Fellow Detail

Neuroimaging, Magnetic Resonance Imaging, Machine Learning and Pattern Recognition, Computational Psychiatry, Brain Stimulation Modeling

I am a postdoctoral researcher at the Wellcome Centre for Integrative Neuroimaging (WIN). My research involves developing tools and algorithms for analysing neuroimaging data from multimodal MRI with a focus on 1) Detection of predictive brain biomarkers using research and clinical datasets, 2) Developing machine learning applications for automated detection and classification, 3) Development of neuroimaging pipelines on data analysis platforms (DPUK, HPC, AWS), 4) Reproducibility and open research practices in neuroimaging.

I am currently working on a project funded by the NIHR Oxford Health BRC Brain Technologies theme, which focuses on the scientific/clinical interface to create and test accurate tools for measuring brain structure and function to improve risk identification, early diagnosis, outcome prediction, and treatment targeting. In this project, I am primarily developing methods to obtain comparable measures from scans acquired with different hardware in clinical and research settings.

In my previous postdoc at the Dept of Psychiatry, I worked on the Dementias Platform UK (DPUK) project where I implemented and adapted UK Biobank brain imaging pipelines for dementia cohorts. I am also involved in developing image analysis pipelines for clinical datasets (FSL clinical). I work with various neuroimaging software/tools/coding frameworks for analysing multimodal brain magnetic resonance imaging data. I enjoy working with big data, developing and troubleshooting brain imaging pipelines, and reproducibility in neuroimaging research.

I have been selected as a UKRN Local Network Lead where I work collaboratively with other leads to engage and expand the Reproducible Research Oxford (RROx) community via networking, training and other events at the University of Oxford. I have been awarded with Software Sustainibility Institute (SSI) fellowship. With this exciting opportunity, I engage and collaborate with the research software community, and explore interdisciplinary approaches for contributing to and nurturing best practices in research software in my field.