Exit Menu

Design human-like machine learning

A study of the publications available via PubMed alone (a database of 26 million references to biomedical publications), shows that only four machine learning models (linear regression, Bayesian reasoning, Random Forests and Neural Networks) are frequently employed in most of the experiments. Non-experienced machine learning users tend to always use some standard methods which have previously been used in similar studies. For a non-expert, it is difficult to know which method to use when. Even a 5% error difference may have a huge impact in some cases such as prescribing a drug, for instance. The models themselves have their limitations and there is a whole branch of computer scientists who work on theoretical aspects and prove the bounds of these models.

A solution to this unnecessary scientific uncertainty will be automated machine learning. This automated machine learning should be able to imitate all aspects of human information acquisition:

  • learn (from experience or from data);
  • unlearn (or forget information that is no longer valid);
  • relearn (when previous information has not been corrected acquired);
  • be taught (via an optimal number of examples).

There isn’t any complex methodology available which includes all these building blocks together and makes use of them. This will be the aim of this project, resulting in an easy to access framework for advanced data analysis and visualisation; an intelligent assistant which could interact with the user, could self-learn from experience based on similarity between the data analysed so far and which - based on a research on (already existing) research - could ensure the quality of the results through repetitive tests, automating the automation of knowledge discovery.

A strong computer science (machine learning) background is required as well as strong programming skills (Python, R, Java, C++).

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Crina Grosan - I am a Senior Lecturer in the Department of Computer Science at Brunel University. Prior to joining Brunel, I was an academic staff member at Babes-Bolyai University Cluj-Napoca, one of the top universities in Romania. My research interests span the areas of machine learning, optimisation, and multi-relational graphs. They include both theoretical and algorithmic development as well as applications for particular classes of problems such as classification, clustering, prediction, estimation, decision-making, pattern finding, data mining, very large systems of nonlinear equations and large-scale optimisation. 

Related Research Group(s)

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.