In the following table, contact information relevant to the page. The first column is for visual reference only. Data is in the right column.
|Organisation:||University of Warwick|
|Tags:||Fellowship: Established Career Fellowship, Researcher, University of Warwick|
|Related theme:||Engineering Healthcare technologies ICT Mathematical sciences|
Mark is an Established Career Research Fellow, previously an Advanced Research Fellow, Director of the EPSRC Network on Computational Statistics and Machine Learning, Fellow of the Royal Society of Edinburgh, and recipient of a Royal Society Wolfson Research Award. He is currently an Executive Director of the Alan Turing Institute.
The vision of this research is to formalise the geometric foundations of computational statistics and provide the tools and analytic results required to realise the ambition of developing the advanced statistical methodology that is essential to address emerging inference problems of major importance across the sciences and industry. As ever more demanding and ambitious applications of existing statistical inference methods are being considered, the capabilities of computational statistics tools are constantly being stretched, often beyond what is practically feasible.
For example, the potential to gain insights into the mechanisms of cellular function, elucidating ecological dynamics; improving neurological diagnostics, and uncovering the deep mysteries of the cosmos are only some of the ongoing scientific studies that are heavily reliant on statistical inference methods and are placing unparalleled demand on the current capabilities of available statistical methodology. This situation motivates continual innovation in the development of statistical methods for the quantification of uncertainty.
The aim of this proposed research is to be more ambitious and go much further in establishing a novel paradigm that underpins the advancement of next generation computational statistical methods by formalising and developing advanced Monte Carlo methods. The geometric foundations of computational statistics will be formalised within this proposed research in a way that reaches beyond traditional interfaces between statistical and mathematical sciences.
Motivation to Apply
I had just completed an research in Computational Statistics, which made novel connections between non-Euclidean geometry, dynamical systems and Markov chain Monte Carlo theory. Given the opportunities, excitement and whole new research agenda this was producing then it made sense for me to apply for an Established Career Research Fellowship to lead and drive this new research agenda.
Career benefits of Fellowship
My academic career is unusual in that after my degree I joined IBM and spent ten years with them before electing to do a PhD. I therefore missed out a decade of experience and development in my career so a fellowship provided me the opportunity to, as it were, catch up. This period of time and the freedom the fellowship provided me to undertake research was enormously productive. It catapulted my career and it was clear that a Leadership Fellowship would then further establish me as a research leader on the international stage, this has been the case so far.
Advice for future applicants
It can be daunting when considering applying for such a competitive and prestigious fellowship. However my advice is that if you have a vision and a clear research agenda that would benefit from such a fellowship is to focus on the process of formally describing your research aims and goals.