JACOB Pierre
Département : Systèmes d’Information, Sciences de la Décision et Statistiques
Professeur
Campus de Cergy
Contact
- email : pierre.jacob@essec.edu
Diplômes
- 2012 : Doctorat, Autre, Mathématiques (Université Paris-Dauphine, PSL University, France)
- 2009 : (L'École nationale de la statistique et de l'administration économique (ENSAE), France)
Carrière
- 2021 - présent : Professeur (ESSEC Business School, France)
- 2019 - 2021 : Professeur associé (Harvard University, États-Unis)
- 2015 - 2019 : Professeur assistant (Harvard University, États-Unis)
- 2013 - 2015 : Post-Doctorant (University of Oxford, Royaume-Uni)
- 2012 - 2013 : Post-Doctorant (National University of Singapore, Singapour)
Positions académiques principales
Bourses
- 2019 - 2022 : DMS 1844695 (CAREER) (National Science Foundation, États-Unis)
- 2019 : Dean’s Competitive Fund for Promising Scholarship (Harvard University, États-Unis)
- 2017 - 2020 : DMS 1712872 (National Science Foundation, États-Unis)
- 2016 : Milton Fund (Harvard University, États-Unis)
Prix
- 2022 - 2025 : Committee of Presidents of Statistical Societies Leadership Academy Award (États-Unis)
- 2022 : Susie Bayarri Lecture
- 2021 : Royal Statistical Society Guy Medal in Bronze (Royaume-Uni)
- 2018 : David Pickard Award for Teaching and Mentoring (Harvard University, États-Unis)
- 2013 : Prix de thèse de la Fondation Dauphine
- 2012 : Prix de thèse Jacques Neveu
Articles
- 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.
Actes d'une conférence
- 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.
Communications dans une conférence
- 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.
Invité dans une conférence académique (Keynote speaker)
Enseignement
- 2022 - présent : Business Statistics & Introduction to Analytics (Grande Ecole - Master in Management ESSEC Business School France)
- 2022 - présent : Forecasting & Predictive Analytics (Grande Ecole - Master in Management ESSEC Business School France)
- 2021 - présent : Forecasting and predictive analytics (Master in Data science and Business analytics ESSEC Business School France)
- 2021 STAT248 Couplings & Monte Carlo ( Harvard University États-Unis)
- 2016 - 2018 : STAT131 Time Series & Prediction ( Harvard University États-Unis)
- 2015 - 2019 : STAT213 Statistical Inference II ( Harvard University États-Unis)
- 2013 - 2015 : Advanced Simulation ( University of Oxford Royaume-Uni)
Activités professionnelles
- 2022 : - présent : Research Section Committee of the Royal Statistical Society, Royaume-Uni
Membre d'une association professionnelle, d'un groupe d'experts ou d'un conseil d'administration
Thèses
- 2021 : O'LEARY J. (Harvard University), Directeur de thèse
- 2021 : JU N. (Harvard University), Directeur de thèse
- 2019 : SHAO S. (Harvard University), Directeur de thèse
- 2019 : BERNTON E. (Harvard University), Directeur de thèse
