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Line Pouchard

Senior Researcher


Line Pouchard leads efforts to create new approaches for data management, curation, and discovery at the extreme scale, focused on provenance and semantic technologies. Her interests include improving discovery and analysis tools for experimental data, workflow performance, and the reproducibility of scientific and computational experiments. 

Computational Sciences Research Activities

  • Provenance for performance analysis and workflow execution at extreme scales
  • Data curation and discovery at the National Synchrotron Light Source II (NSLS-II), including experimental data provenance
  • Computational reproducibility
  • Text mining from the scientific literature


  • Ph.D., Comparative Literature, The Graduate Center, City University of New York
  • M.S., Information Science, University of Tennessee, Knoxville

Professional Appointments

  • 2017-present, Senior Researcher, Center for Data-Driven Discovery, Computational Science Initiative, Brookhaven National Laboratory
  • 2014-2016, Assistant Professor and Computational Science Information Specialist, Research Data Group, Purdue University Libraries
  • 2013-2014, Joint Faculty Appointment, School of Information Sciences, University of Tennessee, Knoxville
  • 2001-2014, Staff Scientist/Research Associate, Computer Science and Mathematics Division, Oak Ridge National Laboratory
  • 1999-2001, Post-doctoral Appointment, Oak Ridge Associated Universities

Selected Publications & Research Highlights

Pouchard L, Y Lin, and H Van Dam (2020). Replicating Machine Learning Experiments in Materials Sciences. In: Advances in Parallel Computing, Parallel Computing: Technology Trends, vol. 36., eds. I Foster, GR Joubert, L Kucera, WE Nagel, and F Peters, pp. 743-755. DOI: 10.3233/APC200105.

Pouchard L, P Juhas, G Park, H Van Dam, SI Campbell, E Stavitski, S Billinge, and CJ Wright (2020). Provenance Infrastructure for Multi-modal X-ray Experiments and Reproducible Analysis. Chapter 15: Handbook on Big Data and Machine Learning in the Physical Sciences, Vol 2: Advanced Analysis Solutions for Leading Experimental Techniques, eds. K Kleese van Dam, KG Yager, SI Campbell, R Farnsworth, and M van Dam, pp. 307-331. World Scientific Publishing. DOI: 10.1142/9789811204579_0015.

Peterka T, D Bard, JC Bennett, EW Bethel, RA Oldfield, L Pouchard, C Sweeney, and M Wolf (2020). Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources. The International Journal of High Performance Computing Applications. Online first, March 27, 2020. DOI: 10.1177/1094342020913628.

Pouchard L, S Baldwin, T Elsethagen, S Jha, B Raju, E Stephan, L Tang, and K Kleese van Dam (2019). Computational reproducibility for scientific workflows at extreme scales. The International Journal of High Performance Computing Applications 33(5):763-776. DOI: 10.1177/1094342019839124.

Pouchard L, S Campbell, P Juhas, and K Kleese van Dam (2019). Experimental data curation at large instrument facilities with open source software. Presented at International Digital Curation Conference (IDCC 19). February 4-6, 2019, Melbourne, Australia. Available online.

Pouchard L, K Huck, G Matyasfalvi, D Tao, L Tang, H van Dam, and S Yoo (2018). Prescriptive provenance for streaming analysis of workflows at scale. In Proceedings of the IEEE 2018 New York Scientific Data Summit (NYSDS), pp. 1-6. August 6-8, 2018, Upton, NY. IEEE, Piscataway, NJ. DOI: 10.1109/NYSDS.2018.8538951.

Nelson MS and L Pouchard (2017). A pilot “big data” education modular curriculum for engineering graduate education: Development and implementation. In 2017 IEEE Frontiers in Education Conference (FIE), pp. 1-5. October 18-21, 2017, Indianapolis, IN. IEEE, Piscataway, NJ. DOI: 10.1109/FIE.2017.8190688.

Pouchard L, A Malik, H Van Dam, C Xie, W Xu, and K Kleese van Dam (2017). Capturing provenance as a diagnostic tool for workflow performance evaluation and optimization. In Proceedings of the New York Scientific Data Summit (NYSDS). August 6-9, 2017, New York, NY. IEEE, Piscataway, NJ. DOI: 10.1109/NYSDS.2017.8085043.

