I am a mathematical data scientist. I study the randomness of algebraic structures, and randomness in algebraic settings.

In my research, I work with complex data structures and I develop methods based on algebra (notably algebraic geometry and topology, and more generally, nonlinear algebra [PDF]) advance statistical methodology and machine learning algorithms. Some examples of data that I have worked with are spatial data, medical images, trees, networks, and higher-dimensional simplicial complexes. My methods are constructed from pure mathematics to accommodate modern data scientific challenges, and I have used them to analyze real data and solve concrete problems that arise in computational and mathematical biology. My exposure to biological problems has also inspired my theoretical research in mathematics, statistics, and machine learning.

Affiliations at Imperial:

• Applied Mathematics and Mathematical Physics

• Artificial Intelligence Network

• Biomathematics Group

• Machine Learning Initiative

• Mathematics in Medicine

• Mental Health Research Network

• Statistics

Some Professional Highlights:

• I have been awarded over $950'000 USD in research funding as PI and co-PI, and within one year of joining Imperial, I was awarded 4 grants. You can find out more about my funded projects on my Funding page.

• I was featured as a Nonlinear Algebra Researcher at the Max Planck Institute for Mathematics in the Sciences

• I have had six proposals accepted to host 5-day workshops at the Banff International Research Station