Line Pouchard

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-120
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4626
pouchard@bnl.gov

Preferred Gender Pronouns (PGPs): she

Expertise | Research | Education | Appointments | Publications | Awards

Expertise

• Reproducibility for computational and data-intensive science
• Research Data Management for HPC and AI/ML driven discovery
• Provenance and FAIR principles for HPC and ML

Research Activities

• Lead scientific and technical teams in computational science research projects - DOE ASCR Principal Investigator (RECUP: Scalable Metadata and Provenance Services for Reproducible Hybrid Workflows, 2023-26)
• Create new approaches for data management and discovery, including at DOE Scientific User Facilities
• Plans and conducts original research focused on reproducibility and provenance
• Co-chairs and conducts DOE workshop sessions and coordinates reports (BER, ASCR)

Education

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

Professional Appointments

• 2023-present, Senior Computational Scientist, Center for Data-Driven Discovery, Computational Science Initiative
• 2022-2023, Chair, Diversity, Equity, Inclusion & Accessibility Council, Computational Science Initiative,  BNL
• 2017-2022, Senior Researcher, 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

• Nicolae B, Islam TZ, Ross R, et al (2023) Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP. 2023 IEEE 19th International Conference on e-Science (e-Science). https://doi.org/10.1109/e-science58273.2023.10254808
• Pouchard L, Reyes KG, Alexander FJ, Yoon B-J (2023) A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digital Discovery. https://doi.org/10.1039/d3dd00094j
• Pouchard L, Van Dam H, Verdooren E (2023) Reproducing the predictions of a super-resolution adversarial model: a case study. Zenodo. https://doi.org/10.5281/ZENODO.10565182
• Pouchard LC (2023) FAIR Enabling Re-Use of Data-Intensive Workflows and Scientific Reproducibility. Companion of the 2023 ACM/SPEC International Conference on Performance Engineering. https://doi.org/10.1145/3578245.3586012
• Guo L, Lofstead J, Ren J, et al (2023) Understanding System Resilience for Converged Computing of Cloud, Edge, and HPC. High Performance Computing 221–233. https://doi.org/10.1007/978-3-031-40843-4_17
• Da Silva RF, Badia RM, Bala V, et al (2023) Workflows Community Summit 2022: A Roadmap Revolution. Zenodo
• Abhinit I, Adams EK, Alam K, et al (2022) Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows. 2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS). https://doi.org/10.1109/works56498.2022.00016
• Pouchard L, Islam T, Nicolae B (2022) Challenges for Implementing FAIR Digital Objects with High Performance Workflows. Research Ideas and Outcomes 8:. https://doi.org/10.3897/rio.8.e94835
• Hickmon N, Varadharajan C, Hoffman F, et al (2022) Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop Report. https://doi.org/10.2172/1888810. Chapter on Explainability.
• Park G, Rayz J, Pouchard L (2022) Figure Descriptive Text Extraction using Ontological Representation
• Plale BA, Malik T, Pouchard LC (2021) Reproducibility Practice in High-Performance Computing: Community Survey Results. Computing in Science & Engineering 23:55–60. https://doi.org/10.1109/mcse.2021.3096678
• Park G, Pouchard L (2021) Advances in scientific literature mining for interpreting materials characterization. Machine Learning: Science and Technology 2:045007. doi: 10.1088/2632-2153/abf751
• Pouchard L, Soto C, Branstetter M, Prakash G (2021) FAIR data infrastructure and tools for AI-assisted streamflow prediction. https://doi.org/10.2172/1769710
• Foster I, Ainsworth M, Bessac J, et al (2021) Online data analysis and reduction: An important Co-design motif for extreme-scale computers. The International Journal of High Performance Computing Applications 35:617–635. https://doi.org/10.1177/10943420211023549
• Serbin S, Giangrande S, Kuang C, et al (2021) AI to Automate ModEx for Optimal Predictive Improvement and Scientific Discovery. https://doi.org/10.2172/1769662
• 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.
• Acharya A, Agarwal R, Baker MB, et al (2020) Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. Journal of Chemical Information and Modeling 60:5832–5852. https://doi.org/10.1021/acs.jcim.0c01010
• Kelly C, Ha S, Huck K, Van Dam H, Pouchard L, Matyasfalvi G, Tang L, D'Imperio N, Xu W, Yoo S, Van Dam KK (2020) Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool. ISAV'20 In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization. doi: 10.1145/3426462.3426465
• Peterka T, Bard D, Bennett JC, Bethel EW, Oldfield RA, Pouchard L, Sweeney C, Wolf M (2020) Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources. The International Journal of High Performance Computing Applications 34:409–427. doi: 10.1177/1094342020913628
• Pouchard L, Juhas P, Park G, et al (2020) Provenance Infrastructure for Multi-modal X-ray Experiments and Reproducible Analysis. Handbook on Big Data and Machine Learning in the Physical Sciences 307–331. https://doi.org/10.1142/9789811204579_0015
• Pouchard L, Kleese van Dam K, Campbell SI (2019) Experimental Data Curation at Large Instrument Facilities with Open Source Software. International Journal of Digital Curation 14:114–125. doi: 10.2218/ijdc.v14i1.637
• Pouchard L, Baldwin S, Elsethagen T, Jha S, Raju B, Stephan E, Tang L, Van Dam KK (2019) Computational reproducibility of scientific workflows at extreme scales. The International Journal of High Performance Computing Applications 33:763–776. doi: 10.1177/1094342019839124
• Park G, Pouchard L (2019) Scientific Literature Mining for Experiment Information in Materials Design. 2019 New York Scientific Data Summit (NYSDS). https://doi.org/10.1109/nysds.2019.8909726
• Pouchard L, Huck K, Matyasfalvi G, et al (2018) Prescriptive provenance for streaming analysis of workflows at scale. 2018 New York Scientific Data Summit (NYSDS). https://doi.org/10.1109/nysds.2018.8538951
• Foster I, Ainsworth M, Allen B, Bessac J, Cappello F, Choi JY, Constantinescu E, Davis PE, Di S, Di W, Guo H, Klasky S, Van Dam KK, Kurc T, Liu Q, Malik A, Mehta K, Mueller K, Munson T, Ostouchov G, Parashar M, Peterka T, Pouchard L, Tao D, Tugluk O, Wild S, Wolf M, Wozniak JM, Xu W, Yoo S (2017) Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. Euro-Par 2017: Parallel Processing 3–19. doi: 10.1007/978-3-319-64203-1_1
• Zhang B, Pouchard LC, Smith PM, Gasc A, Pijanowski BC (2016) Data storage and sharing for the long tail of science. 2016 New York Scientific Data Summit (NYSDS). doi: 10.1109/nysds.2016.7747811
• Gopavarapu P, Pouchard LC, Pujol S (2016) Increasing Datasets Discoverability in an Engineering Data Platform using Keyword Extraction. Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. doi: 10.1145/2910896.2925443
• Pouchard L (2016) Revisiting the Data Lifecycle with Big Data Curation. International Journal of Digital Curation 10:176–192. doi: 10.2218/ijdc.v10i2.342
• Tuarob S, Pouchard LC, Mitra P, Giles CL (2015) A generalized topic modeling approach for automatic document annotation. International Journal on Digital Libraries 16:111–128. doi: 10.1007/s00799-015-0146-2
• Hernández JM, Pouchard L, McDonald J, Prowell S (2013) Developing a power measurement framework for cyber defense. Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop on - CSIIRW '13. doi: 10.1145/2459976.2460008

Awards & Recognition

Outstanding Mentor Award, DOE