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 Robust, Scalable Sequential Monte Carlo with Application To Urban Air Quality project and was an investigator within The CoSinES Project.
He is an investigator within the OCEAN Project.
He was a co-director of APTS from 2012–2023.
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 Prob-AI)
- Adrien Corenflos (part of the OCEAN Project)
- Filippo Pagani (part of the OCEAN Project)
PhD Students
- Rocco Caprio (co-supervisor Andi Wang; submitted)
- Shu Huang (co-supervisors Richard Everitt and Massimiliano Tamborrino)
- Janique Krasnowska (co-supervisor Paul Jenkins)
- Usman Ladan (co-supervisors Emma Horton and Andreas Kyprianou)
- Jen Ning Lim
- Federico Perlino (co-supervisor Theo Damoulas)
- Liwen Xue
Research Interns
- Killian Flossman (URSS; co-supervisor Theo Damoulas)
- Mithusan Paramanantham (URSS)
- Max Richards (URSS)
- Linh Tra Vu (Summer Research Experience)
Publications
(Pre)Publications to date are listed here. Selected recent additions are listed below.
- 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):1–38, 2025. [journal|arxiv] - 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), in press, 2025. [journal|arxiv|code]
- 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, July 2024. In press. [arxiv]
- 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. [journal|arxiv]
- 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. [journal|arxiv]
- 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. [journal|arxiv]
- 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). [proceedings|arxiv]
Editorial and Committee Responsibilities
I am currently:
Previous roles include:
- Associate Editor for Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2021-2024)
- Commissioning Editor for the Newsletter of the London Mathematical Society (2018–2023)
Software
- SMCTC: A Sequential Monte Carlo Template Class (C++)
- RcppSMC: An Rcpp library which has evolved from the above (currently version 0.2.6) ; the development version of RcppSMC lives on github and a google-groups-based discussion list also exists.
Adam M. Johansen
MSB 2.18
tel: 024761- 50919
email: a.m.johansen@warwick.ac.uk
bsky: adamjohansen.bsky.social