Ming Jiang is a computer scientist in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). His current research focuses on applying machine learning to automate simulation workflows and exploiting Big Data analytics for HPC simulation data. His research interests include scientific machine learning, data-intensive computing, multiresolution analysis, and flow visualization.

Ming joined LLNL in 2005 as a postdoctoral researcher in the Data Science & Analytics Group in CASC, developing scalable and progressive techniques for large format imagery and streaming geospatial data. He has been a principal investigator, project lead, and task lead on several collaborative research projects, including machine learning for ALE simulations, data-centric architectures, and real-time space situational awareness.

Ming received his Ph.D. degree in Computer Science and Engineering from The Ohio State University (OSU) in 2005. His dissertation focused on developing a feature-based approach to visualizing and mining large-scale simulation data. He received his B.S. degree in Computer and Information Science with a minor in Mathematics from OSU in 1999 and graduated with Honors and Distinction.

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Professional Activities

Program Committee

• IEEE Visualization Conference, 2010–2012
• International Symposium on Visual Computing (ISVC), 2010–2022
• Conference on Visualization and Data Analysis (VDA), 2011–2022
• IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2012, 2014–2018, 2020–2022
• IEEE Cluster Conference, 2016–2017
• ACM Conference on Knowledge Discovery and Data Mining (KDD), 2021–2022
• SIAM International Conference on Data Mining (SDM), 2022

Conference Committee

• Student Volunteers Chair, IEEE Visualization Conference, 2006–2007
• Birds-of-a-Feather co-Chair, IEEE Visualization Conference, 2008–2009
• Session Chair for “Vector and Tensor Data”, IEEE Visualization Conference, 2010
• Best Paper Committee, IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2015
• Workshop Committee, International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2016
• Birds-of-a-Feather Committee, International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2018
• Session Chair for “In Situ”, IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2020

Review Panelist

• DOE Office of Science, Advanced Scientific Computing Research (ASCR), 2009
• NSF Information & Intelligent Systems (IIS), 2010, 2014, 2017–2018
• DOE Office of Science, Small Business Innovation Research (SBIR), 2010
• DOE Office of Nonproliferation and Verification R&D (NA-22), 2011
• LLNL Laboratory Directed Research and Development (LDRD), 2012–2013, 2016, 2019–2022
• LLNL Computation Innovation Initiative (Idea Days), 2015–2019
• LLNL Computation Technology Base (TechBase), 2016–2018
• LLNL Institutional Scientific Capability Portfolio (ISCP), 2018–2019

Editorship

• LNCS Advances in Visual Computing, Volume 6938, Springer, 2011
• LLNL Computation Annual Report, Scientific Editor, 2014–2015
• LLNL CASC Newsletter, Editor, 2020–2022

Selected Publications

• M. Jiang, B. Gallagher, A. Chu, G. Abdulla, and T. Bender, “Exploiting Spark for HPC Simulation Data: Taming the Ephemeral Data Explosion,” International Conference on High Performance Computing in Asia-Pacific Region (HPCAsia), pp. 150–160, 2020.

• M. Jiang, B. Gallagher, N. Mandell, A. Maguire, K. Henderson, and G. Weinert, “A Deep Learning Framework for Mesh Relaxation in Arbitrary Lagrangian-Eulerian Simulations,” SPIE Applications of Machine Learning, vol. 11139, pp. 168–182, 2019.

• E. Deelman, A. Mandal, M. Jiang, and R. Sakellariou, “The Role of Machine Learning in Scientific Workflows,” International Journal of High Performance Computing Applications, 33(6):1128–1139, 2019.

• R. da Silva, R. Filgueira, I. Pietri, M. Jiang, R. Sakellariou, and E. Deelman, “A Characterization of Workflow Management Systems for Extreme-Scale Applications,” Future Generation Computer Systems, 75:228–238, 2017.

• M. Jiang, B. Gallagher, J. Kallman, and D. Laney, “A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations,” IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 977–982, 2016.

• M. Jiang, B. Van Essen, C. Harrison, and M. Gokhale, “Multi-Threaded Streamline Tracing for Data-Intensive Architectures,” IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2014.

