Solon Karapanagiotis

Solon Karapanagiotis

Research Associate
MRC Biostatistics Unit

University of Cambridge

Biography

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. Under this scenario it is desired to use a metric which reflects the loss function to be used for the prediction problem thus “making” the model perform well for the particular task.

I’m also looking at novel ways to detect cancer directly from the blood through “liquid biopsies” which has the potential to change the way cancer is diagnosed, monitored, and even treated. My research focuses on novel ways to incorporate circulating tumour DNA (ctDNA) into the management of cancer patients.

I obtained my PhD from the MRC Biostatistics Unit under the supervision of Paul Newcombe and Oscar Rueda. Before starting my PhD, I studied for a MSc in Statistics at KU Leuven.

Interests

  • 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

Teaching

  • I have supervised 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.



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