Current members of the Research Software Engineering team are listed below. Previous members of the team can be found on our Alumni page.

Romain Thomas

Romain joined the RSE Team as Head in August 2023. Prior to coming to the University of Sheffield he was a staff Astronomer at the European Southern Observatory (Chile) where he led the development of the SCUBA software framework, a tool dedicated to control the quality of more than 15 Very Large Telescope (VLT) instruments.

Romain holds a PhD in Astrophysics and Cosmology carried out at the Laboratoire D’Astrophysique de Marseille, France. He then went for a postdoc at the Universidad de Valparaiso, Chile and became a fellow at the European Southern Observatory a couple of years later. During his research time, Romain focused on the study of high redshift galaxies using large galaxy survey conducted mainly at the VLT. Romain has published various modules/software related to his work in astronomy (SPARTAN,dfitspy, SEDOBS, Photon).

Twin Karmakharm

Twin is a Senior Research Software Engineer who completed his PhD at the University of Sheffield. He specialises in High-performance agent-based pedestrian simulation, Parallel computing using GPUs, Virtual reality and Deep learning. He currently provides consultancy, training and technical support for researchers on Deep learning and other GPU related software engineering problems.

Adam Stanton

Adam is a Senior Research Software Engineer with a focus on AI and ML, and works jointly at the Alan Turing Institute and the University of Sheffield. His research background is in nature-inspired autonomous systems, with particular focus on evolutionary robotics. Prior to taking up this post, he held a lectureship in Computer Science at Aston University and before that, a similar post at Keele University.

Daniele Tartarini

Daniele joined the RSE team in 2020. He mainly supports:

  • CompBioMed2 Centre of Excellence for Biomedical Applications (EC-Horizon2020 project).
  • INSIGNEO Institute for in silico medicine.

In CompBioMed, he optimises biomedical applications and workflows to make them ready for the next generation exascale supercomputers (i.e. machines capable of computing 10^18 floating point operations/second) and Cloud-HPC. The pFIRE (Parallel Framework for Image Registration) is one of the applications under development in CompBioMed.

Daniele has a PhD in “Interdisciplinary Sciences and Technologies” and a Laurea cum Laude in “Information Systems Engineering”, from the Universita’ del Salento in Lecce, Italy. His expertise spans from parallel and distributed computing, GPU computing, bioinformatics, machine learning, and computational mechanics.

Before joining the RSE team, he worked as Research Associate on a range of multidisciplinary projects:

  • ERC Excellence Science “A high-fidelity isogeometric simulation methodology for fracture in porous media”, PI Prof. Rene’ de Borst. Dept. of Civil and Structural Engineering, University of Sheffield.
  • EPRSC IAA “Accelerating in silico cancer research with graphics processors”, PI Daniele Tartarini. Dept. Of Mechanical Engineering, University of Sheffield.
  • EC-Horizon2020 CHIC, “Computational Horizons in Cancer: Developing Meta- and Hyper-Multiscale Models and Repositories for In Silico Oncology”, PIs Prof Marco Viceconti, Dr DC Walker. Dept. Of Mechanical Engineering, University of Sheffield.
  • EPSRC, Automatic finite element code generation for GPU/parallel architectures. Dept. of Engineering, University of Cambridge.
  • Italian Ministry of Education, Development of a distributed virtual laboratory of bioinformatics in a Grid infrastructure. University of Salento in Lecce, Italy.
  • He has also provided consultancy for public administration and private companies.


Dan Brady

Dan joined the RSE team in December 2022. His background is in Cognitive Neuroscience, completing his PhD at Goldsmiths in 2016. Since then he has worked as a Research Fellow at Birkbeck and the University of Surrey and as Research Technician at the University of Reading. He has experience of writing research software and analysis pipelines using R, Python and Julia. He is also a keen advocate of open and reproducible research practices.

Edwin Brown

Edwin joined the RSE team in October 2022. He comes from a background in geophysics following a BSc and MSc in Geophysical Sciences at the University of Leeds. After university, he worked in the private sector, developing machine learning (ML) workflows to solve geophysical imaging and inversion problems.

Edwin has practical experience in the designing, training and evaluation of ML models. He is experienced in Python having worked with data science libraries such as Numpy, Pandas, Scikit-learn, Tensorflow and Keras. He has a growing interest in MLOps (Machine Learning Operations) and the practical challenges of scaling up ML practices.

