Books and bookchapters

  1. Artificial Intelligence for Science: A Deep Learning Revolution, https://doi.org/10.1142/13123, 2023, Edited By: Alok Choudhary (Northwestern University, USA), Geoffrey Fox (University of Virginia, USA) and Tony Hey (Rutherford Appleton Laboratory, UK). World Scientific Publishing. https://www.worldscientific.com/worldscibooks/10.1142/13123#t=aboutBook

  2. Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, Micah Beck, “Harnessing the Computing Continuum for Programming Our World”, Technical Report March 11 2019 published in “Fog Computing: Theory and Practice” published by Wiley and edited by Albert Zomaya  Assad Abbas  Samee Khan, DOI 25 April 2020 http://infomall.org/publications/paper_computing_continuum-2.pdf DOI

Lecture Notes and course

  1. Cybertraining rehosting and update of previous and current classes. https://cybertraining-dsc.github.io/

Open Source Software and Resources: Current

  1. FFFFWNPF Deep Learning model for time series https://www.researchgate.net/deref/https%3A%2F%2Fcolab.research.google.com%2Fdrive%2F1dUwrxlUq8G8HHTzImJXmJ9qiUFabdIKf%3Fusp%3Dsharing
  2. Cylon “Data Engineering Everywhere” https://github.com/cylondata/cylon/releases/ with tutorial
  3. Twister2 high performance data analytics hosting environment that can work in both cloud and HPC environments https://github.com/DSC-SPIDAL/twister2/releases released May 10 2019 with extensive tutorial.
  4. SPIDAL Github https://github.com/DSC-SPIDAL data analytics library. This is being federated with all software from SPIDAL NSF Dibbs project
  5. WebPlotViz 3D point viewer including time series https://spidal-gw.dsc.soic.indiana.edu/. This replaces Plotviz 3D software below
  6. Cloudmesh DevOps Software originally developed for FutureGrid. Now for NIST and SDSC.
  7. MSI-CIEC Portal for outreach to Minority Serving Institutions
  8. eHumanity cultural portal developed for AIHEC the American Indian Higher Education Consortium with NEH (National Endowment for Humanities) funding


