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 processes and spatiotemporal data. I research practical problems that can not be analysed using the methods from a typical "Time Series 101" class. That means the data could be nonstationary, anisotropic, fractional or non-Markovian, and multivariate or high-dimensional.
I have an application focus in modelling large-scale global oceanographic data. I also investigate time series obtained from neuroscience and seismology, amongst others.
Please do get in touch if you have overlapping research interests.
I am an EU-funded Marie Curie Research Fellow. I spent the last two years working at NorthWest Research Associates in Seattle, USA - but I have now returned to the UCL Department of Statistical Science, as of April 2016.
My current research focuses primarily on developing stochastic process models and corresponding estimation techniques for large-scale multivariate time series and spatiotemporal data. I have a particular interest in modelling in the frequency domain, and using the Matern process. I have also developed an improved version of the Whittle likelihood, for the efficient parameter estimation of stochastic processes in 1D and 2D (see recent papers below).
I have applied my research to oceanographic data. I am implementing our methods on data from the Global Drifter Program: a large global database of satellite-tracked freely-drifting instruments known as 'drifters'. Our 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, which is the simplest abstraction of the exploration-exploitation tradeoff. I have developed algorithms for how this tradeoff can be tuned on-line in practical problems. I have extended these ideas to multi-player problems, which then brings in ideas from game theory.
Here are slides available to download for a short course I give on "visualising" spectral analysis methods. This is aimed at senior undergraduate or graduate level:
Time Series Analysis, Stochastic Processes, Nonstationarity and Anisotropy, Applications in Oceanography, Decision Theory, Game Theory, Tennis.
Recent publications and preprints
- Sykulski AM, Olhede SC, Lilly JM and Danioux E (2016) Lagrangian time series models for ocean surface drifter trajectories. Journal of the Royal Statistical Society Series C, 65(1), pp. 29-50. Link to paper Link to ArXiv version
- Elipot S, Lumpkin R, Perez RC, Lilly JM, Early JJ and Sykulski AM (2016) A global surface drifter dataset at hourly resolution. Journal of Geophysical Research – Oceans, 121, doi:10.1002/2016JC011716. Link to paper
- Sykulski AM, Olhede SC and Lilly JM (2016) The de-biased Whittle likelihood for second-order stationary stochastic processes. Link to ArXiv version
- Sykulski AM, Olhede SC, Lilly JM and Early JJ (2016) Stochastic modeling and estimation of stationary complex-valued signals. Link to ArXiv version
- Sykulski AM and Percival DB (2016) Exact simulation of noncircular or improper complex-valued stationary Gaussian processes using circulant embedding. Link to ArXiv version
- Lilly JM, Sykulski AM, Early JJ and Olhede SC (2016) Fractional Brownian motion, the Matern process, and stochastic modeling of turbulent dispersion. Link to ArXiv version
- Guillaumin AP, Sykulski AM, Olhede SC, Early JJ and Lilly JM (2016) Analysis of nonstationary modulated time series with applications to oceanographic flow measurements. Link to ArXiv version
- Bartlett TE, Sykulski AM, Olhede SC, Lilly JM, Early JJ (2015) A power variance test for nonstationarity in complex-valued signals. 14th International Conference on Machine Learning and Applications, 911-916. Link to paper Link to ArXiv version
- Sykulski AM, Olhede SC and Lilly JM (2015) A widely linear improper complex autoregressive process of order one. Link to ArXiv version