The University of Southampton

Dr Dan Burns 

Dan is a Research Engineer at the IT Innovation Centre within the School of Electronics and Computer Science, University of Southampton. After a PhD in Theoretical Particle Physics from the University of Manchester completed in 2015, with a short post-doc in Particle Cosmology in 2016, he joined the IT Innovation Centre in 2017.

He has an intimate familiarity with the data science pipeline: from the extraction of meaningful data and pre-processing, to the critical evaluation of results from machine learning algorithm studies and the deployment of machine-learned models. He is primarily driven by the vast application domain of statistics, particularly where cross-disciplinary work is involved, and has extensive experience in statistical analysis and the development of Monte-Carlo simulations.

Dan has worked on various EU H2020 and Home Office projects. At current, his work has been on the following research projects:

CrowdHEALTH (EU H2020 project): CrowdHEALTH intends to integrate high volumes of health-related heterogeneous data from multiple sources and develop data analytics tools to operate on the integrated data, with the aim of supporting policy making decisions. Dan’s work in this project is in the development of machine learning prediction algorithms to predict when patients will have the first diagnosis of a set of high-impact diseases, such as cardiovascular disease and chronic kidney disease, in a timely and accurate fashion such that one can construct possible preparation and prevention approaches. The project utilises the prediction algorithm for population risk stratification.

BigMedilytics (EU H2020 project): BigMedilytics aims to transform the EU healthcare sector by using state-of-the-art big data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously. Dan’s work in this project revolves around studying patient-inputted data associated with chronic obstructive pulmonary disease, linked with weather and pollution data in the patient’s home environment, to predict COPD exacerbation events. He is currently working on a machine learning algorithm to predict when these events will occur based on the linked data, so that patients can either prepare for the event by obtaining medication, or by preventing the event altogether.

OPERANDO (EU H2020 project): OPERANDO aims to specify, implement, field-test, validate and exploit an innovative privacy enforcement platform that will enable the Privacy as a Service (PaS) business paradigm and the market for online privacy services. Dan’s work in OPERANDO primarily involved the development of a privacy state model, where the state evolves dependent on the actions that are taken using a user’s personal data. The model can be used as a design tool to understand how user privacy is affected when designing systems, or as a runtime tool to keep track of user privacy in systems. It can also be used in assessing the risk to user privacy violation in various approaches to the pseudonymisation of data.

Dan has also completed several pieces of commercially funded work as part of the VIVACE accelerated capability environment, as well as direct projects from the UK Home Office.


Research interests

Dan’s research interests spans multiple fields in computer science. Dan is primarily interested in data science, particularly in the improvement of the overall data science pipeline. However, his core interests are in the following:

Missing data approaches: the vast majority of real-world applications of machine learning methods involve the problem of missing data. Imputation is one the most common approaches to handling missing data, and is practically useful. However, for accurate imputation, one needs a lot of data, as the imputation problem is often a more difficult problem to solve than the machine learning task at hand. Dan has great interest in developing approaches to missing data that do not involve imputation and exploits the full data in other manners, either through the modification of existing machine learning algorithms, or by the creation of new ones.

Electronic Patient Record event prediction: having an advanced knowledge of certain unfavourable medical events occurring, such as the first diagnosis of a high-impact disease or medical events that can repeat such as myocardial infarction, for individual patients can be used to either prepare for the event’s occurrence, or prevent it altogether. Dan is interested in developing approaches to predict these events, using either statistical analysis or machine learning methodologies, and developing models of disease progression. He is primarily interested in approaches that are generic, i.e., using electronic patient record datasets to determine the most significant factors affecting the events of interest rather than explicit domain expertise.

Development of machine learning approaches: Dan is keenly interested in the development of new and more effective feature selection approaches. He also has further interest in the development of new classification and regression approaches to incorporate imposed properties that the model should have. For example, an object detection and identification model using image data should have a robustness to a degree of noise in the image data.

Other interests within data science includes:

  • Development of new classification techniques for the exploration of scientific data
  • Time series, image, audio/speech specific feature extraction and classification approaches

Dan holds other research interests outside of data science, including:

  • Cryptography: efficient fully homomorphic encryption schemes for secure cloud computation
  • Cybersecurity: tools for cyberattack simulation


Grace, Paul, Burns, Daniel, Neumann, Geoffrey, Pickering, Brian, Melas, Panagiotis and Surridge, Michael (2018) Identifying privacy risks in distributed data services: A model-driven approach. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE. 1513 - 1518 . (In Press) (doi:10.1109/ICDCS.2018.00157).

Neumann, Geoffrey, Grace, Paul, Burns, Daniel and Surridge, Michael (2019) Pseudonymization risk analysis in distributed systems. Journal of Internet Services and Applications, 10 (1). (doi:10.1186/s13174-018-0098-z).

Burns, Daniel, Karamitsos, Sotirios and Pilaftsis, Apostolos (2016) Frame-covariant formulation of inflation in Scalar-Curvature theories. Nuclear Physics B, 907, 785-819. (doi:10.1016/j.nuclphysb.2016.04.036).

Burns, Daniel and Pilaftsis, Apostolos (2015) Matter quantum corrections to the graviton self-energy and the Newtonian potential. Physical Review D, 91 (6). (doi:10.1103/PhysRevD.91.064047).


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