Shakeel Gavioli-Akilagun

Hello! Welcome to Shakeel's academic website. I am a Fellow in Statistics at the London School of Economics and Political Science; I am part of the Time Series and Statistical Learning research group. Previously I was a PhD student in the same department, where I was supervised by Professor Piotr Fryzlewicz. Some of my research interests include:
  • Multiscale statistical modeling
  • Change points and feature detection
  • Shape constrained estimation
  • Aspects of causal inference
A recent version of my CV can be found here.


Publications:

Fast and Optimal Inference for Change Points in Piecewise Polynomials via Differencing. S. Gavioli-Akilagun and P. Fryzlewicz. Electronic Journal of Statistics (2025) [open access link; R package; numerical examples]

Invited discussion of "Automatic Change-Point Detection in Time Series via Deep Learning" by Li, Fearnhead, Fryzlewicz, and Wang. S. Gavioli-Akilagun. Journal of the Royal Statistical Society Series B (2023) [discussion; slides; original paper]


Preprints and projects:

Optimal Online Change Detection via Random Fourier Features. F. Kalinke and S. Gavioli-Akilagun In submission (2025) [arXiv; software; numerical examples]

Adaptive Detection of Machine-Generated Text. H. Zhou, J. Zhu, P. Su, K. Ye, Y. Yang, S. Gavioli-Akilagun, and C. Shi. In submission (2025)

Causal Change Point Detection using Kernels. F. Quinzan, S. Gavioli-Akilagun, and B. Dou. Work in progress (2025+)

Detecting Changes in Production Frontiers. S. Gavioli-Akilagun and Y. Chen. Work in progress (2024+)

Online Detection of Changes in Mean Reverting Processes with Local Cointergration. S. Gavioli-Akilagun, B. Dou, S. Tiwari, and Q. Yao. Work in progress (2024+)

Robust Inference for Change Points in Piecewise Polynomials using Confidence Sets. S. Gavioli-Akilagun and P. Fryzlewicz. Manuscript in preparation (2023+)


Recent and upcoming events:


Teaching:

This academic year (2024/25) I am the GTA for the following courses (office hours bookable via the Student Hub).