Professor Peter Flach
M.Sc.(Twente)
Current positions
Professor of Artificial Intelligence
School of Computer Science
Contact
Press and media
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Research interests
Short 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).
From 2010 to 2020, Prof Flach was 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, former President and current Vice-President of the European Association for Data Science.
Prof Flach's research has been funded by UKRI, EPSRC, MRC, TSB and the EU, among others. He is currently Director of the UKRI Centres for Doctoral Training in Interactive Artificial Intelligence and Practice-Oriented Artificial Intelligence.
Expertise
Prof Flach's main expertise is in data-driven computational methods such as machine learning and data science, and in human-centred artificial intelligence which combines data-driven and knowledge-driven approach to AI with human-AI interaction and responsible AI.
Keywords
- Machine Learning
- Data Science
- Human-Centred Artificial Intelligence
- Practice-Oriented Artificial Intelligence
Projects and supervisions
Research projects
UK-Canada doctoral exchange scheme Stefan Radic Webster
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/09/2022 to 31/08/2023
UK-Canada doctoral exchange scheme Stefan Radic Webster
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/09/2022 to 31/08/2023
UK-Canada doctoral exchange scheme Stefan Radic Webster
Principal Investigator
Managing organisational unit
School of Computer ScienceDates
01/09/2022 to 31/08/2023
8030 H2020 TAILOR 952215
Principal Investigator
Managing organisational unit
Department of Computer ScienceDates
01/09/2020 to 31/08/2023
InnovateUK ML4D: Machine Learning for Enhanced Diabetes Self-Care
Principal Investigator
Role
Co-Investigator
Description
Innovate UK: Digital health technology catalyst round 2, collaboration between Quin Technologies Ltd and University of BristolManaging organisational unit
Department of Computer ScienceDates
01/11/2018 to 30/04/2020
Thesis supervisions
Object-oriented data mining
Supervisors
Towards Intelligible and Robust Surrogate Explainers
Supervisors
Higher-order frameworks for profiling and matching heterogeneous data
Supervisors
Towards Explainable Time Series
Supervisors
Uncertainty aware classification
Supervisors
Efficient Continual Learning
Supervisors
Publications
Selected publications
01/04/2017Beta 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)
Unsupervised learning of sensor topologies for improving activity recognition in smart environments
Neurocomputing
Reframing in context
AI Communications
Recent publications
21/10/2023Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers
CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations
A multi-sensor dataset with annotated activities of daily living recorded in a residential setting
Scientific Data
Classifier calibration
Machine Learning
Collecting Food and Drink Intake Data With Voice Input
JMIR mHealth and uHealth