Julie Bessac

Professional service

• I am program director for the SIAM Activity Group of the Mathematics of Planet Earth for 2023-2024

• I am a SIAM science policy fellow for 2023-2024

Research

Research interests

• Techniques: Spatiotemporal and multivariate statistical modeling, Probabilistic forecast evaluation, Extremes, State-space models, Clustering, Deep learning

• Applications: Weather and climate, Data compression and reduction, Energy systems, Nuclear physics

Ph.D. thesis

• On the construction of stochastic generators of wind conditions off-shore Brittany,
  Universite de Rennes 1, France, 2014, available on TEL
  

Articles and preprints

• Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification
  Daniel Getter, Julie Bessac, Johann Rudi, Yan Feng
  Submitted, 2023, arXiv.

• Neural networks for parameter estimation in intractable models
  Amanda Lenzi, Julie Bessac, Johann Rudi and Michael L. Stein
  Submitted, 2022, arXiv.

• Robustness of the stochastic parameterization of sub-grid scale wind variability in sea-surface fluxes
  Kota Endo, Adam H. Monahan, Julie Bessac, Hannah M. Christensen and Nils Weitzel
  Revisions, Monthly Weather Review, 2023.

• Station-wise statistical joint assessment of wind speed and direction under future climates across the United States
  Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang and Rao Kotamarthi
  Advances in Statistical Climatology, Meteorology and Oceanography 2022, available here.

• Projected U.S. drought extremes through the 21st century with vapor pressure deficit
  Brandi Gamelin, Jeremy Feinstein, Jiali Wang, Julie Bessac, Eugene Yan, and Veerabhadra R. Kotamarthi
  Scientific Reports, 2022.

• Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
  Mitchell Krock, Julie Bessac, Michael L. Stein and Adam H. Monahan
  Weather and Climate Extremes, 2022. arXiv.

• Detecting large frequency excursions in the power grid with Bayesian decision theory
  Amanda Lenzi, Julie Bessac, and Mihai Anitescu
  IEEE Open Access Journal of Power and Energy, 2022.

• Forecast score distributions with imperfect observations
  Julie Bessac and Philippe Naveau
  Advances in Statistical Climatology, Meteorology and Oceanography, 2021, available here.

• Online data analysis and reduction: an important co-design motif for extreme-scale computers
  Ian Foster, Mark Ainsworth, Julie Bessac, Franck Cappello, Jong Choi, Sheng Di, Zichao Di, Ali M Gok, Hanqi Guo, Kevin A Huck, Christopher Kelly, Scott Klasky, Kerstin Kleese van Dam, Xin Liang, Kshitij Mehta, Manish Parashar, Tom Peterka, Line Pouchard, Tong Shu, Ozan Tugluk, Hubertus van Dam, Lipeng Wan, Matthew Wolf, Justin M Wozniak, Wei Xu, Igor Yakushin, Shinjae Yoo, Todd Munson.
  The International Journal of High Performance Computing Applications, 2021.

• Power grid frequency prediction using spatio-temporal modeling
  Amanda Lenzi, Julie Bessac, and Mihai Anitescu
  Journal of Statistical Analysis and Data Mining, 2021.

• Scale-aware spacetime stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes
  Julie Bessac, Hannah M. Christensen, Kota Endo, Adam H. Monahan, and Nils Weitzel
  Journal of Advances in Modeling Earth Systems, 13, e2020MS002367, 2021, available here.

• Statistical treatment of inverse problems constrained by differential equations-based models with stochastic terms
  Emil M. Constantinescu, Noemi Petra, Julie Bessac and Cosmin G. Petra
  SIAM/ASA Journal of Uncertainty Quantification 8 (1), 170-197, 2020, arXiv.

• Stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes
  Julie Bessac, Adam Monahan, Hannah Christensen and Nils Weitzel
  Monthly Weather Review, 147 (5), 1447-1469, 2019, available here.

• Global sensitivity analysis for statistical model parameters
  Jospeh Hart, Julie Bessac and Emil M. Constantinescu
  SIAM/ASA Journal of Uncertainty Quantification, 7(1), 67-92, 2019, arXiv.

• Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C
  Kshitij Kulshreshtha, Sri Hari Krishna Narayanan, Julie Bessac and Kaitlyn MacIntyre
  Optimization Methods and Software, 33(4-6), 1173-1191, 2018.

• Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models,
  Julie Bessac, Emil M. Constantinescu and Mihai Anitescu
  The Annals of Applied Statistics, 12(1), 432-458, 2018, arXiv.

• Internal variability of a dynamically downscaled climate over North America
  Jiali Wang, Rao Kotamarthi, Julie Bessac, Emil M. Constantinescu and Beth Drewniak
  Climate Dynamics, 50(11-12), 4539-4559, 2018.