  1. Arup Kumar Sarker, Md. Khairul Islam, Yuan Tian, Geoffrey Fox, “MVAM: Multi-variant Attacks on Memory for IoT Trust Computing”, SafeThings 2023, IEEE/ACM Workshop on the Internet of Safe Things San Antonio, Texas USA, May 9, 2023
  2. J Quetzalcoatl Toledo-Marin, James A Glazier, Geoffrey Fox, “Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation”, https://arxiv.org/abs/2302.03786 submitted to Machine Learning: Science and Technology, March 3, 2023.
  3. Niranda Perera, Arup Kumar Sarker, Mills Staylor, Gregor von Laszewski, Kaiying Shan, Supun Kamburugamuve, Chathura Widanage, Vibhatha Abeykoon, Thejaka Amila Kanewela, Geoffrey Fox “In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes “ Submitted to PPAM Conference 2022 Special Issue in FGCS, Elsevier, February 27, 2023
  4. David Grzan, John B Rundle, Geoffrey C Fox, Andrea Donnellan, “Forecasting tsunami inundation with convolutional neural networks for a potential Cascadia Subduction Zone rupture”. Authorea. February 09, 2023. DOI: 10.22541/essoar.167591125.50103833/v1
  5. EA Huerta, Ben Blaiszik, L Catherine Brinson, Kristofer E Bouchard, Daniel Diaz, Caterina Doglioni, Javier M Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S Katz, Volodymyr Kindratenko, Christine R Kirkpatrick, Kati Lassila-Perini, Ravi K Madduri, Mark S Neubauer, Fotis E Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu, “FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective”, Scientific Data, October 13, 2022, https://arxiv.org/abs/2210.08973, Proceedings of FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.
  6. J. B. Rundle, A. Donnellan, G. Fox, L. Grant Ludwig, and J. P. Crutchfield, “Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?,” Earth and Space Science Open Archive, p. 18, 2022, https://doi.org/10.1002/essoar.10512008.4, arXiv:2208.03839
  7. J. B. Rundle, J. Yazbeck, A. Donnellan, and L. Grant Ludwig, “Optimizing Earthquake Nowcasting with Machine Learning: The Role of Strain Hardening in the Earthquake Cycle,” Earth and Space Science Open Archive. 2022 https://www.essoar.org/doi/abs/10.1002/essoar.10510940.4, Volume10, Issue2, February 2023, e2022EA002521 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EA002521
  8. G. Fox, J. Rundle, A. Donnellan, and B. Feng, “Earthquake Nowcasting with Deep Learning,” arXiv [physics.geo-ph], 18-Dec-2021 [Online]. Available: http://arxiv.org/abs/2201.01869, GeoHazards 3 (2), 199-226 (2022)
  9. V. Abeykoon, S. Kamburugamuve, C. Widanage, N. Perera, A. Uyar, T. A. Kanewala, G. von Laszewski, and G. Fox, “HPTMT Parallel Operators for High Performance Data Science and Data Engineering,” Frontiers in Big Data, vol. 4, 2022 [Online]. Available: https://www.frontiersin.org/article/10.3389/fdata.2021.756041
  10. J. Thiyagalingam, M. Shankar, G. Fox, and T. Hey, “Scientific Machine Learning Benchmarks,” arXiv [cs.LG], 25-Oct-2021 [Online]. Available: http://arxiv.org/abs/2110.12773, Nature Reviews Physics 4 (6), 413-420
  11. E. Garyfallidis, S. Koudoro, J. Guaje, M.-A. Côté, S. Biswas, D. Reagan, N. Anousheh, F. Silva, G. Fox, and Fury Contributors, “FURY: advanced scientific visualization,” J. Open Source Softw., vol. 6, no. 64, p. 3384, Aug. 2021 [Online]. Available: https://joss.theoj.org/papers/10.21105/joss.03384
  12. J. B. Rundle, A. Donnellan, G. Fox, J. P. Crutchfield, and R. Granat, “Nowcasting Earthquakes: Imaging the Earthquake Cycle in California with Machine Learning,” Earth and Space Science, 8(12), 2021. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021EA001757
  13. J. B. Rundle, A. Donnellan, G. Fox, and J. P. Crutchfield, “Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine Learning: A Comparison of Two Methods,” Surveys in Geophysics 43 (2), 483-501. https://doi.org/10.1007/s10712-021-09655-3
  14. A. Uyar, G. Gunduz, S. Kamburugamuve, P. Wickramasinghe, C. Widanage, K. Govindarajan, N. Perera, V. Abeykoon, S. Akkas, and G. Fox, “Twister2 Cross‐platform resource scheduler for big data,” Concurr. Comput., Jul. 2021 [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1002/cpe.6502 and 34 (9), e6502
  15. John B. Rundle, Andrea Donnellan, Geoffrey Fox, James P Crutchfield, Robert A Granat, “Nowcasting Earthquakes: Imaging the Earthquake Cycle in California with Machine Learning”, Earth and Space Science Open Archive,  28 Mar 2021, DOI 10.1002/essoar.10506614.1, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021EA001757
  16. Vibhatha Abeykoon  Geoffrey Fox  Minje Kim  Saliya Ekanayake  Supun Kamburugamuve  Kannan Govindarajan  Pulasthi Wickramasinghe  Niranda Perera  Chathura Widanage  Ahmet Uyar  Gurhan Gunduz  Selahatin Akkas, “Stochastic gradient descent‐based support vector machines training optimization on Big Data and HPC frameworks”, published in Concurrency and Computation: Practice and Experience: 30 March 2021and 34 (8), e6292 (2022). https://doi.org/10.1002/cpe.6292
  17. Quetzalcóatl Toledo-Marín, J; Fox, Geoffrey; Sluka, James P; Glazier, James A, “Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations” February 10, 2021 Front. Physiol., vol. 12, p. 667828, Jun. 2021 [Online]. Available: http://dx.doi.org/10.3389/fphys.2021.667828, Arxiv Researchgate
  18. Thanh Nguyen, Tongbin Zhang, Geoffrey Fox, Sisi Zeng, Ni Cao, Chuandi Pan, Jake Y. Chen, “Linking Clinotypes to Phenotypes and Genotypes from Laboratory Test Results in Comprehensive Physical Exams” BMC Medical Informatics and Decision Making volume 21, Article number: 51 (2021). DOI  Researchgate February 24, 2021
  19. H. He, W. Zhao, S. Huang, G. C. Fox, and Q. Wang, “Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud,” IEEE Trans. Serv. Comput., vol. 13, no. 5, pp. 944–957, 2020 [Online]. Available: http://dx.doi.org/10.1109/TSC.2017.2723006

