Research Funding Awards & Fellowships

I have raised over US $950'000 in external and international funding with projects active through to 2025.  My research has been supported by the US National Institutes of Health (NIH) under the National Institute of General Medical Sciences (NIGMS), National Center for Advancing Translational Sciences (NCATS), National Cancer Institute (NCI), and the Physical Sciences in Oncology Network (PSON) as well as the Government of Canada.

Active Projects:

Role: PI; Funding amount: approx. €120'000; Funding period: 2021-2025.

I was awarded an Imperial–TUM Joint Academy of Doctoral Studies grant to recruit two PhD students (one to be enrolled at Imperial; and one at TUM) both to be co-supervised by myself together with Mathias Drton.  The project will develop novel methods to learn discrete geometric structures from high dimensional data, and then do statistical analysis with sets of these discrete geometric structures.  Particular examples of these structures that we will focus on are phylogenetic trees, graphs and networks, and graphical models.  We will apply our methods to synthetic, public, and proprietary biological data pledged by the Haematopoietic Stem Cell Laboratory at the Francis Crick Institute.  Roan Talbut and Daniele Tramontano are the PhD students who will be working with us on this project.

Role: Lead Supervisor; Funding amount: £124'000; Funding period: 2021-2025.

I was awarded full funding by The Cancer Research UK Imperial Centre for one PhD student to be trained at the Modern Statistics and Machine Learning Centre for Doctoral Training at Imperial College in computational methods in cancer biology.  The project aims to model and predict brain cancer phenotypes from molecular and biomedical imaging data using topological data analysis.  The PhD will be jointly supervised by myself and Matthew Williams (co-PI).  Qiquan Wang is the PhD student who will be working with us on this project.

Past Projects (Incomplete List)

Role: Co-Investigator; Funding amount: €8,100; Funding period: July 2021-June 2022.

Mathias Drton (PI), Kaie Kubjas (co-I) and I were awarded a TUM Global Incentive Fund award to establish collaborations among our three research groups and further methodological and theoretical developments using algebraic methods in statistical and machine learning contexts.  We are doing this via a series of workshops, including an initial foundational event online on 22-23 November 2021 and have an upcoming online data camp and hackathon planned for 10-14 January 2022 where we will focus on the applications of techniques from topological data analysis, numerical algebraic geometry and algebraic statistics to real data.  Finally, we will be meeting in person in Munich from 28 March-4 April 2022 to work on methodological and theoretical developments.

Role: PI; Funding amount: £7'000; Funding period: January-December 2021.

I was awarded a collaboration grant by the Technical University of Munich and Imperial College Collaboration Fund to study change point detection using algebraic statistics.  The project will fund one UROP that will take place online in the summer of 2021; Isaac Moselle is an undergraduate student in Mathematics at the University of Cambridge who will be working with me on this project.  The award is joint with Mathias Drton (co-PI) and Carlos Améndola (co-I).

Role: Co-Investigator; Funding amount: CAD $250'000; Funding period: 2019-2021.

Leandro Sanchez (PI), Primoz Skraba (co-PI), Benoît Fournier (co-I) and I were awarded the New Frontiers in Research Fund (NFRF): Exploration Stream by the Canada Research Coordinating Committee (CRCC) and the Government of Canada via the Social Sciences and Humanities Research Council (SSHRC) to apply methods from topological data analysis and develop deep learning algorithms to detect, diagnose, and prognosticate damages in concrete infrastructure.   Stéphane Béreux worked with me on this project during his MSc (M2) student in Data Science at the École Polytechnique.
This project received the maximum fundable amount in a competition where only 12% of applications were successful.