Visualization of T Cell Migration in the Spleen Reveals a Network of Perivascular Pathways that Guide Entry into T Zones.
Chauveau A. et al, (2020), Immunity
Author Correction: Diverse and robust molecular algorithms using reprogrammable DNA self-assembly.
Woods D. et al, (2019), Nature, 572
Correction: Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.
Zhou FY. et al, (2019), eLife, 8
A deep learning framework for quality assessment and restoration in
Ali S. et al, (2019)
Diverse and robust molecular algorithms using reprogrammable DNA self-assembly.
Woods D. et al, (2019), Nature, 567, 366 - 372
Developments in next-generation sequencing, high-content image screening and high-resolution microscopy techniques have led to an unprecedented data explosion in biology with vast potential for pursuing unbiased, systematic understanding of complex biological systems on the systems-level rather than limited to particular molecules or pathways. However, a major impediment is the lack of systematic easy-to-use data analysis tools that can extract and condense the salient information from individual datasets suitable for integrative analysis and hypothesis generation across different types of data e.g. genetics and imaging.
The overall goal of my research is to develop quantitative platforms that provide computational methods to enable the generation of unique informative signatures that 'barcode' the analysed biological phenomena of interest. Critically, the signatures can be used for extensive applications such as phenotype clustering, phenotype discovery and integration with other types of data e.g. integrating imaging signatures with genetics signatures without requiring the original dataset, and these signatures should ideally be interpretable biologically. In doing so, my work aims to generate more mechanistic understanding of fundamental biological processes, improve early detection of cancer and enhance quantitative phenotypic screens for more targeted development of drugs for patients.
To build such systems my research exploits methods across numerous quantitative disciplines, including signal processing, computer vision, deep learning and mathematical modelling.
Figure: Mapping the motion phenotypes in videos of different oesophageal cell-line interactions in vitro to predict the effect of treatment by applying principal component analysis on motion signatures computed using Motion Sensing Superpixels (MOSES).
Examples of current projects:
1) Development of quantitative organoid screens for personalised medicine
2) Quantitative analysis of clinical endoscopy videos for early detection and enhanced reporting of oesophageal cancer.
3) Host-pathogen interaction of Epstein-Barr virus in Nasopharyngeal Carcinoma
4) Quantitative understanding and phenotyping of migration in early mouse embryonic development.
5) Understanding the spatial-temporal organisation of centriole and centrosomes with respect to the cell cycle.
For my publications please see my Google Scholar page:
Previously I read Engineering at the University of Cambridge where I obtained my BA and MEng degrees with specialisation in electrical and information sciences. My Master's thesis was on DNA nanolithography on graphene. During this time I also spent time in Caltech (California Institute of Technology) as a summer undergraduate research fellow (SURF) working on DNA computing supervised by Profs. Damien Woods (University of Maynooth), David Doty (UC Davis) and Erik Winfree (Caltech).