Conference Papers

  1. Jeyan Thiyagalingam, Gregor von Laszewski, Junqi Yin, Murali Emani, Juri Papay, Gregg Barrett, Piotr Luszczek, Aristeidis Tsaris, Christine Kirkpatrick, Feiyi Wang, Tom Gibbs, Venkatram Vishwanath, Mallikarjun Shankar, Geoffrey Fox, Tony Hey, “AI Benchmarking for Science: Efforts from the MLCommons Science Working Group “,High Performance Computing. ISC High Performance 2022 International Workshops: Hamburg, Germany, May 29–June 2, 2022, Revised Selected Papers, pages 47-64, Springer, January 4, 2023 2. Kaiying Shan, Niranda Perera, Damitha Lenadora, Tianle Zhong, Arup Sarker, Supun Kamburugamuve, Thejaka Amila Kanewela, Chathura Widanage, Geoffrey Fox, “Hybrid Cloud and HPC Approach to High-Performance Dataframes”, https://arxiv.org/abs/2212.13732, Scalable Cloud Data Management Workshop at IEEE Big Data Conference 2022.
  2. John B Rundle, Andrea Donnellan, Geoffrey Fox, James P Crutchfield, Lisa Grant Ludwig, “Earthquake Nowcasting with Machine Learning: Analyzing the Information Content of Earthquake Catalogs for Scenario Earthquakes” December 15, 2022 AGU Fall Meeting 2022
  3. J. Alameda, C. Stirm, G. Bauer, T. Boerner, B. Bode, M. Dahan, W. Gropp, M. Pierce, C. Yewdall, M. Zentner, G. Fox and Others, “Informing Design: Exploring Community Use of GPU Resources for NCSA’s Delta System: Interview Highlights,” Gateways 2022 Conference, San Diego 18-20 October 2022.
  4. X. H. Sun, C. Z. Xu, G. Fox, and S. Chen, “Editorial for 2022 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS),” HPBD&IS
  5. J. Rundle, A. Donnellan, G. Fox, and J. Crutchfield, “Nowcasting Earthquakes in Southern California with Machine Learning: Correlations with Regional Tectonic Stress,” 2021, vol. 2021, AGU Fall Meeting Abstracts 2021, T53B-06. https://ui.adsabs.harvard.edu/abs/2021AGUFM.T53B..06R
  6. X. H. Sun, C. Z. Xu, G. Fox, W. Li, and K. Ye, “Editorial for 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS),” HPBD&IS International Conference, 2021 https://ieeexplore.ieee.org/abstract/document/9658450/
  7. S. Farrell, M. Emani, J. Balma, L. Drescher, A. Drozd, A. Fink, G. Fox, D. Kanter, T. Kurth, P. Mattson, D. Mu, A. Ruhela, K. Sato, K. Shirahata, T. Tabaru, et al., “MLPerfTM HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems,” in 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), 2021, pp. 33–45 [Online]. Available: http://dx.doi.org/10.1109/MLHPC54614.2021.00009
  8. B. Feng and G. C. Fox, “Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California,” in Proceedings of 2021 IEEE 17th International Conference on eScience, Innsbruck, Austria, 2021, pp. 99–107.
  9. S. Kamburugamuve, C. Widanage, N. Perera, V. Abeykoon, A. Uyar, T. A. Kanewala, G. von Laszewski, and G. Fox, “HPTMT: Operator-based architecture for scalable high-performance data-intensive frameworks,” in Proceedings of 2021 IEEE Cloud, Virtual, 2021 [Online]. Available: http://arxiv.org/abs/2107.12807
