI," IEEE IPDPS, May 2012.
• R. Ferrer, P. Bellens, J. Yeom, S. Schneider, K. Koukos, M. Alvanos, V. Beltran, M. González, X. Martorell, R. Badia, D. Nikolopoulos, A. Bilas, and E. Ayguadé, "Parallel Programming Models for Heterogeneous Multicore Architectures," IEEE Micro, Vol.30, No. 5, pp. 42-53, Sept./Oct. 2010.
• J. Yeom and D. Nikolopoulos, "Strider: Runtime Support for Optimizing Strided Data Accesses on Multi-Cores with Explicitly Managed Memories," ACM/IEEE SC10, Nov. 2010.
• S. Schneider, J. Yeom, and D. Nikolopoulos, "Programming Multiprocessors with Explicitly Managed Memory Hierarchies," IEEE Computer, Vol. 42, No. 12, pp. 28-34, Dec. 2009.
• S. Schneider, J. Yeom, B. Rose, J. Linford, A. Sandu, and D. Nikolopoulos, "A Comparison of Programming Models for Multiprocessors with Explicitly Managed Memory Hierarchies," ACM PPoPP, Feb. 2009.
• F. Blagojevic, M. Curtis-Maury, J. Yeom, S. Schneider and D. Nikolopoulos, "Scheduling Asymmetric Parallelism on a PlayStation3 Cluster," IEEE CCGrid, May 2008.
• J. Yeom, O. Tonguz, and G. Castañón, "Security in All-Optical Networks: Self-Organizing Networks and Attack Avoidance," IEEE ICC, June 2007.

• LLNL Director’s Science and Technology Award: Flux team, “Flux: A Hierarchical Workload Manager for Supercomputing Workflows,” Sept. 2022.
• NERSC Award for Innovative Use of High Performance Computing: A. Bhatele, J. Yeom, N. Jain, C. Kuhlman, Y. Livnat, K. Bisset, L. Kale, and M. Marathe, "Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks," Sept. 2017.