{"id":2,"date":"2026-09-09T08:00:34","date_gmt":"2026-09-09T08:00:34","guid":{"rendered":"http:\/\/christian-engelmann.de\/?page_id=2"},"modified":"2026-09-09T21:57:37","modified_gmt":"2026-09-09T21:57:37","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/www.christian-engelmann.info\/","title":{"rendered":"About Me"},"content":{"rendered":"

Extreme-Scale Computing | Fault Resilience | HW\/SW Co-Design Tools | Computing Continuum | Autonomous Experiments<\/b><\/i><\/p>\n

Dr. Christian Engelmann is a Distinguished Computer Scientist<\/a> and the Intelligent Systems and Facilities Research Group<\/a> Leader at Oak Ridge National Laboratory (ORNL)<\/a>, the US Department of Energy’s (DOE) largest multiprogram science and technology laboratory with an annual budget of $2.6 billion and 7,000+ staff. He has more than 25 years experience in software research and development for extreme-scale high-performance computing (HPC) systems. Dr. Engelmann’s research solves computer science challenges in HPC software, such as scalability, dependability, and interoperability.<\/p>\n

Dr. Engelmann’s primary expertise is in HPC resilience<\/a>, i.e., efficiency and correctness in the presence of faults, errors, and failures. He is a leading HPC resilience expert and was a member of the DOE Technical Council on HPC Resilience 2013-15. He received the 2015 DOE Early Career Award for research in resilience design patterns<\/a>. Dr. Engelmann’s secondary expertise is in system software for the instrument-to-edge-to-Cloud-to-center computing continuum<\/a>, enabling science breakthroughs with autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI) driven design, discovery and evaluation. He further has expertise in lightweight simulation of future-generation extreme-scale supercomputers<\/a>, studying the impact of hardware\/software properties on performance and resilience for application-architecture co-design. Dr. Engelmann is also an expert in operating system and runtime software for parallel and distributed systems.<\/p>\n

Dr. Engelmann earned a Dipl.-Ing. (FH) in Computer Systems Engineering from the University of Applied Sciences Berlin, Germany, and a M.Sc. in Computer Science from the University of Reading, UK, both in 2001 as conjoint degrees, and a Ph.D. in Computer Science from the University of Reading in 2008. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). In 2025, was recognized as a Distinguished Contributor of the IEEE Computer Society. He is also a Member of the Society for Industrial and Applied Mathematics (SIAM) and the Advanced Computing Systems Association (USENIX). <\/p>\n

\"View<\/a> | <\/a> | \"View<\/a> | \"DBLP:<\/a> | Scopus ID: 18037364000<\/a><\/span> | \"ORCIDorcid.org\/0000-0003-4365-6416<\/a><\/span> | \"GitHub<\/a><\/p>\n

Contact: engelmannc@computer.org<\/a><\/span> | 2-page biography: \"Publication\"<\/a><\/span> | Resume: Available upon request<\/a><\/span><\/i><\/p>\n

Ongoing Projects<\/h4>\n

2025-…:<\/b> The Transformational AI Model Consortium<\/a>: Creating the Data Broker Standards for the DOE Genesis Mission<\/a>
\n2025-…:<\/b> The American Science Cloud (AmSC)<\/a>: Designing the Data Service architecture and APIs for the DOE Genesis Mission<\/a>
\n2024-…:<\/b> The Resilient Federated Ecosystem for Self-Driving Laboratories<\/a> project creates an error- and failure-resilient federated ecosystem for instrument science, enabling reliable autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence driven design, discovery, and evaluation.
\n2024-…:<\/b> The Privacy-Preserving Federated Learning for Science: Building Sustainable and Trustworthy Foundation Models<\/a> project creates develops efficient communication, memory, and energy optimization techniques for federated learning algorithms, particularly for large-scale foundation models, while ensuring fairness and incentivizing participation.\n<\/p>\n

