Knowledge Discovery and Machine Learning

Knowledge Discovery and Machine Learning (KDML) is concerned with the development and application of algorithms that can analyse data and derive useful information from it. Our research encompasses both fundamental aspects of this area, as well as applications, primarily aimed at image analysis.

The  KDML working group forms a part of the department of Informatics at the University of Leicester.


Academic staff

Prof. Rajeev Raman

Prof. Yu-Dong Zhang (Eugene)

Dr. Joe Huiyu Zhou

Dr. Shuihua Wang

RAs and Ph.D. Students

Yasemin Asan - thesis topic: Mining Sequential Patterns from Uncertain Data

Rafael Ktistakis - thesis area: Compact Data Structures for Sequence Prediction

Former Members

Dr. Leandro Minku

Dr. Neil Walkinshaw

Othman Esoul - thesis topic: Identifying network packet structures from Sniffed Data

Tom Gransden - thesis topic: Automated Theorem Proving by State Machine Inference

Andreas Poyias - thesis topic: Compact Data Structures for Frequent Pattern Mining

Research Areas

  • Knowledge discovery and machine learning
  • Signal processing and image analysis
  • Data structures and big data

Research Funding

  • 2016: AWE Detection programme pilot project: Evidential Reasoning for Radiological Detection - Walkinshaw PI
  • 2015 - 2016: Department for Transport T-TRIG Grant: PREPAReD: Predicting, Preventing, and Analysing Rail Delays - Walkinshaw PI
  • 2015 - present: Royal Society International Exchanges: Discovering sequential patterns in large uncertain data - Raman PI
  • 2015 - 2016: DSTL ASUR EVIRE project (An Evidence-Based Reasoning Framework to Support the Transparent Control, Verification, and Validation of Autonomous Systems) - Walkinshaw PI
  • 2011 - 2016: College of Science and Engineering PhD Studentship Scheme + EPSRC DTA to support Thomas Gransden. Walkinshaw and Raman PI

Potential Collaborative Projects with Industry

Within KDML we have a broad range of research interests and capabilities. Below are some examples of current projects. If you have any queries or ideas, please do not hesitate to contact us (see below for contact details).

Effort-estimation from cross-company data: We have developed Machine Learning algorithms that can enable organisations to accurately predict effort by using cross-company data, reducing the dependence upon internally recorded data.

Textile flaw detection: We have had a successful series of collaborations with an industrial partner in the textile industry. As a part of this, we inferred classifiers to more accurately detect textile flaws, flagging up fewer false-positives, and leading to a higher degree of automation.

Analysing live data streams to predict rail traffic build-up: The DfT funded PREPAReD project is fusing live rail data with computational models to enable the prediction of rail delays.

Multi-factor decision support for software safety case assessments: We have developed a tool-supported approach to aggregate multi-faceted safety assessments for critical software components, and to produce coherent overviews.


Contact details for each staff member and most PhD students are available from their respective homepages.

For general enquiries about the KDML working group, please contact Joe Zhou. We welcome enquiries from industry about potential applications of algorithms.

Share this page: