DPhil EPSRC and MRC Systems Approaches to Biomedical Sciences Centre for Doctoral Training (Oct 2016 – Jan 2021)

• Thesis: In silico Characterisation of Antigen Receptor Binding Site Structures
• In Oxford Protein Informatics Group (OPIG), supervised by Prof. Charlotte Deane
• Industrial partners: AstraZeneca/MedImmune, GlaxoSmithKline, Roche and UCB.

BEng (Hons) Bioengineering, University of Sheffield (First Class Honours) (Sep 2013 – Jun 2016)

• Biomedical Engineering pathway

• Computational Modelling, Biomechanics, Classic Control Theories

• Scientist in Computational Engineering and Data Science
• Antibody sequence diversity analysis using machine learning
• Commission technology platforms
• Support therapeutics development projects
• Engage in collaborations with academic and international colleagues

• Communication and Relations Principal
• Manage marketing and mentorship for life science start-ups in the portfolio.

• Computer-aided drug discovery team
• Analysed in-house medicinal chemistry project data using a graph database

• Group: Oxford Protein Informatics Group
• Primary Supervisors: Prof Charlotte Deane, Dr Jinwoo Leem
• Industrial Partners: GSK, Roche, MedImmune, UCB
• Develop bioinformatics tools to analyse antibody structures and next generation sequencing datasets, for features contributing to the specific binding in antibodies.
• Techniques: Python, C++, R, Database, Statistical analysis
• Additional responsibility: Software development, Front-end and back-end web development

• Group: Oxford Protein Informatics Group
• Primary Supervisors: Prof Charlotte Deane, Dr. Jinwoo Leem
• Industrial Partners: GSK, Roche, MedImmune
• This project aims at discovering the preference of CDR canonical forms on the antibody framework. To investigate on the large next generation sequencing datasets, I built a pipeline for CDR canonical form (structural cluster) prediction from sequence information.
• Techniques: Python, Database, Short presentation

• Group: Computational Biology Group, Cardiac Electromechanics Research Group
• Supervisors: Dr Joe Pitt-Francis, Prof David Gavaghan (Oxford), Dr Steven Niederer, Dr Sander Land (King’s College London)
• Diastolic heart failure has been linked to stiffened myocardium with stiffened titin. By building a mathematical model for titin, implementing experimental observations of titin stiffness changes in relation to pathological observations, and applying to a human cardiac contraction model casted on heart geometries, we have initial observations on the proposed links between titin pathological changes and how these manifest in the whole heart scale.
• Techniques: Mathematical Modelling (ODE), Finite Element Methods, C++, Short presentation

• Group: Theoretical Mechanobiology Group
• Supervisors: Dr Paul Watton, Aikaterini Mandaltsi
• The project uses experimental data to sophisticate the computational model whose results are validated with clinical observations, with the aim to assess the evolution of intracranial aneurysm affected by the local mechanical environments postulated in the computational model.
• Techniques: Mathematical Modelling, Soft tissue mechanics, Computational Fluid Dynamics (in ANSYS-CFX), Fortran, Perl, Conference Poster presentation

• Group: Department of Materials Science and Engineering, The University of Sheffield; Insigneo Institute of in silico Medicine
• Supervisors: Dr Gwendolen Reilly, Stefania Marcotti
• The funded research project aims at optimising the engineering of bone tissues. This involves subjecting MLO-A5 pre-osteocytes to fluid flow on a rocking platform under different regimes, and quantifying for their proliferation.
• Techniques: Mammalian cell culture, alamarBlue Cell Viability Assay, Alizarin Red S Staining, Experiments Planning, Literature Review, Data Analysis

• Group: Center for Computational Imaging & Simulation Technologies in Biomedicine
• Supervisors: Prof Alejandro Frangi, Dr Helena Molina Abril
• The mini research project focused on improving a plug-in for better in silico simulation of a cerebral vascular tree, within Gimias, a medical image analysis software.
• Techniques: C++, Problem solving