Samuel Whitaker Email & Phone Number

York, England, United Kingdom

Newcastle-Upon-Tyne, England, United Kingdom

Title: Fast and efficient Bayesian inference for complex epidemic modelsDeveloped complex, yet computationally efficient, statistical inference schemes used on large-scale disease outbreak data. Inferred model parameter values used to generate accurate forecasts of future values of the epidemic. Applications of the methodology included Ebola and Covid‑19.• Developed a Sequential Monte Carlo (SMC) scheme for mass action stochastic epidemic models with time varying infection rates, methodology tested on synthetic data and applied to two real data sets;• Methodology and applications prepared and presented to statistics seminar group consisting of colleagues and peers, and at Newcastle University’s Postgraduate Conference 2021;• Further developments included a Linear Noise Approximation (LNA) suitable for surveillance data;• Co-authored; “Accelerating Bayesian inference for stochastic epidemic models using incidence data” (Published 12th October 23 in Statistics and Computing Journal). Currently writing; “Fast and efficient sequential Bayesian inference for stochastic epidemic models”.Key Skills• Advanced programming skills highlighted by the development of code for complex inference schemes using a variety of languages, including R and Python;• Strong communication and presentation skills with the ability to portray novel and complex mathematical ideas to a non-specialist audience, developed through numerous seminar and conference talks;• Ability to provide insight into complex data sets using innovative adaptations of existing methodologies;• Experience working in a collaborative team, both in-person and remotely, generating ideas through whiteboarding sessions and online collaborative tools;• Project organisational skills, including maintaining high levels of security and version control using industry standard tools such as GitHub. Delivering timely project milestones for reviews and presentations throughout the project.

Newcastle Upon Tyne, United Kingdom

Mid‑PhD lecturing opportunity within the School of Mathematics, Statistics and Physics. Responsible for creating, managing and delivering a multitude of statistics modules for students reading all years including postgraduate students. Students were assessed and graded to the university’s standard.• Created engaging material for multiple modules to be delivered in lectures, with complementary lecture notes and accessible online resources;• Managed a large and varied workload, delegating appropriate tasks to postgraduate students. Maintained an excellent standard via regular reviews with markers;• Designed examination material, working closely with senior lecturers, determined the assessment criteria and moderated the results;• Received excellent evaluations from both students and colleagues commenting on the level of teaching and materials, with special mentions from senior lecturers and head of Statistics.Key skills• Collaboration skills used whilst working with a variety of people, ranging from students to senior lecturers, to provide module material which was both engaging and accessible;• Organisational skills used to manage a large cohort of students, ensuring routine administrative tasks completed alongside lecture preparation and additional student support;• Demonstrated a high level of understanding of complex mathematical material by being able to convey difficult concepts in a variety of ways, both in lectures and in one-to-one support sessions;• Ability to manage a large workload whilst maintaining an excellent standard of work.

Doctor Of Philosophy - Phd, Statistics

Developed complex, yet computationally efficient, statistical inference schemes used on large-scale disease outbreak data. Inferred model.

Master'S Degree, Mathematics And Statistics, 1:1

A 4 year integrated masters course comprising core mathematics modules and advanced statistics modules. Years 1 and 2 also included some.