Foster I, M Ainsworth, B Allen, J Bessac, F Cappello, JY Choi, E Constantinescu, PE Davis, S Di, W Di, H Guo, S Klasky, K Kleese Van Dam, T Kurc, Q Liu, A Malik, K Mehta, K Mueller, T Munson, G Ostouchov, M Parashar, T Peterka, L Pouchard, D Tao, O Tugluk, S Wild, M Wolf, JM Wozniak, W Xu, and S Yoo (2017). Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. In Euro-Par 2017: Parallel Processing, eds., FF Rivera, TF Pena, and JC Cabaleiro, Lecture Notes in Computer Science, vol. 10417, pp. 3-19. 23rd International Conference on Parallel and Distributed Computing (Euro-Par 2017). Aug. 28-Sept. 1, 2017, Santiago de Compostela, Spain. Springer, Cham. DOI: 10.1007/978-3-319-64203-1_1.

Zhang B, LC Pouchard, PM Smith, A Gasc, and BC Pijanowski (2016). Data storage and sharing for the long tail of science. In  Proceedings of the New York Scientific Data Summit (NYSDS), pp. 1-9. August 14-17, 2016, New York, NY. IEEE, Piscataway, NJ. DOI: 10.1109/NYSDS.2016.7747811.

Gopavarapu P, LC Pouchard, and S Pujol (2016). Increasing Datasets Discoverability in an Engineering Data Platform using Keyword Extraction. In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL '16), pp. 225-226. June 19-23, 2016, Newark, NJ. ACM, New York, NY. DOI: 10.1145/2910896.2925443.

Pouchard L (2016). Revisiting the Data Lifecycle with Big Data Curation. International Journal of Digital Curation 10(2):176-192. DOI: 10.2218/ijdc.v10i2.342.

Pouchard L and MS Bracke (2016). An Analysis of Selected Data Practices: A Case. Issues in Science & Technology Librarianship 85:1-15. DOI: 10.5062/F4057CX4.

Tuarob S, LC Pouchard, P Mitra, and CL Giles (2015). A generalized topic modeling approach for automatic document annotation. International Journal on Digital Libraries 16(2):111-128. DOI: 10.1007/s00799-015-0146-2.

Pouchard LC, R Devarakonda, M Branstetter, and N Noy (2015). A Linked Science Investigation: Enhancing Climate Change Data Discovery with Ontologies and Semantic Technologies. Chapter 3 in Studies on the Semantic Web, Vol. 20: The Semantic Web in Earth and Space Science. Current Status and Future Directions, eds. T Narock and P Fox, pp. 33-50. AKA Verlag-IOS Press, Berlin. DOI: 10.3233/978-1-61499-501-2-33.

DiGiuseppe N, LC Pouchard, and NF Noy (2014). SWEET ontology coverage for earth system sciences. Earth Science Informatics 7(4):249-264. DOI: 10.1007/s12145-013-0143-1.

Hernández, JM, L Pouchard, J McDonald, and S Prowell (2013). Developing a power measurement framework for cyber defense. In Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop (CSIIRW '13), Article 28. January 08-10, 2013, Oak Ridge, Tennessee. ACM, New York. DOI: 10.1145/2459976.2460008.

Pouchard L, J Dobson, and S Poole (2010). Collecting Sensor Data for High-Performance Computing: A Case-study. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), pp. 384-389. July 12-15, 2010, Las Vegas, Nevada. Available online.

Bernholdt D, S Bharathi, D Brown, K Chanchio, M Chen, A Chervenak, L Cinquini, B Drach, I Foster, P Fox, J Garcia, C Kesselman, R Markel, D Middleton, V Nefedova, L Pouchard, A Shoshani, A Sim, G Strand, and D Williams (2005). The Earth System Grid: Supporting the Next Generation of Climate Modeling Research. Proceedings of the IEEE 93(3):485-495. DOI: 10.1109/JPROC.2004.842745.