Robert Chisholm

Robert is a Research Software Engineer that previously completed his PhD at the University of Sheffield. He specialises in GPU accelerated computing and complex system simulations, following his PhD’s focus on improving the performance of spatial communication in GPU accelerated algorithms.

He is a developer of the FLAMEGPU software framework, facilitating wider access to complex systems modelling on GPUs.

Previously he worked on the PRIMAGE project, which proposed an open cloud-based platform to support decision making in the clinical management of two paediatric cancers. In particular, working towards the development of a cell scale model of neuroblastoma to be scaled across multiple GPUs and distributed HPC resources.

Currently he is working with Fujitsu Research Europe developing a GPU accelerated transport model.

Since the 2022/2023 academic year he has been the module leader for COM4521/COM6521 that covers parallel programming with OpenMP and CUDA.

Peter Heywood

Peter is a Research Software Engineer in the process of completing his PhD at the University of Sheffield. He specialises in GPU accelerated computing and complex system simulations; including transport network simulation and biological cellular simulations. He is currently working on the STriTuVaD project (a Horizon2020 project), which focuses on the use of in silico trials to support and improve tuberculosis vaccine development.

Tamora James

Tamora joined the RSE team in November 2023. She began her career in web and software development, working for over a decade in a variety of roles, before returning to academia to complete an MRes in population ecology and a PhD in conservation demography at the University of Sheffield. While studying for her PhD she became interested in how she could use her background in software development to help other researchers to improve their research software outputs by teaching them about commonly used tools and practices such as version control.

After completing her PhD in 2020, she joined the Centre for Environmental Modelling and Computation (CEMAC) at the University of Leeds as a Software Development Scientist, initially supporting the Global Challenges Research Fund African Science for Weather Information and Forecasting Techniques (GCRF African SWIFT) project. While at Leeds she worked on a range of projects including automated synoptic plotting for operational forecasting in Africa, visualisation of climate co-benefits, detection of biometeors using radar data, and the FASTA mobile app for near-real time weather forecasting (nowcasting) in Africa.

She has experience in writing research software using R and Python, and she has developed and led training in version control and reproducible research practices since 2016. She has been a co-organiser of Sheffield R Users Group since 2017.

Matthew Leach

Matt is a Research Software Engineer in Complex Systems. He has a background in computer graphics, using physical-modelling to produce animations. He has recently completed his PhD on modelling the human mouth using the finite element method and also has experience with fluid simulation. Aside from physical-modelling, he also has research experience working with virtual and augmented reality.

Matt’s work on the team primarily revolves around the development of FLAMEGPU and advocating the use of GPU computing to support research. He is currently improving the performance of a model of tuberculosis spread.

  • Email: m.leach (at)
  • Github: @MILeach

Neil Shephard

Neil has taken a convoluted path to reach his current role as research software engineer. After completing his undergraduate (BSc Zoology and Genetics) and post-graduate (MSc in Genetic Epidemiology) at The University of Sheffield he spent a number of years as a Genetics Statistician researching the aetiology of complex human diseases at University of Manchester, and University of Western Australia before returning to University of Sheffield learning UNIX system administration and literate programming along the way. He then shifted careers to medical statistics and spent eight years working at the Clinical Trials Research Unit at the University of Sheffield. Throughout this time Neil developed and became enthusiastic about reproducible research and developed practical approaches to achieving reproducible research in R using RMarkdown.

In 2018 he left academia for the private sector working for a telematics company using data captured from mobile phones and “black boxes” to quantify driver behaviour. Here he developed an understanding of working with geo-spatial data and learnt Python along with various aspects of good software development practices including working collaboratively using Git for version control.

Current projects:

Interests include:

  • Reproducible research and literate progrmaming
  • Open Science
  • Python, R and Bash
  • Evolutionary Genetics
  • Emacs and Org-mode


Contact Us

For queries relating to collaborating with the RSE team on projects:

Information and access to JADE II and Bede.

Join our mailing list so as to be notified when we advertise talks and workshops by subscribing to this Google Group.

Queries regarding free research computing support/guidance should be raised via our Code clinic or directed to the University IT helpdesk.