Navin Seeburn Email and Phone Number

Development of a position estimation algorithm for particle interactions in a dark matter detector, trained on a Monte Carlo simulation of the detector.Statistical analysis and modelling of trends in algorithm behaviour in large noisy datasets. Used cluster computing on a regular basis for large scale Monte Carlo simulation and data analysis.Communicated research outcomes to a 80-member collaboration based in 5 countries at regular collaboration and analysis group meetings, and in written technical reports. This includes heads of the experiment and board members. Communicated research to scientific community at conferences, and to the public at outreach events.Improved position estimation algorithm speed by factor of 40, and integrated it in automated data flow.Designed time series analysis to implement signal timing information in position estimation.Developed dark matter search analysis software which uses analytic statistical functions fitted to signal and noise sources and performs hypothesis testing for signal observation scenarios.

Marked C++ and statistics problem sets for postgraduate course in Statistics and Data Analysis. closely follows the lecturer G. Cowan's book Statistical Data Analysis. Covers from frequentist and Bayesian statistics up to parameter estimation and the use of artificial neural networks. The coursework is equal parts statistical mathematics and C++.Taught problem classes and marked for undergraduate course in Particle Astrophysics. Provided additional curiosities and understanding for undergraduates interested in research in dark matter and cosmology.

Designed and implemented a genetic algorithm which predicts redshifts from light intensity in 5 bands of the visible light spectrum, for galaxies from the Sloan Digital Sky Survey. Contributed to subsequent publication.