Adam Sykulski

Senior Lecturer in Statistics

Imperial College London

United Kingdom

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About

Adam Sykulski is a Senior Lecturer in Statistics at Imperial College London, UK. His research areas include isotropy testing in spatial point patterns, probabilistic prediction of oceanographic velocities, and spectral density estimation. He has also contributed to topics such as macro-scale genetic connectivity of marine plankton populations and outlier detection in network revenue management.

Articles (17)

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2025

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2020

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2020

Collaborators (6)

Guillermo Perez Algorta

University of Lancaster

UNITED KINGDOM

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Shane Elipot

Research Associate Professor

University of Miami

UNITED STATES

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Frederik Simons

Professor

Princeton University

UNITED STATES

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edward cripps

University of Western Australia

AUSTRALIA

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Catherine Cleophas

University of Lancaster

UNITED KINGDOM

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Lachlan Astfalck

University of Western Australia

AUSTRALIA

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Adam Sykulski

Research Interests

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About

Articles (17)

Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification

Probabilistic Prediction of Oceanographic Velocities with Multivariate Gaussian Natural Gradient Boosting – CORRIGENDUM

Debiasing Welch’s Method for Spectral Density Estimation

Outlier detection in network revenue management

Probabilistic Prediction of Oceanographic Velocities with Multivariate Gaussian Natural Gradient Boosting

Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations

A multivariate pseudo-likelihood approach to estimating directional ocean wave models

Analysing and visualising bike-sharing demand with outliers

The elliptical Ornstein–Uhlenbeck process

The Debiased Spatial Whittle likelihood

A dataset of hourly sea surface temperature from drifting buoys

Identifying and Responding to Outlier Demand in Revenue Management

Estimating the parameters of ocean wave spectra

Estimating the travel time and the most likely path from Lagrangian drifters

Nonparametric Time Series Summary Statistics for High-Frequency Accelerometry Data from Individuals with Advanced Dementia

Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

Collaborators (6)

Guillermo Perez Algorta

Shane Elipot

Frederik Simons

edward cripps

Catherine Cleophas

Lachlan Astfalck

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