Summary .
Associate Professor of Computational Biology at the University of Oxford with over a decade of experience in applying machine learning to genomics ands drug discovery. Published 45+ peer-reviewed papers in journals including Nature, Cell, and Genome Research.
Principal Investigator on £2.5M+ in research grants from UKRI, Wellcome Trust, and European Research Council. Supervised 12 PhD students and 8 postdoctoral fellows.
Experience .
Associate Professor of Computational Biology
Lead research group of 15 members developing ML methods for genomics and drug discovery.
- Principal Investigator on 4 active research grants (£2.5M+ total)
- Published 18 papers since appointment, including 3 in Nature journals
- Supervised 4 PhD students to completion
- Teaching undergraduate and graduate courses in computational biology
Lecturer in Bioinformatics
Established independent research program in ML for genomics.
- Awarded Wellcome Trust Henry Dale Fellowship (£1.2M)
- Published 15 papers including first senior author Nature paper
- Developed MSc course in Machine Learning for Life Sciences
Postdoctoral Fellow
Developed deep learning methods for functional genomics data integration.
- Published 5 papers as first author
- Received EMBO Long-Term Fellowship
Education .
University of Cambridge
PhD thesis on machine learning methods for protein structure prediction, supervised by Prof. Sir Alan Fersht.
Imperial College London
Specialized in statistical genomics and computational methods.
University of Edinburgh
Interdisciplinary degree combining pure mathematics with molecular biology.
Skills .
Computational Methods
Programming & Tools
Certifications .
Fellow of the Royal Society of Biology
Associate Fellow - Higher Education Academy
Languages .
Languages
Awards .
European Research Council Starting Grant
€1.5M grant for project "DeepGenome: Machine Learning for Functional Genomics"
Wellcome Trust Henry Dale Fellowship
Prestigious 5-year fellowship for early-career researchers (£1.2M)
EMBO Young Investigator
Recognition of exceptional young researchers in life sciences.
Best Paper Award
Best paper at RECOMB conference for deep learning in genomics.
Publications .
Deep learning for prediction of drug-target interactions
Novel graph neural network approach for predicting drug-target interactions.
Single-cell multi-omics integration using transformers
Transformer-based architecture for integrating single-cell RNA-seq and ATAC-seq.
Genome-wide association studies meet deep learning
Machine learning in structural biology: a decade of progress
Conferences .
AI for Drug Discovery: From Genome to Clinic
Keynote lecture on our lab's work applying ML to drug target identification.
Deep Learning for Single-Cell Genomics
Invited talk at computational biology's largest conference.
Transformers for Biological Sequence Analysis
References .
Research Interests .
My research focuses on developing and applying machine learning methods to understand biological systems:
- Deep Learning for Genomics: Developing transformer architectures for DNA/RNA sequence analysis
- Drug Discovery: Predicting drug-target interactions and drug response
- Single-Cell Biology: Multi-omics integration and cell type identification
- Protein Structure: ML methods for structure and function prediction
Current funding: ERC Starting Grant, UKRI AI for Science, Wellcome Trust Collaborative Award