Risk and Decision-Making for Data Science and AI (Postgraduate)
This module provides a comprehensive overview of the challenges of risk assessment, prediction and decision-making covering public health and medicine, the law, government strategy, transport safety and consumer protection. Students will learn how to see through much of the confusion spoken about risk in public discourse, and will be provided with methods and tools for improved risk assessment that can be directly applied for personal, group, and strategic decision-making. The module also directly addresses the limitations of big data and machine learning for solving decision and risk problems.
Full and current details of my research and publications can be found on my home page.
My current research focuses primarily on quantitative risk assessment. This typically involves analysing and predicting the probabilities of unknown events using causal, probabilistic models (Bayesian networks). This type of reasoning enables improved assessment by taking account of both statistical data where available and also expert judgment, providing more powerful insights and better decision making than is possible from purely data-driven solutions. Hence, the approach can be summarized as 'smart data rather than big data'. Applications include medicine, law and forensics (I have been an expert witness or consultant in many major criminal and civil cases), security, software reliability, transport safety and reliability, finance, and football prediction. I have been Principle Investigator in multiple collaborative projects (details of my current and recent projects can be found here).
I have a special interest in raising public awareness of the importance of probability theory and Bayesian reasoning in everyday life (including how to present such reasoning in simple lay terms) and maintain a blog, twitter account and also a website dedicated to this. I have published 7 books and over 300 referred articles. My book "Risk Assessment and Decision Analysis using Bayesian Networks" with Martin Neil (first edition 2012, second edition 2018) was the first to bring Bayesian networks to a general audience. In 2015 I presented the award-winning BBC documentary Climate Change by Numbers.
In addition to my research on risk assessment, I have a long track record of work in software engineering (including pioneering work on software metrics); the third edition of my book ?Software Metrics: A Rigorous and Practical Approach? was published in November 2014.