• C. Harrison, P. Navratil, M. Mossalem, M. Jiang, and H. Childs, “Efficient Dynamic Derived Field Generation on Many-Core Architectures Using Python,” Workshop on Python for High Performance and Scientific Computing (PyHPC), pp. 11–20, 2012.

• M. Jiang, W. de Vries, A. Pertica, and S. Olivier, “Computing and Visualizing Reachable Volumes for Maneuvering Satellites,” Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2011.

• B. Summa, G. Scorzelli, M. Jiang, P.-T. Bremer, and V. Pascucci, “Interactive Editing of Massive Imagery Made Simple: Turning Atlanta into Atlantis,” ACM Transactions on Graphics, 30(2), Article 7, 2011.

• M. Jiang, M. Andereck, A. Pertica, and S. Olivier, “A Scalable Visualization System for Improving Space Situational Awareness,” Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), pp. 603–612, 2010.

• R. Roberts, T. Trucano, P. Pope, C. Aragon, M. Jiang, T. Wei, L. Chilton, and A. Bakel, “On the Verification and Validation of Geospatial Image Analysis Algorithms,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 174–177, 2010.

• S. Olivier, K. Cook, B. Fasenfest, D. Jefferson, M. Jiang, J. Leek, J. Levatin, S. Nikolaev, A. Pertica, D. Phillion, K. Springer, and W. de Vries, “High-Performance Computer Modeling of the Cosmos-Iridium Collision,” Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), pp. 720–731, 2009.

• M. Jankun-Kelly, D. Thompson, M. Jiang, B. Shannahan, and R. Machiraju, “Vortex Characterization for Engineering Applications,” AIAA Aerospace Sciences Meeting and Exhibit, Paper 2008–0929, 2008.

• M. Jankun-Kelly, M. Jiang, D. Thompson, and R. Machiraju, “Vortex Visualization for Practical Engineering Applications,” IEEE Transactions on Visualization and Computer Graphics, 12(5):957–964, 2006.

• G. Craciun, M. Jiang, D. Thompson, and R. Machiraju, “Spatial Domain Wavelet Design for Feature Preservation in Computational Datasets,” IEEE Transactions on Visualization and Computer Graphics, 11(2):149–159, 2005.

• R. Machiraju, S. Parthasarathy, J. Wilkins, D. Thompson, B. Gatlin, D. Richie, T.-S. Choy, M. Jiang, S. Mehta, M. Coatney, S. Barr, and K. Hazzard, “Mining Temporally-Varying Phenomena in Scientific Datasets,” Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha, eds, AAAI Press/The MIT Press, pp. 273–290, 2004.

• M. Jiang, R. Machiraju, and D. Thompson, “Detection and Visualization of Vortices,” The Visualization Handbook, C. Johnson and C. Hansen, eds, Academic Press, pp. 287–301, 2004.

• M. Jiang, T.-S. Choy, S. Mehta, M. Coatney, S. Barr, K. Hazzard, D. Richie, S. Parthasarathy, R. Machiraju, D. Thompson, J. Wilkins, and B. Gatlin, “Feature Mining Paradigms for Scientific Data,” SIAM International Conference on Data Mining (SDM), pp. 13–24, 2003.

• M. Jiang, R. Machiraju, and D. Thompson, “Geometric Verification of Swirling Features in Flow Fields,” IEEE Visualization Conference, pp. 307–314, 2002.

• D. Thompson, R. Machiraju, M. Jiang, J. Nair, G. Craciun, and S. Venkata, “Physics-Based Feature Mining for Large Data Exploration,” IEEE Computing in Science & Engineering, 4(4):22–30, 2002.

• M. Jiang, R. Machiraju, and D. Thompson, “A Novel Approach to Vortex Core Region Detection,” Joint Eurographics – IEEE TCVG Symposium on Visualization (VisSym), pp. 217–225, 2002.

• G. Craciun, D. Thompson, R. Machiraju, and M. Jiang, “A Framework for Filter Design Emphasizing Multiscale Feature Preservation,” Third Workshop on Mining Scientific Datasets, pp. 105–111, 2001.

