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Mengran Li

PhD Candidate in Statistics School of Mathematics and Statistics, University of Glasgow
Extreme value theory | Causal inference

I am a graduating Ph.D. researcher specializing in the intersection of Extreme Value Theory (EVT) and Causal Inference, with a focus on applications in Climate Extremes and Environmental Statistics.

📢 Update: I am open to research-intensive postdoctoral opportunities and collaborations, including jointly developing proposals for competitive postdoctoral fellowships (e.g., EPSRC, Leverhulme). I would also be happy to discuss longer-term academic roles (e.g., Lecturer or Research Fellow) in research-aligned environments. If my research aligns with your interests, please feel free to contact me.

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Education

Doctor of Philosophy in Statistics

2022 – Present
PhD in Statistics under the supervision of Dr. Daniela Castro-Camilo.

Master of Science in Statistics (with Distinction)

2020 – 2021
Dissertation: Spatiotemporal modelling and prediction of average flows in Scotland based on generalized additive models.

Bachelor of Science in Statistics (Major) and Finance (Double Major)

2015 – 2019
Completed Bachelor’s degrees with a strong foundation in probability, statistical inference, and mathematical statistics.

Research

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Causal Discovery in Extremes

 Developing novel causal inference algorithms tailored for extreme value regimes, currently focusing on theoretical robustness and validation using extensive simulation studies.
Oct 1, 2025
1 min read

Extreme quantile treatment effect (EQTE)

 Extreme quantile treatment effects (eQTEs) measure causal impacts on outcome distribution tails. We propose the TIEE framework combining information across quantile levels with extreme value models, enabling causal inference for rare, high-impact events in environmental risk, economics, and public health.
Oct 1, 2025
3 min read

Extreme Event Attribution (EEA)

 Exploring how tail assumptions influence attribution of extreme climate events, combining simulation, spatial methodology, and software development.
Jul 1, 2025
4 min read

Wee Extremes: EVA 2023 Data Challenge

 Statistical Methodology for Extreme Event Risk Assessment in the EVA 2023 Data Challenge.
May 30, 2023
2 min read