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Benchmarking Machine Learning Algorithms

Machine learning algorithms are employed in a myriad of applications in almost any domain, be it industrial, commercial, scientific or academic. Many techniques have been developed to tackle such a diversity of applications. There are, however, very few comparative studies of these algorithms, most of them limited to a specific problem and its related datasets. Therefore, practitioners do not have well-supported information on what algorithms to use for their problems, having the burden of considering many algorithms, often without in-depth knowledge about their underlying technical details.

This project aims to go beyond these very few comparative studies, and to develop a systematic analysis and benchmarking of the widely-used machine learning algorithms and their implementations. The study will be generic, meaning it will not be problem/domain specific.

The student working on this project will have the opportunity to develop knowledge and skills related to a broad range of machine learning algorithms, techniques and tools. Such core abilities will result in a high employability level having the flexibility of applying them in any industry or domain.

Applicants should have (or about to obtain) a first or second class UK honours undergraduate or master degree, or a recognised equivalent from an overseas university in a discipline such as computer science, computer engineering, statistics, mathematics, physics, or other related areas. A solid background in computer programming is essential.

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)

Liliana Teodorescu - Liliana has joined the Electronic and Computer Engineering Department at Brunel University as a Research Fellow, progressing to a Lecturer and then to the current Senior Lecturer position. She has previously been a Research Fellow at Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy, and a Visiting Researcher at Stanford Linear Accelerator Centre (SLAC), USA, and at Thomas Jefferson National Accelerator Facility (Jefferson Lab), USA. Liliana has a diverse experience acquired participating in international large-scale high and medium energy particle and nuclear physics experiments. She combines physics with computer science/engineering and radiation detection instrumentation both in her research and teaching. She has experience in research supervision both at doctoral and post-doctoral level. Liliana served the scientific community by organising high-profile international events at Brunel University such as the 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT), and the 33rd CERN School of Computing. She continues her involvement in these events. Liliana is a Fellow of Higher Education Academy (HEA), and a member of the Institution of Engineering and Technology (IET).