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Selected Presentations

• “Exploiting Spark for HPC Simulation Data: Taming the Ephemeral Data Explosion,” Salishan Conference on High-Speed Computing (virtual), April 2022.

• “Multidisciplinary Research in the Center for Applied Scientific Computing,” Applied Math Colloquium, University of Arizona (virtual), October 2021.

• “Exploiting Spark for HPC Simulation Data: Taming the Ephemeral Data Explosion,” International Conference on High Performance Computing in Asia-Pacific Region (HPCAsia), Fukuoka, Japan, January 2020.

• “A Deep Learning Framework for Mesh Relaxation in Arbitrary Lagrangian-Eulerian Simulations,” SPIE Applications of Machine Learning, San Diego, CA, August 2019.

• “Machine Learning for Semi-Automating Mesh Management in ALE Simulations,” LLNL Predictive Science Panel, Livermore, CA, November 2017.

• “Machine Learning for Semi-Automating Mesh Management in ALE Simulations,” International Conference on Numerical Methods for Multi-Material Fluid Flow (MultiMat), Santa Fe, NM, September 2017. (poster)

• “A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations,” IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, December 2016.

• “Multi-Threaded Streamline Tracing for Data-Intensive Architectures,” IEEE Symposium on Large Data Analysis and Visualization (LDAV), Paris, France, November 2014.

• “LLNL: Visualization, Rendering and GPUs,” DreamWorks Animation, Glendale, CA, March 2012.

• “Computing and Visualizing Reachable Volumes for Maneuvering Satellites,” Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, September 2011. (poster)

• “NAPA: Rapid Exploration and Progressive Processing of Large Format Imagery,” Monsanto Science Fellows' Symposium, St. Louis, MO, February 2011.

• “A Scalable Visualization System for Improving Space Situational Awareness,” Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, September 2010. (poster)

• “Iridium 33 – Cosmos 2251 Collision Simulation and Visualization,” Oceanit, Honolulu, HI, April 2010.

• “NAPA: Rapid Exploration and Progressive Processing of Large Format Imagery,” Motion Imagery Standards Board Conference, Herndon, VA, May 2009.

• “Novel Architectures and Progressive Algorithms for Real-Time Analysis of Streaming Data,” Center for Advanced Signal and Image Science Workshop (CASIS), Livermore, CA, November 2008.

• “Novel Architectures for Progressive Image Analysis,” Stanford Linear Accelerator Center, Menlo Park, CA, February 2008.

• “Progressive Processing and Visualization of High-Resolution Satellite Imagery for Geospatial Intelligence,” LLNL Computing Applications and Research Showcase, Livermore, CA, April 2007.

• “Novel Architectures for Progressive Image Analysis,” NGA Academic Research Program Symposium, Washington, D.C., September 2006.

• “Progressive Techniques for Efficient Processing of Massive Image Models,” Bay Area Scientific Computing Day, Livermore, CA, March 2006.

• “Cache-Oblivious Data Layouts for Real-Time Access to Massive Models,” LLNL Computing Applications and Research Showcase, Livermore, CA, November 2005. (poster)

• “A Feature-Based Approach to Visualizing and Mining Simulation Data,” LLNL Center for Applied Scientific Computing Seminar, Livermore, CA, July 2005.

• “Feature Mining Paradigms for Scientific Data,” SIAM International Conference on Data Mining (SDM), San Francisco, CA, May 2003.

• “Geometric Verification of Swirling Features in Flow Fields,” IEEE Visualization Conference, Boston, MA, October 2002.

• “A Novel Approach to Vortex Core Region Detection,” Joint Eurographics – IEEE TCVG Symposium on Visualization (VisSym), Barcelona, Spain, May 2002.

