Professor Peter Flach
M.Sc.(Twente)
Current positions
Professor of Artificial Intelligence
Department of Computer Science
Contact
Media contact
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Research interests
Data-intensive computing and analytics;
Mining complex and highly structured data;
Evaluation, calibration and reuse of machine learning models;
Feature construction and subgroup discovery in data streams;
Intelligent reasoning, artificial intelligence.
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
Positions
University of Bristol positions
Professor of Artificial Intelligence
Department of Computer Science
Projects and supervisions
Research projects
Towards a Measurement Theory for Data Science and Artificial Intelligence
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/11/2018 to 31/10/2020
SPHERE (EPSRC IRC)
Principal Investigator
Role
Principal Investigator
Description
SPHERE : An EPSRC Interdisciplinary Research Collaboration (IRC).
This project aims to develop a platform of sensors to be deployed in people's homes to monitor the health and wellbeing
SPHERE is…Managing organisational unit
Department of Electrical & Electronic EngineeringDates
01/10/2013 to 30/09/2018
IEU Theme 2
Principal Investigator
Managing organisational unit
Bristol Medical School (PHS)Dates
01/06/2013 to 31/03/2018
REFrAMe
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/02/2013 to 01/08/2016
Generating value from smart electricity meter data
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/07/2011 to 01/02/2013
Thesis supervisions
Publications
Selected publications
19/04/2017Unsupervised learning of sensor topologies for improving activity recognition in smart environments
Neurocomputing
Beta calibration
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017)
Computational support for academic peer review
Communications of the ACM
Background Check
2016 IEEE 16th International Conference on Data Mining (ICDM 2016)
Reframing in context
AI Communications
Recent publications
01/01/2021Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
FACE
AIES '20
Explainability fact sheets
FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
POLSAR IMAGE CLASSIFICATION VIA ROBUST LOW-RANK FEATURE EXTRACTION AND MARKOV RANDOM FIELD
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium