Teaching
Data Science
The Computer Science module COMP229 (Introduction to Data Science) covers basic statistics (data summaries and hypothesis testing) and applied linear algebra (data clustering and principal component analysis).
Geometric and Topological Data Analysis
This new Computer Science module is under development and will be run in Spring 2020 for the first PhD cohort in the new doctoral network in Doctoral network in Artificial Intelligence for Future Digital HealthArtificial Intelligence for Future Digital Health.
Cryptography
The Computer Science module COMP315 (Technologies for e-commerce) covers online auctions and cryptography.
Modules for 2025-26
Introduction to Data Science
Module code: COMP229
Role: Teaching
Supervised Theses
- A Framework for Program Synthesis on Conditional Domains
- A new compressed cover tree for k-nearest neighbour search and the stable-under-noise mergegram of a point cloud
- APPLICATION OF MACHINE LEARNING FOR MASS CYTOMETRY DATA ANALYSIS OF CHRONIC LYMPHOCYTIC LEUKAEMIA
- APPLICATION OF MACHINE LEARNING FOR MASS CYTOMETRY DATA ANALYSIS OF CHRONIC LYMPHOCYTIC LEUKAEMIA
- Accelerating Molecular Materials Discovery Following Data-Driven Approaches
- Characterising urban processes using new forms of data and analysis
- Continuous Isometry Invariants of Periodic Crystal Structures
- Continuous Isometry Invariants of Periodic Crystal Structures
- Continuous Spaces of Low Dimensional Lattices
- Geometric and Topological Methods for Applications to Materials and Data Skeletonisation
- Isometry Invariants of Crystal Structures Based on Voronoi Domains and Interatomic Distances
- Maximally dense crystallographic symmetry group packings for molecular crystal structure prediction acceleration.
- Metrics for Materials Discovery
- Pattern Recognition for Weather Phenomena in Climate Data
- The Development of Novel Pulse Shape Analysis Algorithms for AGATA