• “Feature-Significant Exploration of Terascale Datasets,” Yosemite Educational Symposium on Multiscale and Multiresolution Methods, Yosemite National Park, CA, November 2000. (poster)

• LLNL Service Award, LLNL, 2020
• DPD Bronze Award for Publication, LLNL, 2020
• CASC Spot Award, LLNL, 2019
• LLNL Outstanding Mentor Award, LLNL, 2017
• WCI Bronze Star Award for Code Development, LLNL, 2016
• LLNL Service Award, LLNL, 2015
• Certificate of Appreciation in Science Fellows' Symposium, Monsanto, 2011
• DOE Outstanding Mentor Award, LLNL, 2008
• Best Talk Award in Computing Applications and Research Showcase, LLNL, 2007
• Finalist in the Graduate Associate Teaching Award, OSU, 2005
• Outstanding Research Award in Computer Science and Engineering, OSU, 2005
• Excellence in Scholarship Award, OSU, 1999
• Honor Society: Phi Beta Kappa 1999, Upsilon Pi Epsilon 1999, and Phi Kappa Phi 1998

Mentoring & Advising

Graduate Students

• Rutuja Gurav, University of California, Riverside, 2022
• Juan Meriles, University of California, Berkeley, 2021–2022
• Jacqueline Alvarez, University of California, Merced, 2021–2022
• Ava Hill, Michigan State University, 2021
• Sven Amaya, California State University, Sacramento, 2021
• Steven Walton, University of Oregon, 2020
• Ekta Gujral, University of California, Riverside, 2020
• Carlos Pereyra, University of California, Davis, 2019
• Mokbel Karam, University of Utah, 2018
• Tu Mai Anh Do, University of Southern California, 2018
• Eric Riewski, University of Wisconsin, Madison, 2018
• Pravallika Devineni, University of California, Riverside, 2017
• Brian Burrows, Texas A&M University, 2017
• Noah Mandell, Princeton University, 2017
• Chris Bryan, University of California, Davis, 2016–2018
• Arthur Lui, University of California, Santa Cruz, 2016–2017
• Thomas Torsney-Weir, University of Vienna, Austria, 2016
• Phillip Odom, Indiana University, 2016
• Yuriy Ayzman, Texas A&M University, 2015–2016
• Eric Buras, Rice University, 2015
• Hassan Nawaz, University of Southern California, 2015
• Wayne Mitchell, University of Colorado, Boulder, 2014
• Michael Andereck, The Ohio State University, 2009–2011
• Minh Huynh, University of California, Davis, 2007
• Shantanu Singh, The Ohio State University, 2007
• Kenneth Weiss, University of Maryland, College Park, 2006

Undergraduate Students

• Jacob Huckelberry, United States Military Academy, 2020–2021
• Sorie Yillah, United States Military Academy, 2020–2021
• William (Max) Wallace, University of California, Berkeley, 2020
• Joanna Held, University of Iowa, 2019
• Daniel Finnegan, United States Naval Academy, 2019
• Nicholas Stern, Brown University, 2018
• Amy Huang, Harvey Mudd College, 2017–2018
• Evan Chrisinger, Harvey Mudd College, 2017–2018
• Jeb Bearer, Harvey Mudd College, 2017–2018
• Katelyn Barnes, Harvey Mudd College, 2017–2018
• Rebecca Peterson, Brigham Young University, 2016–2017
• Andrew Wheeler, Brigham Young University, 2016–2017
• Matthew Oehler, Brigham Young University, 2016–2017
• Jared Hoff, Brigham Young University, 2016–2017
• Qiasheng Zou, University of Illinois, Urbana-Champaign, 2016–2017
• Xingrui Huang, University of Illinois, Urbana-Champaign, 2016–2017
• Tong Li, University of Illinois, Urbana-Champaign, 2016–2017
• Renee Swischuk, Texas A&M University, 2016
• Joshua Asplund, Brigham Young University, 2015–2016
• Ethan Garofolo, Brigham Young University, 2015–2016
• Li chieh (Jessie) Young, Brigham Young University, 2015–2016
• Damon Alcorn, Las Positas College, 2015–2016
• Nicholas Weidner, University of South Carolina, 2015
• Gavin Sonne, Hartnell College, 2015
• Mohamed Elsharkawy, University of Illinois, Chicago, 2013
• Alexander Badrenkov, University of California, Berkeley, 2012
• Garrett Aldrich, University of California, Davis, 2006–2007