Dr Adam Johansen is a Reader in Statistics; his research focuses upon methodological and theoretical aspects of simulation-based algorithms.
He is a group leader within the Data Centric Engineering Programme of The Alan Turing Institute.
He is co-director of APTS.
Some documents relating to the current triennial valuation of the USS may be of interest.
Some generic teaching information - - applicable to my personal tutees, MSc students and those attending my lectures is available from my teaching page. This year I will be lecturing the following locally:
- One of the ST343 Topics in Data Science -- Modelling the Written Word;
- ST407 Monte Carlo Methods; and
- the OxWaSP Stochastic Simulation module, with Arnaud Doucet
- I will be lecturing the APTS Computer Intensive Statistics module again in 2018, this year together with Paul Jenkins.
- I will be talking about particle filters at the CIRM Masterclass on Bayesian Statistics in October 2018.
Students and Collaborators
Current interests include Monte Carlo methodology, particularly sequential methods together with Bayesian statistics and decision theory more generally.
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 Ph.D. Students:
- Letizia Angeli (co-supevisor Stefan Grosskinsky)
- Lewis Rendell (co-supervisor Anthony Lee)
- Denishrouf Thesingarajah
- Måns Unosson (co-supervisor Bärbel Finkenstädt )
(Pre)Publications to date are listed here. Selected recent additions are listed below.
- P. Guarniero, A. M. Johansen and A. Lee. The Iterated Auxiliary Particle Filter. Journal of the American Statistical Association 112(520):1636–1647, 2017 [journal|arxiv]
- F. Lindsten, A. M. Johansen, C. Naesseth, B. Kirkpatrick, T. Schön, J. A. D. Aston, and A. Bouchard-Côté. Divide and conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics 26(2):445–458, 2017. [journal website|arxiv]
- Y. Zhou, A. M. Johansen and J. A. D. Aston, Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach. Journal of Computational and Graphical Statistics, 25(3):701–726, 2016. [journal|arxiv]
- M. Pollock, A. M. Johansen and G. O. Roberts, On Exact and -strong Simulation of (Jump) Diffusions. Bernoulli, 22(2):794–856, 2016. [pdf|journal website|arxiv].
- SMCTC: A Sequential Monte Carlo Template Class (C++)
- RcppSMC: An Rcpp library which has evolved from the above (currently version 0.2) ; the development version of RcppSMC lives on github and a google-groups-based discussion list also exists.
Adam M. Johansen
C0.20 Zeeman Building
a dot m dot johansen at warwick.ac.uk