Bio

Andrew Burnie is studying for a PhD in Computer Science at UCL. His research applies open-source tools (e.g. R and Python) to analyse the linkage between changes in social media discussions and phasic shifts in the cryptocurrency price movement across time. His work has been presented at ACM SIGIR and published in Royal Society Open Science and Ledger. His cryptocurrency classification framework was included with written evidence provided by Eversheds Sutherland LLP in the UK Parliament Digital Currencies Inquiry. Previously, he was a data scientist at Hitachi Consulting and at ERS insurance (a Lloyd’s syndicate), where he set up the data science team. He has a BA in Economics and Management Studies (Cambridge University) and an MSc Finance, culminating in a publication (Burnie and Mchawrab, 2017).

Research interests

His research examines the association between events and concerns expressed in social media posts and phasic shifts in the cryptocurrency price movement across time. This applies natural language processing, neural networks (word2vec), non-parametric statistical hypothesis testing, time series analysis, social media analysis, causal inference and sentiment analysis.