Jordan graduated from the University of East Anglia in 2015 with a BSc in Actuarial Sciences. He then worked as an analyst for NHS Property Services Ltd in London for 2 years where he mainly created, developed, and trained staff from various departments on financial models. He noticed the increasing uses of machine learning and artificial intelligence in a wide range of applications and went on to complete his MSc in Data Science at the University of Bath. His Master’s dissertation was on the applications of machine learning for stock investment, whereby given some state representation of the market, one had to decide whether to buy or sell a stock. Jordan modelled this as a reinforcement learning problem with the use of neural networks as a function approximator. In his spare time, Jordan enjoys playing table tennis, badminton and the piano.

Research project title:
Prediction of Biological Meta-Data using DNA Sequences

Supervisor(s):
Sandipan Roy, Matt Nunes, Lauren Cowley

Project description:
A human base-paired DNA sequence is approximately 3 million letters in length and consists of the letters “G”, “A”, “T”, and “C”. With the multitude of different combinations, it is of interest to find whether, given a subset of much smaller sub-sequences, one can infer that an individual caught some bacteria from a particular country or any other associated meta-data. The bacteria of interest initially include Shiga Toxigenic Escherichia coli and Salmonella. Though machine learning methods have shown recently to provide state-of-theart results in many domain areas in high dimensional space, many of the methods are seen as “black boxes” with little interpretability. Jordan is investigating explainable machine learning and statistical methods in context of both prediction and ranking of the significance of the sub-sequences.