HENG Jeremy
Department : Information Systems, Decision Sciences and Statistics
Assistant Professor
Campus de Singapour
Contact
- email : heng@essec.edu
- tél : +65 6413 9753
Personal links
Diplomas
- 2017 : PhD in Statistics (University of Oxford, United Kingdom)
- 2012 : BSc in Statistics (University College London, United Kingdom)
Career
- 2019 - Present : Assistant Professor (ESSEC Business School, Singapore)
- 2017 - 2019 : Postdoctoral Fellow (Harvard University, United States of America)
Full-time academic appointments
Other Academic Appointments
Journal 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.
Conference Proceedings
- 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.
