HENG Jeremy
Département : Systèmes d’Information, Sciences de la Décision et Statistiques
Professeur assistant
Campus de Singapour
Contact
- email : heng@essec.edu
- tél : +65 6413 9753
Diplômes
- 2017 : PhD en Statistiques (University of Oxford, Royaume-Uni)
- 2012 : BSc en Statistiques (University College London, Royaume-Uni)
Carrière
- 2019 - présent : Professeur assistant (ESSEC Business School, Singapour)
- 2017 - 2019 : Postdoctoral Fellow (Harvard University, États-Unis)
Positions académiques principales
Autres positions académiques
Articles
- FULOP, A., HENG, J., LI, J. and LIU, H. (2021). Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo. Journal of Econometrics, In press.
- HENG, J., DOUCET, A. and POKERN, Y. (2021). Gibbs flow for approximate transport with applications to Bayesian computation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), In press.
- JASRA, A., YU, F. and HENG, J. (2020). Multilevel Particle Filters for the Non-Linear Filtering Problem in Continuous Time. Statistics and Computing, 30, pp. 1381-1402.
- JASRA, A., HENG, J., XU, Y. and BISHOP, A.N. (2020). A Multilevel Approach for Stochastic Nonlinear Optimal Control. International Journal of Control, In press.
- HENG, J. and JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. Biometrika, 106(2), pp. 287-302.
- HENG, J., BISHOP, A.N., DELIGIANNIDIS, G. and DOUCET, A. (2019). Controlled Sequential Monte Carlo. Annals of Statistics, 48(5), pp. 2904-2929.
Actes d'une conférence
- LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. and JACOB, P.E. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. In: 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.