Recently In the News<\/h4>\n

2025-12:<\/b> IEEE Computer Socienty. The 2025 Class of the Distinguished Contributor Recognition Program<\/a> recognizes members for technical contributions to the computing profession, computing community, and humanity.
\n2025-12-10:<\/b> DOE. Energy Department Advances Investments in AI for Science<\/a>.
\n2025-07-08:<\/b> DOE Advanced Scientific Computing Research. 1.1 million supercomputer node-hours awarded to Privacy-Preserving Federated Learning for Foundation Models<\/a>.
\n2025-04-16:<\/b> ORNL Review. Creating the lab of the future: INTERSECT unites AI and automation to revolutionize scientific discovery<\/a>.
\n2024-10-15:<\/b> ORNL News. New ORNL projects included in $67 million from DOE for AI in science research<\/a>.\n<\/p>\n

Latest Peer-Reviewed Publications<\/h4>\n
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  1. C. Engelmann, A. Ayres, S. DeWitt, M. J. Brim, and B. Eiffert. Building Resilient Self-Driving Laboratories with the INTERSECT Federated Ecosystem<\/b>. In Proceedings of the 39th International Conference on High Performance Computing, Networking, Storage and Analysis (SC) Workshops 2026<\/a>: 8th Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing (XLOOP) 2026<\/a><\/i>, November, 2026. To appear. \"Abstract\"<\/a> \"BibTeX<\/a><\/li>\n
  2. S. Boehm, C. A. Bridges, P. Widener, T. Jones, S. Ghafoor, C. Engelmann, and O. Kuchar. The INTERSECT Scientific Data Layer: An Ontological Framework for Data Provenance for Complex Scientific Workflows<\/b>. In Lecture Notes in Computer Science: Proceedings of the 32nd European Conference on Parallel and Distributed Computing (Euro-Par) 2026 Workshops<\/a>: 3rd Workshop on High-Performance eScience Tools and Applications (HiPES)<\/a><\/i>, August, 2026. To appear. \"Abstract\"<\/a> \"BibTeX<\/a><\/li>\n
  3. O. Kotevska, T. Nguyen, R. F. da Silva, C. Engelmann, and P. Balaprakash. Scalable Federated Learning for Scientific Foundation Models on Leadership-Class Systems<\/b>. In Proceedings of the 21st European Conference on Computer Systems (EuroSyS)<\/a>: 6th European Workshop on Machine Learning and Systems (EuroMLSys)<\/a><\/i>, April, 2026. DOI 10.1145\/3805621.3807639<\/a>. Accept. rate 69.2% (18\/26). \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n
  4. P. Valero-Lara, A. Young, T. Naughton, C. Engelmann, A. Geist, J. S. Vetter, K. Teranishi, and W. F. Godoy. ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads<\/b>. In Proceedings of the Supercomputing Asia \/ International Conference on High Performance Computing in the Asia-Pacific Region (SCA\/HPCAsia) 2026<\/a><\/i>, January, 2026. DOI 10.1145\/3773656.3773659<\/a>. Accept. rate 36.6% (37\/101). \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n
  5. K. Kim, K. Raghavan, O. Kotevska, M. Dorier, R. Madduri, M. Ryu, T. Munson, R. Ross, T. Flynn, A. Kagawa, B. Yoon, C. Engelmann, and F. Yousefian. Privacy-Preserving Federated Learning for Science: Challenges and Research Directions<\/b>. In Proceedings of the 12th IEEE International Conference on Big Data (BigData) 2024<\/a><\/i>, December, 2024. DOI 10.1109\/BigData62323.2024.10825853<\/a>. Accept. rate 18.5% (122\/661). \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n<\/ol>\n