  10. G. von Laszewski, F. Wang, and G. C. Fox, “Comprehensive evaluation of XSEDE’s scientific impact using semantic scholar data,” in PEARC21 Practice and Experience in Advanced Research Computing, Boston MA USA, 2021 [Online]. Available: https://dl.acm.org/doi/10.1145/3437359.3465601
  11. Gregor von Laszewski , Anthony Orlowski , Richard H. Otten , Reilly Markowitz , Sunny Gandhi , Adam Chai , Geoffrey C. Fox and Wo L. Chang, “Using GAS for Speedy Generation of Hybrid Multi-Cloud Auto Generated AI Services” December 8, 2020 Preprint accepted for COMPSAC 2021: Intelligent and Resilient Computing for a Collaborative World 45th Anniversary Conference All-Virtual July 12-16, 2021 http://infomall.org/publications/vonLaszewski-openapi.pdf
  12. Y. Wang, M. Xu, J. Paden, L. Koenig, G. Fox, and D. Crandall, “Deep Tiered Image Segmentation for Detecting Internal Ice Layers in Radar Imagery,” in Proceedings of 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021, pp. 1–6 [Online]. Available: http://arxiv.org/abs/2010.03712
  13. Pulasthi Wickramasinghe, Geoffrey Fox, “Multidimensional Scaling for Gene Sequence Data with Autoencoders”, Best paper award at Data Science conference CONF-CDS 2021 January 28 2021 Stanford online. 2021 2nd International Conference on Computing and Data Science (CDS), 2021, pp. 516-523, doi: 10.1109/CDS52072.2021.00095 http://infomall.org/publications/Multidimensional%20Scaling%20for%20Gene%20Sequence%20data%20with%20Autoencoders.pdf
  14. Niranda Perera, Vibhatha Abeykoon, Chathura Widanage, Supun Kamburugamuve, Thejaka Amila Kanewala, Pulasthi Wickramasinghe, Ahmet Uyar, Hasara Maithree, Damitha Lenadora, Geoffrey Fox, “A Fast, Scalable, Universal Approach For Distributed Data Aggregations”, International Workshop on Big Data Reduction held with 2020 IEEE International Conference on Big Data Atlanta, GA December 10,2020 http://infomall.org/publications/cylon_aggregates_iwbr20.pdf

Technical reports

  1. Niranda Perera, Kaiying Shan, Supun Kamburugamuwe, Thejaka Amila Kanewela, Chathura Widanage, Arup Sarker, Mills Staylor, Tianle Zhong, Vibhatha Abeykoon, Geoffrey Fox, “Supercharging Distributed Computing Environments For High Performance Data Engineering” https://arxiv.org/pdf/2301.07896.pdf January 19, 2023
  2. Gregor von Laszewski, JP Fleischer, Geoffrey C Fox, “Hybrid Reusable Computational Analytics Workflow Management with Cloudmesh”, October 30, 2022, https://arxiv.org/pdf/2210.16941.pdf
  3. B. Feng and G. Fox, “GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events,” arXiv [cs.LG], 18-Jan-2022 [Online]. Available: https://arxiv.org/pdf/2201.06717.pdf


Several largely virtual talks were given describing this project and how the community can work with MLCommons. This included presentations on refinements of our contributed earthquake forecasting benchmark. The links are recorded in products and include

