About Me

Extreme-Scale Computing | Fault Resilience | HW/SW Co-Design Tools | Computing Continuum | Autonomous Experiments

Dr. Christian Engelmann is a Distinguished Computer Scientist and the Intelligent Systems and Facilities Research Group Leader at Oak Ridge National Laboratory (ORNL), 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.

Dr. Engelmann’s primary expertise is in HPC resilience, 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. Dr. Engelmann’s secondary expertise is in system software for the instrument-to-edge-to-Cloud-to-center computing continuum, 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, 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.

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).

| | | | Scopus ID: 18037364000 | orcid.org/0000-0003-4365-6416 |

Contact: engelmannc@computer.org | 2-page biography: | Resume: Available upon request

Ongoing Projects

2025-…: The Transformational AI Model Consortium: Creating the Data Broker Standards for the DOE Genesis Mission
2025-…: The American Science Cloud (AmSC): Designing the Data Service architecture and APIs for the DOE Genesis Mission
2024-…: The Resilient Federated Ecosystem for Self-Driving Laboratories 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.
2024-…: The Privacy-Preserving Federated Learning for Science: Building Sustainable and Trustworthy Foundation Models 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.

Recently In the News

2025-12: IEEE Computer Socienty. The 2025 Class of the Distinguished Contributor Recognition Program recognizes members for technical contributions to the computing profession, computing community, and humanity.
2025-12-10: DOE. Energy Department Advances Investments in AI for Science.
2025-07-08: DOE Advanced Scientific Computing Research. 1.1 million supercomputer node-hours awarded to Privacy-Preserving Federated Learning for Foundation Models.
2025-04-16: ORNL Review. Creating the lab of the future: INTERSECT unites AI and automation to revolutionize scientific discovery.
2024-10-15: ORNL News. New ORNL projects included in $67 million from DOE for AI in science research.

Latest Peer-Reviewed Publications

1. C. Engelmann, A. Ayres, S. DeWitt, M. J. Brim, and B. Eiffert. Building Resilient Self-Driving Laboratories with the INTERSECT Federated Ecosystem. In Proceedings of the 39th International Conference on High Performance Computing, Networking, Storage and Analysis (SC) Workshops 2026: 8th Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing (XLOOP) 2026, November, 2026. To appear.
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. In Lecture Notes in Computer Science: Proceedings of the 32nd European Conference on Parallel and Distributed Computing (Euro-Par) 2026 Workshops: 3rd Workshop on High-Performance eScience Tools and Applications (HiPES), August, 2026. To appear.
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. In Proceedings of the 21st European Conference on Computer Systems (EuroSyS): 6th European Workshop on Machine Learning and Systems (EuroMLSys), April, 2026. DOI 10.1145/3805621.3807639. Accept. rate 69.2% (18/26).
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. In Proceedings of the Supercomputing Asia / International Conference on High Performance Computing in the Asia-Pacific Region (SCA/HPCAsia) 2026, January, 2026. DOI 10.1145/3773656.3773659. Accept. rate 36.6% (37/101).
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. In Proceedings of the 12th IEEE International Conference on Big Data (BigData) 2024, December, 2024. DOI 10.1109/BigData62323.2024.10825853. Accept. rate 18.5% (122/661).

Highly Cited Peer-Reviewed Publications

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. International Journal of High Performance Computing Applications (IJHPCA), volume 28, number 2, May, 2014. DOI 10.1177/1094342014522573. 555 citations.
2. A. B. Nagarajan, F. Mueller, C. Engelmann, and S. L. Scott. Proactive Fault Tolerance for HPC with Xen Virtualization. In Proceedings of the 21st ACM International Conference on Supercomputing (ICS) 2007, June, 2007. DOI 10.1145/1274971.1274978. Accept. rate 23.6% (29/123). 526 citations.
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. In Proceedings of the 25th IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2012, November, 2012. DOI 10.1109/SC.2012.49. Accept. rate 21.2% (100/472). 397 citations.
4. C. Wang, F. Mueller, C. Engelmann, and S. L. Scott. Proactive Process-Level Live Migration in HPC Environments. In Proceedings of the 21st IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2008, November, 2008. DOI 10.1145/1413370.1413414. Accept. rate 21.3% (59/277). 249 citations.
5. J. Elliott, K. Kharbas, D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann. Combining Partial Redundancy and Checkpointing for HPC. In Proceedings of the 32nd International Conference on Distributed Computing Systems (ICDCS) 2012, June, 2012. DOI 10.1109/ICDCS.2012.56. Accept. rate 13.8% (71/515). 211 citations.

Other Significant Publications

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. Journal of Parallel and Distributed Computing (JPDC), volume 153, July, 2021. DOI 10.1016/j.jpdc.2021.03.001.
2. G. Ostrouchov, D. Maxwell, R. Ashraf, C. Engelmann, M. Shankar, and J. Rogers. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability. In Proceedings of the 33rd IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis (SC) 2020, November, 2020. DOI 10.1109/SC41405.2020.00045. Accept. rate 25.1% (95/378).
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. In Lecture Notes in Computer Science: Proceedings of the 26th European Conference on Parallel and Distributed Computing (Euro-Par) 2020, August, 2020. DOI 10.1007/978-3-030-57675-2_25. Accept. rate 24.5% (39/159).
4. D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann. Mini-Ckpts: Surviving OS Failures in Persistent Memory. In Proceedings of the 30th ACM International Conference on Supercomputing (ICS) 2016, June, 2016. DOI 10.1145/2925426.2926295. Accept. rate 24.2% (43/178).
5. C. Engelmann. Scaling To A Million Cores And Beyond: Using Light-Weight Simulation to Understand The Challenges Ahead On The Road To Exascale. Future Generation Computer Systems (FGCS), volume 30, number 0, January, 2014. DOI 10.1016/j.future.2013.04.014. 69 citations.

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