Professor Adam Johansen
Adam Johansen is a Professor of Statistics; his research focuses upon methodological and theoretical aspects of simulation-based algorithms.
He led the project and was an investigator within .
He is an investigator within the .
He was a co-director of from 2012鈥�2023.
Teaching
Some generic teaching information - - applicable to my personal tutees, MSc students and those attending my lectures is available from my teaching page.
Research
Current interests include Monte Carlo methodology, particularly sequential methods together with Bayesian statistics and decision theory more generally.
Information about former students and postdoctoral researchers.
Prospective Ph.D. students should feel free to email me to discuss possible research directions and might find the theses of some of my former students (available by following the above link) useful indicators of the types of project in which I am typically involved.
Current Research Group
Postdoctoral Researchers
- Rocco Caprio (Independent Research Fellow funded by )
- Shishen Lin (part of the )
- Filippo Pagani (part of the )
PhD Students
- El Houssaine Chahboun (EUTOPIA co-tutelle student; co-supervsors Richard Everitt, and )
- Shu Huang (co-supervisors Richard Everitt and Massimiliano Tamborrino)
- Yuhan Jiang (co-supervisor Adrien Corenflos)
- Janique Krasnowska (co-supervisor )
- Usman Ladan (co-supervisors Emma Horton and Andreas Kyprianou
- Benchi Lin
- Federico Perlino (co-supervisor Theo Damoulas)
- Liwen Xue (submitted)
Publications
(Pre)Publications to date are listed here. Selected recent additions are listed below.
- R. Caprio and A. M. Johansen. Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality. Biometrika, 112(4):asaf061, 2025. [|]
- R. Caprio, J. Kuntz, S. Powers, and A. M. Johansen. Error Bounds for Particle Gradient Descent,
and Extensions of the log-Sobolev and Talagrand Inequalities. Journal of Machine Learning Research, 26(103):1鈥�38, 2025. [|] - R. Salomone, L. F. South, A. M. Johansen, C. C. Drovandi, and D. P. Kroese. Unbiased and consistent nested sampling via sequential Monte Carlo. Journal of the Royal Statistical Society Series B (Statistical Methodology), 87(4):1221鈥�1238, 2025. [||]
- J. N. Lim, J. Kuntz, S. Power, and A. M. Johansen. Momentum particle maximum likelihood. In Proceedings of 41st International Conference on Machine Learning (ICML), Vienna, Austria, PMLR 235:29816-29871, 2024. [|
- J. Kuntz, F. R. Crucinio, and A. M. Johansen. Divide-and-conquer Sequential Monte Carlo: Properties and limit theorems. Annals of Applied Probability, 34(1B):1469-1523. 2024. [|]
- R. Chan, M. Pollock, A. M. Johansen, and G. O. Roberts. Divide-and-conquer Monte Carlo Fusion. Journal of Machine Learning Research, 174(193):1-82, 2023. |]
- F. R. Crucinio, A. Doucet, and A. M. Johansen. A particle method for solving Fredholm integral equations of the first kind. Journal of the American Statistical Association, 118:542, 937-947, 2023. [|
- J. Kuntz and J. N. Lim A. M. Johansen. Particle algorithms for maximum likelihood training of latent variable models. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:5134-5180, 2023. (oral presentation). [|]
Editorial and Committee Responsibilities
I am currently:
- Chair of the
Previous roles include:
- Associate Editor for (2021-2024)
- Commissioning Editor for the (2018鈥�2023)
Software
- SMCTC: A Sequential Monte Carlo Template Class (C++)
- ; the lives on github and a google-groups-based also exists.