  1. “Science part of MLCommons Community Meeting,” July 15, 2021. MLCommons Community Meeting. Online
  2. “Study of Earthquakes with Machine Learning,” 22 July 2021. Online presentation to Frankfurt Institute for Advanced Studies.
  3. “HPTMT High-Performance Data Science and Data Engineering based on Data-parallel Tensors, Matrices, and Tables, Slide 37 onwards” July 26, 2021. HPC 2021. Cetraro, Italy
  4. “Study of Earthquakes with Deep Learning,” July 27, 2021. HPC 2021. Cetraro, Italy.
  5. “Research at Intersection of Deep Learning (AI), High Performance Computing and Scientific Data Analysis”, August 27, 2021, University of Virginia Student Class. Computer Science Department
  6. “Study of Earthquakes with Deep Learning,” September 27, 2021. Frankfurt Institute for Advanced Study Seismology & Artificial Intelligence Kickoff Workshop
  7. “Science Working Group at MLCommons Research,” December 9, 2021. MLCommons Community Meeting. online
  8. “Deep Learning for Geospatial Time Series,” December 10, 2021. 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP’21)
  9. “MLCommons Benchmarks,” January 20, 2022. University of Virginia Data Science Event.
  10. “AI for Science illustrated by Deep Learning for Geospatial Time Series,” January 27, 2022. The IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC2022)
  11. “AI Driven Systems: Supercomputers and Clouds”, Thoughtworks e4r Symposium on theme Beyond Moore’s Law, February 19 2022 (zoom)
  12. “SBI: Surrogate Benchmark Initiative. FAIR Surrogate Benchmarks Supporting AI and Simulation Research”, June 7, 2022, FAIR for AI Workshop. Argonne and Online
  13. “Deep Learning for Earthquake Nowcasting,” July 5, 2022. HPC 2022 High Performance Computing, State Of The Art, Emerging Disruptive Innovations And Future Scenarios. Cetraro, Italy.
  14. “Science part of MLCommons Community Meeting,” July 14, 2022. MLCommons Community Meeting NVIDIA HQ Santa Clara, CA
  15. “Deep Learning for Science,” July 21, 2022. REU Undergraduate Presentation. online.
  16. “Advancing Science with AI from the Edge to the Datacenter,” August 3, 2022, Middleware and Grid Interagency Coordination (MAGIC) Meeting by zoom
  17. “AI for Science Patterns”, September 8, 2022, Workshop on Clusters, Clouds, and Data for Scientific Computing CCDSC 2022 Lyons, France
  18. “Algorithms and Systems Enabling AI for Science,” September 20, 2022, CS6190 University of Virginia Class
  19. “Science part of MLCommons Community Meeting,” September 22, 2022. MLCommons Community Meeting (zoom)
  20. “Algorithms and Systems Enabling AI for Science,” October 25, 2022, E500 Indiana University Class
  21. “ NSF HDR² : From Harnessing the Data Revolution to Harvesting the Data Revolution” PI meeting Washington DC: Data and Cyberinfrastructure Panel October 26 2022
  22. “Enabling AI for Science,” November 14, 2022, Keynote at AI4S Workshop SC22 Dallas
  23. ‘MLCommons Science Working Group.’ at SC22 Birds of a Feather November 16, 2022 Dallas
  24. “Surrogate Benchmark Initiative,” Department of Energy Presentation November 28 2022 (zoom)
  25. “Performance: Deep Learning, Data Engineering, and Systems (MLSys)” Systems Meeting December 2, 2022, University of Virginia
  26. “Science part of MLCommons Community Meeting,” December 8, 2022. MLCommons Community Meeting (zoom)
  27. “Algorithms and Systems Enabling AI for Science”. The 23rd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’22), December 7-9, 2022, Sendai, Japan

Theses by Community Grids Laboratory and Digital Science Center Students

Last modified June 30, 2023: Update 2023.md (f6e382b)