MRC Biostatistics Unit

I’m a researcher at the MRC Biostatistics Unit, University of Cambridge.

I’m interested in many areas across statistics, machine learning, and their interactions. My research focuses on tailored model development, that is targeted model building for a specific task of interest, mostly prediction.

I’m also developing and applying tools leveraging genomics to detect cancer earlier, which will ultimately lead to more personalized treatment for patients. Toward these goals, I draw from a wide range of disciplines, including molecular biology, computational biology, and medical oncology. More specifically, I’m looking at novel ways to incorporate liquid biopsies into the management of cancer. Liquid biopsies - the analysis of tumours using biomarkers circulating in fluids such as the blood - have the potential to change the way cancer is diagnosed, monitored, and treated.

- Bayesian Methods
- Biomedical Research
- Genomics
- Statistical Computing
- Statistical Modelling
- Longitudinal Data Analysis
- Statistical Learning
- Machine Learning
- Medical Decision Making
- Predictive Modelling and Model Validation
- Survival Analysis
- Translational Genomics

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False Positive Multiparametric Magnetic Resonance Imaging Phenotypes in the Biopsy-naïve Prostate: Are They Distinct from Significant Cancer-associated Lesions? Lessons from PROMIS.
In *EUROPEAN UROLOGY*.

(2021).
Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.
In *Clinical Cancer Research*.

(2018).
Ventilatory limitation and dynamic hyperinflation during exercise testing in Cystic Fibrosis.
In *Pediatric pulmonology*, 52(1), 29-33.

(2017).
Isocapnic hyperpnea with a portable device in Cystic Fibrosis: an agreement study between two different set-up modalities.
In *Journal of Clinical Monitoring and Computing*, 29(5), 569-572.

(2015).
Dissertation Supervisor (2022) at the MPhil in Computational Biology, University of Cambridge.

Lecturer (Lent 2022) at the MPhil in Computational Biology.

Teaching Assistant and Dissertation Supervisor (2021-2022) at the MPhil in Population Health Sciences, University of Cambridge.

Supervisor Statistics IB, Lent 2019 (2nd year undergraduate course from the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge).

This is the handbook for the course I teach as a Brilliant club tutor. The Brilliant Club is a charity aiming to increase the number of pupils from under-represented backgrounds progressing to highly selective universities. They do this by mobilising PhD researchers to share their academic expertise with state schools.

The material is designed for Key stage 4 pupils but can be easily adapted to Key stage 5.

About the course: Virtually every decision is made in the face of uncertainty. In this course, I quantify uncertainty using probability theory. I then introduce the expected utility framework as a model of choice behaviour under uncertainty.

Cite this work as: Solon Karapanagiotis. Which bicycle lock should I buy? A journey to decision making under uncertainty, 2019.

Statistics · R · Random