Jamie Scott

I am a PhD candidate in Statistics at Imperial College London, supervised by Professor Axel Gandy and funded by an EPSRC scholarship. Prior to the PhD, I received an MRes from UCL, an MSc in Statistics from Imperial and an MA from the University of Cambridge.

My current research involves developing methodology for inference in complex networks. See my research interests here.

location_on 526 Huxley Building
Department of Mathematics
Imperial College London, SW7 2AZ
james.scott15 "at" imperial.ac.uk



*Please email for a full length CV.

Education

  • PhD Statistics, Imperial College London (2017-2021)

  • MRes Financial Computing, University College London (2016-2017)

  • MSc Statistics, Imperial College London (2015-2016)

  • MA Economics, Kings College, University of Cambridge (2011-2014)

Experience

  • Quantitative Research Associate Intern J.P. Morgan, London, UK (July-Sep 2019)

  • Visiting Fellow in Statistics Harvard University, Cambridge, MA (Jan-June 2019)

  • Structured Credit Intern MSPA, London, UK (June 2016 - Dec 2018)

Papers & Preprints

Bold indicates first author, otherwise alphabetic authorship

  • Gandy A. and Scott J. (2020), 'Unit Testing for MCMC and Other Monte Carlo Methods,', arXiv:2001.06465 [stat.ME].

  • Scott J. and Gandy A. (2019), 'State Dependent Kernel Selection for Conditional Sampling of Graphs,', Accepted for publication, Journal of Computational and Graphical Statistics.

Awards

  • Francis Warner Dissertation Prize, awarded for MSc Statistics thesis 'Advancing State of the Art Hamiltonian Monte Carlo'.

  • EPSRC Scholarship 4-year stipend for PhD research

Computational Skills

  • C++, R, Python, Julia. OOD, unit testing, version control, packaging.