JACOB Pierre

Pierre E Jacob is a Full Professor at ESSEC Business School, in the Information Systems, Decision Sciences and Statistics (IDS) Department. His interests include time series and prediction, Monte Carlo methods, Markov chains, Bayesian statistics, and probabilistic modelling. 

• 2012 : Doctorate, Other, Mathematics (Université Paris-Dauphine, PSL University, France)
• 2009 : Diplôme de statisticien economiste (L'École nationale de la statistique et de l'administration économique (ENSAE), France)

Full-time academic appointments

• 2021 - Present : Professor (ESSEC Business School, France)
• 2019 - 2021 : Associate Professor (Harvard University, United States of America)
• 2015 - 2019 : Assistant Professor (Harvard University, United States of America)
• 2013 - 2015 : Post-Doctorate (University of Oxford, United Kingdom)
• 2012 - 2013 : Post-Doctorate (National University of Singapore, Singapore)

• 2019 - 2022 : DMS 1844695 (CAREER) (National Science Foundation, United States of America)
• 2019 : Dean’s Competitive Fund for Promising Scholarship (Harvard University, United States of America)
• 2017 - 2020 : DMS 1712872 (National Science Foundation, United States of America)
• 2016 : Milton Fund (Harvard University, United States of America)

• 2022 - 2025 : Committee of Presidents of Statistical Societies Leadership Academy Award (United States of America)
• 2022 : Susie Bayarri Lecture
• 2021 : Royal Statistical Society Guy Medal in Bronze (United Kingdom)
• 2018 : David Pickard Award for Teaching and Mentoring (Harvard University, United States of America)
• 2013 : Prix de thèse de la Fondation Dauphine
• 2012 : Prix de thèse Jacques Neveu

• KISHORE, N., TAYLOR, A.R., JACOB, P., VEMBAR, N., COHEN, T., BUCKEE, C.O. and MENZIES, N.A. (2022). Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. The Lancet Digital Health, 4(1), pp. e27-e36.
• BISWAS, N., BHATTACHARYA, A., JACOB, P. and JOHNDROW, J. (2022). Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors. Journal of the Royal Statistical Society: Series B (Statistical Methodology), In press.
• DAI, C., HENG, J., JACOB, P. and WHITELEY, N. (2022). An invitation to sequential Monte Carlo samplers. Journal of the American Statistical Association, 117(539), pp. 1587–1600.
• JACOB, P., GONG, R., EDLEFSEN, P.T. and DEMPSTER, A.P. (2021). A Gibbs Sampler for a Class of Random Convex Polytopes. Journal of the American Statistical Association, 116(535), pp. 1181-1192.
• BUCHHOLZ, A., CHOPIN, N. and JACOB, P. (2021). Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo. Bayesian Analysis, 16(3), pp. 745-777.
• JACOB, P., O’LEARY, J. and ATCHADÉ, Y.F. (2020). Unbiased Markov chain Monte Carlo methods with couplings. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(3), pp. 543-600.
• JACOB, P., LINDSTEN, F. and SCHÖN, T.B. (2020). Smoothing With Couplings of Conditional Particle Filters. Journal of the American Statistical Association, 115(530), pp. 721-729.
• MIDDLETON, L., DELIGIANNIDIS, G., DOUCET, A. and JACOB, P. (2020). Unbiased Markov chain Monte Carlo for intractable target distributions. The Electronic Journal of Statistics, 14(2), pp. 2842-2891.
• HENG, J. and JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. Biometrika, 106(2), pp. 287-302.
• BERNTON, E., JACOB, P., GERBER, M. and ROBERT, C.P. (2019). Approximate Bayesian computation with the Wasserstein distance. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(2), pp. 235-269.
• SHAO, S., JACOB, P., DING, J. and TAROKH, V. (2019). Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency. Journal of the American Statistical Association, 114(528), pp. 1826-1837.
• TAYLOR, A.R., JACOB, P., NEAFSEY, D.E. and BUCKEE, C.O. (2019). Estimating Relatedness Between Malaria Parasites. Genetics, 212(4), pp. 1337-1351.
• BERNTON, E., JACOB, P., GERBER, M. and ROBERT, C.P. (2019). On parameter estimation with the Wasserstein distance. Information and Inference: A Journal of the IMA, 8(4), pp. 657-676.
• JACOB, P., ALAVI, S.M.M., MAHDI, A., PAYNE, S.J. and HOWEY, D.A. (2018). Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems. IEEE Transactions on Control Systems Technology, 26(2), pp. 497-506.
• MURRAY, L.M., LEE, A. and JACOB, P. (2016). Parallel Resampling in the Particle Filter. Journal of Computational and Graphical Statistics, 25(3), pp. 789-805.
• JACOB, P., MURRAY, L.M. and RUBENTHALER, S. (2015). Path storage in the particle filter. Statistics and Computing, 25(2), pp. 487-496.
• JACOB, P. and THIERY, A.H. (2015). On nonnegative unbiased estimators. Annals of Statistics, 43(2).
• JACOB, P. and RYDER, R.J. (2014). The Wang–Landau algorithm reaches the flat histogram criterion in finite time. Annals of Applied Probability, 24(1), pp. 34-53.
• CHOPIN, N., JACOB, P. and PAPASPILIOPOULOS, O. (2013). SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(3), pp. 397-426.

• JACOB, P., WANG, G. and O'LEARY, J. (2021). Maximal Couplings of the Metropolis-Hastings Algorithm. In: The 24th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 1225-1233.
• NADJAHI, K., DURMUS, A., JACOB, P., BADEAU, R. and SIMSEKLI, U. (2021). Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. In: NeurIPS 2021 Thirty-fifth Conference on Neural Information Processing Systems. Proceedings of Machine Learning Research.
• JACOB, P., LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. and BA, D. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. In: The 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 2476-2484.
• JACOB, P., BISWAS, N. and VANETTI, P. (2019). Estimating Convergence of Markov chains with L-Lag Couplings. In: Advances in Neural Information Processing Systems. Curran Associates, Inc.
• JACOB, P., MIDDLETON, L., DELIGIANNIDIS, G. and DOUCET, A. (2019). Unbiased Smoothing using Particle Independent Metropolis-Hastings. In: The 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 2378-2387.

• JACOB, P., DOUC, R., LEE, A. and VATS, D. (2023). Poisson Equation and Coupled Markov Chains. In: 2023 Bayes4Health & CoSInES Workshop. Oxford.
• DOUC, R., JACOB, P., LEE, A. and VATS, D. (2022). Solving the Poisson equation using coupled Markov chains. In: Computational Methods for Unifying Multiple Statistical Analyses (Fusion). Luminy.

• JACOB, P. (2022). Bayesian Inference in Models Made of Modules. In: 2022 International Society for Bayesian Analysis (ISBA) World Meeting. Montreal.

• 2022 - Present : Business Statistics & Introduction to Analytics (Grande Ecole - Master in Management ESSEC Business School France)
• 2022 - Present : Forecasting & Predictive Analytics (Grande Ecole - Master in Management ESSEC Business School France)
• 2021 - Present : Forecasting and predictive analytics (Master in Data science and Business analytics ESSEC Business School France)
• 2021 STAT248 Couplings & Monte Carlo ( Harvard University United States of America)
• 2016 - 2018 : STAT131 Time Series & Prediction ( Harvard University United States of America)
• 2015 - 2019 : STAT213 Statistical Inference II ( Harvard University United States of America)
• 2013 - 2015 : Advanced Simulation ( University of Oxford United Kingdom)