    Highly Cited Peer-Reviewed Publications<\/h4>\n
      \n
    1. M. Snir, R. W. Wisniewski, J. A. Abraham, S. V. Adve, S. Bagchi, P. Balaji, J. Belak, P. Bose, F. Cappello, B. Carlson, A. A. Chien, P. Coteus, N. A. Debardeleben, P. Diniz, C. Engelmann, M. Erez, S. Fazzari, A. Geist, R. Gupta, F. Johnson, S. Krishnamoorthy, S. Leyffer, D. Liberty, S. Mitra, T. Munson, R. Schreiber, J. Stearley, and E. V. Hensbergen. Addressing Failures in Exascale Computing<\/b>. International Journal of High Performance Computing Applications (IJHPCA)<\/a><\/i>, volume 28, number 2, May, 2014. DOI 10.1177\/1094342014522573<\/a>. 555 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n
    2. A. B. Nagarajan, F. Mueller, C. Engelmann, and S. L. Scott. Proactive Fault Tolerance for HPC with Xen Virtualization<\/b>. In Proceedings of the 21st ACM International Conference on Supercomputing (ICS) 2007<\/a><\/i>, June, 2007. DOI 10.1145\/1274971.1274978<\/a>. Accept. rate 23.6% (29\/123). 526 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
    3. D. Fiala, F. Mueller, C. Engelmann, K. Ferreira, R. Brightwell, and R. Riesen. Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing<\/b>. In Proceedings of the 25th IEEE\/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2012<\/a><\/i>, November, 2012. DOI 10.1109\/SC.2012.49<\/a>. Accept. rate 21.2% (100\/472). 397 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
    4. C. Wang, F. Mueller, C. Engelmann, and S. L. Scott. Proactive Process-Level Live Migration in HPC Environments<\/b>. In Proceedings of the 21st IEEE\/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2008<\/a><\/i>, November, 2008. DOI 10.1145\/1413370.1413414<\/a>. Accept. rate 21.3% (59\/277). 249 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
    5. J. Elliott, K. Kharbas, D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann. Combining Partial Redundancy and Checkpointing for HPC<\/b>. In Proceedings of the 32nd International Conference on Distributed Computing Systems (ICDCS) 2012<\/a><\/i>, June, 2012. DOI 10.1109\/ICDCS.2012.56<\/a>. Accept. rate 13.8% (71\/515). 211 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n<\/ol>\n

      Other Significant Publications<\/h4>\n
        \n
      1. M. Kumar, S. Gupta, T. Patel, M. Wilder, W. Shi, S. Fu, C. Engelmann, and D. Tiwari. Study of Interconnect Errors, Network Congestion, and Applications Characteristics for Throttle Prediction on a Large Scale HPC System<\/b>. Journal of Parallel and Distributed Computing (JPDC)<\/a><\/i>, volume 153, July, 2021. DOI 10.1016\/j.jpdc.2021.03.001<\/a>. \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n
      2. G. Ostrouchov, D. Maxwell, R. Ashraf, C. Engelmann, M. Shankar, and J. Rogers. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability<\/b>. In Proceedings of the 33rd IEEE\/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2020<\/a><\/i>, November, 2020. DOI 10.1109\/SC41405.2020.00045<\/a>. Accept. rate 25.1% (95\/378). \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
      3. H. Jeong, Y. Yang, C. Engelmann, V. Gupta, T. M. Low, P. Grover, V. Cadambe, and K. Ramchandran. 3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication<\/b>. In Lecture Notes in Computer Science: Proceedings of the 26th European Conference on Parallel and Distributed Computing (Euro-Par) 2020<\/a><\/i>, August, 2020. DOI 10.1007\/978-3-030-57675-2_25<\/a>. Accept. rate 24.5% (39\/159). \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
      4. D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann. Mini-Ckpts: Surviving OS Failures in Persistent Memory<\/b>. In Proceedings of the 30th ACM International Conference on Supercomputing (ICS) 2016<\/a><\/i>, June, 2016. DOI 10.1145\/2925426.2926295<\/a>. Accept. rate 24.2% (43\/178). \"Abstract\"<\/a> \"Publication\"<\/a> \"Presentation\"<\/a> \"BibTeX<\/a><\/li>\n
      5. C. Engelmann. Scaling To A Million Cores And Beyond: Using Light-Weight Simulation to Understand The Challenges Ahead On The Road To Exascale<\/b>. Future Generation Computer Systems (FGCS)<\/a><\/i>, volume 30, number 0, January, 2014. DOI 10.1016\/j.future.2013.04.014<\/a>. 69 citations. \"Abstract\"<\/a> \"Publication\"<\/a> \"BibTeX<\/a><\/li>\n<\/ol>\n

        Symbols: \"Abstract\" Abstract, \"Publication\" Publication, \"Presentation\" Presentation, \"BibTeX BibTeX Citation<\/small><\/em><\/p>\n