Dr Adam M Sykulski
|Themes||Multivariate and High Dimensional Data, Stochastic Modelling and Time Series|
I am a statistician specialising in time series analysis, stochastic process and spatiotemporal data. I am particularly interested in processes that are nonstationary, locally stationary, anisotropic and multivariate/high-dimensional. I have an application focus in modelling oceanographic time series obtained from floats and drifters. Please do get in touch if you have overlapping research interests.
I am currently an EU-funded Marie Curie Research Fellow. I am based at NorthWest Research Associates in Seattle, USA - but am collaborating closely with the UCL Department of Statistical Science, where I return for the final year of my fellowship in April 2016.
My research focuses primarily on developing semi-parametric modelling and estimation techniques for multivariate time series. I have a particular interest in frequency-domain modelling, and estimation of parameters via the Whittle Likelihood (see recent papers below).
I have applied my research to oceanographic data. In particular, I am implementing our methods on data from the Global Drifter Program: a large database of worldwide satellite-tracked freely-drifting instruments known as 'drifters'. Our new techniques allow us to make insightful new findings which improves global climate modelling and our ability to respond to environmental threats such as oil spills.
I also have an active research interest (from my PhD) in decision theory problems related to the multi-armed bandit problem. In particular, I am interested in on-line tuning of the exploration-exploitation tradeoff and also extensions to multi-player problems which bring in ideas from game theory.
Time Series Analysis, Spatiotemporal Processes, Nonstationarity, Anisotropy, Applications in Oceanography, Decision Theory, Game Theory, Tennis.
Recent publications and preprints
- Sykulski, AM, Olhede, SC, Lilly, JM and Danioux, E (2015) Lagrangian Time Series Models for Ocean Surface Drifter Trajectories. Journal of the Royal Statistical Society Series C (DOI: 10.1111/rssc.12112). Link to paper Link to ArXiv version
- Sykulski, AM, Olhede, SC, Lilly, JM and Early, JJ (2015) On Parametric Modelling and Inference for Complex-Valued Time Series. Link to ArXiv version