Summary
Adam's research is in spatiotemporal statistics and time series, with an application focus in oceanography and environmental sciences. Adam has supervised 6 PhD students to completion, 3 Post-Doctoral researchers and 7 Master’s projects. Adam has published 27 peer-reviewed papers in leading statistics journals such as Biometrika, IEEE Transactions on Signal Processing, and the Journal of the Royal Statistical Society (Series B and Series C), and leading application journals such as the Journal of Geophysical Research, Scientific Data - Nature, and the European Journal of Operational Research. Adam has obtained external funding on numerous projects as PI and Co-I (please see CV) and is the current Discussion Papers Editor for the Royal Statistical Society.
Current Positions and Appointments
Discussion Papers Editor and Discussion Meetings Secretary, Journals of the Royal Statistical Society
Environmental Statistics Section Committee Member, Royal Statistical Society
External Examiner, Department of Mathematical Sciences, University of Liverpool
Ocean Uncertainty Quantification Working Group Member, US Climate Variability and Predictability Program
Partner Investigator, Transforming energy Infrastructure through Digital Engineering (TIDE), Australian Research Council
PhD Student Supervision
Dr Arthur Guillaumin (graduated 2017)
Dr Nicola Rennie (graduated 2021)
Dr Michael O'Malley (graduated 2022)
Dr Sarah Oscroft (graduated 2022)
Dr Keerati Suibkitwanchai (graduated 2022)
Dr Jake Grainger (graduated 2022)
Maddie Smith (started in 2021)
I am currently accepting applications for new PhD student supervisions at Imperial College, please email me with a detailed CV if interested.
Selected Publications
Journal Articles
Grainger JP, Sykulski AM, Ewans K, et al., A multivariate pseudo-likelihood approach to estimating directional ocean wave models, Journal of the Royal Statistical Society Series C - Applied Statistics, ISSN:0035-9254
Elipot S, Sykulski A, Lumpkin R, et al., 2022, A dataset of hourly sea surface temperature from drifting buoys, Scientific Data, Vol:9, ISSN:2052-4463
Guillaumin AP, Sykulski AM, Olhede SC, et al., 2022, The debiased spatial whittle likelihood, Journal of the Royal Statistical Society Series B - Statistical Methodology, Vol:84, ISSN:1369-7412, Pages:1526-1557
O'Malley M, Sykulski AM, Laso-Jadart R, et al., 2021, Estimating the travel time and the most likely path from lagrangian drifters, Journal of Atmospheric and Oceanic Technology, Vol:38, ISSN:0739-0572, Pages:1059-1073
Sykulski AM, Olhede SC, Guillaumin AP, et al., 2019, The debiased Whittle likelihood, Biometrika, Vol:106, ISSN:0006-3444, Pages:251-266
Lilly JM, Sykulski AM, Early JJ, et al., 2017, Fractional Brownian motion, the Matern process, and stochastic modeling of turbulent dispersion, Nonlinear Processes in Geophysics, Vol:24, ISSN:1023-5809, Pages:481-514
Sykulski AM, Olhede SC, Lilly JM, et al., 2017, Frequency-domain stochastic modeling of stationary bivariate or complex-valued signals, IEEE Transactions on Signal Processing, Vol:65, ISSN:1053-587X, Pages:3136-3151
Sykulski AM, Olhede SC, Lilly JM, 2016, A widely linear complex autoregressive process of order one, IEEE Transactions on Signal Processing, Vol:64, ISSN:1053-587X, Pages:6200-6210
Elipot S, Lumpkin R, Perez RC, et al., 2016, A global surface drifter data set at hourly resolution, Journal of Geophysical Research: Oceans, Vol:121, ISSN:2169-9275, Pages:2937-2966
Sykulski AM, Olhede SC, Lilly JM, et al., 2016, Lagrangian time series models for ocean surface drifter trajectories, Journal of the Royal Statistical Society Series C - Applied Statistics, Vol:65, ISSN:0035-9254, Pages:29-50