Professor Peter A Flach

Biography

Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading researcher in the areas of mining highly structured data and the evaluation and improvement of machine learning models using ROC analysis, he has also published on the logic and philosophy of machine learning, and on the combination of logic and probability. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012).

Prof Flach is the Editor-in-Chief of the Machine Learning journal, one of the two top journals in the field that has been published for over 25 years by Kluwer and now Springer. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol. He is a founding board member of the European Association for Data Science. 

Prof Flach's research has been funded by EPSRC, MRC, TSB and the EU, among others. He is currently leading the Data Fusion and Data Mining work package in the SPHERE IRC funded by EPSRC, the Bioinformatics and Data Mining cross-cutting theme in the Integrative Epidemiology Unit funded by MRC (with Dr Tom Gaunt), and the REFRAME project with the Universities of Valencia and Strasbourg funded by CHIST-ERA. 

Expertise

My main expertise is in mining highly structured data, as found in many scientific disciplines; and in data-intensive computing, which is an emerging computational paradigm in which the sheer volume of data is the dominant performance parameter. I have been working in these areas as part of the University-wide research theme in Exabyte Informatics, which I lead.

• machine learning
• data mining
• structured data
• exabyte informatics
• scientific discovery
• data-intensive computing
• networks

Keywords

• Machine Learning
• Data Mining
• Data Science
• Artificial Intelligence