• Comparison of hidden and observed regime-switching AutoRegressive models for (u,v)-components of wind fields in the Northeast Atlantic,
  Julie Bessac, Pierre Ailliot, Julien Cattiaux and Valerie Monbet
  Advances in Statistical Climatology, Meteorology and Oceanography, 2(1), 1-16, 2016, available here.

• Non-homogeneous hidden Markov-switching models for wind time series,
  Pierre Ailliot, Julie Bessac, Valerie Monbet and Francoise Pene
  Journal of Statistical Planning and Inference, 160, 75-88, 2015, available here .

• Gaussian linear state-space model for wind fields in the North-East Atlantic,
  Julie Bessac, Pierre Ailliot and Valerie Monbet
  Environmetrics, 26(1), 29-38, 2015, main document, supplementary materials.

Proceedings

• Understanding the effects of modern compressors on the Community Earth Science Model
  Robert Underwood, Julie Bessac, , Sheng Di and Franck Cappello
  8th International Workshop on Data Analysis and Reduction for Big Scientific Data in conjunction with SC '22: The International Conference for High Performance Computing, Networking, Storage and Analysis, 2022.

• Exploring lossy compressibility through statistical correlations of scientific datasets
  David Krasowska, Julie Bessac, Robert Underwood, Sheng Di, Jon Calhoun, and Franck Cappello
  7th International Workshop on Data Analysis and Reduction for Big Scientific Data in conjunction with SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis, 2021. arXiv.

• Parameter estimation with dense and convolutional neural networks applied to the FitzHugh-Nagumo ODE
  Johann Rudi, Julie Bessac, and Amanda Lenzi
  Proceedings of Machine Learning Research: Mathematical and Scientific Machine Learning, 2021, arXiv.

• SDRBench: scientific data reduction benchmark for lossy compressors
  Kai Zhao, Sheng Di, Xin Liang, Sihuan Li, Dingwen Tao, Julie Bessac, Zizhong Chen, and Franck Cappello
  The International Workshop on Big Data Reduction held within the 2020 IEEE International Conference on Big Data, 2020.

• Image segmentation for dust detection using semi-supervised machine learning
  Manzhu Yu, Julie Bessac, Ling Xu, Aryya Gangopadhyay, Yingxi Shi, and Jianwu Wang
  IEEE International Conference on Big Data, 2020.

• Computing just what you need: online data analysis and reduction at extreme scales,
  I. Foster, M. Ainsworth, B. Allen, J. Bessac, F. Cappello, J. Y. Choi, Jong Youl, E. Constantinescu, P. E. Davis, S. Di, W. Di, Wendy, 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, J. M. Wozniak, W. Xu and S. Yoo
  European Conference on Parallel Processing, 2017.

Background

• 2022--... Computational Statistician at the Mathematics and Computer Science division at Argonne National Laboratory, Lemont, IL, USA.

• 2017--2022 Assistant Computational Statistician at the Mathematics and Computer Science division at Argonne National Laboratory, Lemont, IL, USA.

• 2014--2017 Post-doctoral appointment at the Mathematics and Computer Science division at Argonne National Laboratory, Lemont, IL, USA, under the supervision of Mihai Anitescu and Emil Constantinescu

• 2011--2014 PhD in Applied Mathematics, University of Rennes 1, Rennes, France, under the supervision of Valerie Monbet and Pierre Ailliot

• 2010--2011 Master in Probability and Statistics, University of Rennes 1 and Ecole Normale Superieure de Cachan, Antenne de Bretagne, Rennes, France.

• 2009--2010 Agregation de Mathematiques, University of Rennes 1 and Ecole Normale Superieure de Cachan, Antenne de Bretagne, Rennes, France.

• 2005--2009 Graduate (Bachelor and Master) in Mathematics, Magistere de Mathematiques , University of Rennes and Ecole Normale Superieure de Cachan, Antenne de Bretagne, Rennes, France.

Teaching

Sept. 2018: The University of Chicago

• Bootcamp "Introduction to the Statistics of Spatial Data"

2012-2014: National School of Statistics and Analysis of Information (ENSAI), Rennes

• Tutoring on: statistical tests ; measure theory and probability ; Markov chains

2011-2012: Faculty of Economics, Universite de Rennes 1, Rennes

• Tutoring on: data analysis ; inferential statistics ; Microsoft Office softwares

Activities

• 2020--... Directorate representative at the Argonne Action Collaborative on Preventing Sexual Harassment in Higher Education, an initiative of the National Academies of Sciences, Engineering, and Medicine.

• 2020: Participation to Cybertraining on Big Data + Atmospheric Sciences + Machine Learning by UMBC

Links

• Workshop on Statistics Topics in Multiscale, Forecast Verification, and Data Assimilation, June 2021.

• R-package for Non-Homogeneous Markov Switching Autoregressive Models, created by Valerie Monbet