Imperial College London

Dr Adam Sykulski

Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer in Statistics



adam.sykulski CV




527Huxley BuildingSouth Kensington Campus





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 24 peer-reviewed papers in leading statistics journals such as Biometrika 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, IEEE Transactions on Signal Processing (x2), and the European Journal of Operational Research. Adam has obtained external funding on numerous projects as PI and Co-I (please see detailed 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 (viva passed in 2017)

Dr Nicola Rennie (viva passed in 2021)

Dr Michael O'Malley (viva passed in 2022)

Dr Sarah Oscroft (viva passed in 2022)

Dr Keerati Suibkitwanchai (viva passed in 2022)

Jake Grainger (viva passed in 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

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

Grainger JP, Sykulski AM, Jonathan P, et al., 2021, Estimating the parameters of ocean wave spectra, Ocean Engineering, Vol:229, ISSN:0029-8018

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

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