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 environmental, climate and ocean sciences. Adam has supervised 6 PhD students to completion (with a further 3 under current supervision), 4 post-doctoral researchers and 11 Master’s project students. Adam has published 30 peer-reviewed papers (see publications tab above) in leading statistics journals such as Biometrika, the Journal of the Royal Statistical Society (Series B and Series C, x3), and IEEE Transactions on Signal Processing (x2), 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 (please see CV) and is the current Discussion Papers Editor and Discussion Meetings Secretary for the Royal Statistical Society.

Links to recorded research talks

- Inference for large time series and spatial data, specifically on debiasing the Whittle Likelihood - given to Banff International Workshop (link to talk)

- Talks on statistics for oceanographic "drifters" - given to Carnegie Mellon University (link to YouTube) and the TIDE Research Hub in Australia (link to YouTube)

Open source data and software

Adam has collaborated on developing the following oceanographic data and software products regarding the Global Drifter Program:

- Hourly resolution position, velocity, and sea surface temperature data (link to NOAA website)

- Online tool for finding the most likely path and travel time taken by a surface particle/drifter between any two locations in the ocean (link to NOAA website)

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

External Examiner, MSc in Statistics, King's College London

Ocean Uncertainty Quantification Working Group Member, US Climate Variability and Predictability Program

Partner Investigator, Transforming energy Infrastructure through Digital Engineering (TIDE), Australian Research Council

Co-Lead for Equality, Diversity and Inclusion, Department of Mathematics, Imperial College London


Imperial College London

2023/24: Spatial Statistics (a new UG 3rd year module in our Mathematics Degree Programmes)

2022/23: Multivariate Analysis (an MSc Statistics and MSci Mathematics module)

Lancaster University

2020/21 and 2021/22: Statistics (UG 1st year module in Mathematics)

2018/19 and 2019/20: Time Series Analysis (UG 3rd year module in Mathematics)

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)

Jakub Pypkowski (started in 2023)

Vanessa Madu (started in 2023)

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., 2023, A multivariate pseudo-likelihood approach to estimating directional ocean wave models, Journal of the Royal Statistical Society Series C - Applied Statistics, Vol:72, ISSN:0035-9254, Pages:544-565

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

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