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Professor Peter A FlachMSc(Twente), PhD(Tilburg)

Professor of Artificial Intelligence

Peter Flach

Professor Peter A FlachMSc(Twente), PhD(Tilburg)

Professor of Artificial Intelligence

Member of

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

View research connections

Postal address:
Merchant Venturers Building
Woodland Road
Clifton
Bristol
United Kingdom

Selected research outputs

  1. Published
  2. Published

    Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Published

    SPHERE: A sensor platform for healthcare in a residential environment

    Research output: Chapter in Book/Report/Conference proceedingChapter in a book

  4. Published

    BDL.NET: Bayesian dictionary learning in Infer.NET

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  5. Published
  6. Published
  7. Published
  8. Published

    ROC Analysis

    Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

  9. Published

    Background Check: A General Technique to Build More Reliable and Versatile Classifiers

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  10. Published

    Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov test

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  11. Published

    Classifier Calibration

    Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

  12. Published

    Cost-sensitive boosting algorithms: Do we really need them?

    Research output: Contribution to journalArticle

  13. Published

    On the Need for Structure Modelling in Sequence Prediction

    Research output: Contribution to journalArticle

  14. Published

    The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data

    Research output: Contribution to journalArticle

  15. Published
  16. Published

    Machine learning to assist risk of bias assessments in systematic reviews

    Research output: Contribution to journalArticle

  17. Published
  18. Published

    Precision-Recall-Gain Curves: PR Analysis Done Right

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  19. Published

    Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy (Best paper award)

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  20. Published
  21. Published

View all (204) »

Selected awards and activities

  1. Machine Learning (Journal)

    Activity: Publication peer-review and editorial workEditorial activity

View all (1) »