Résumé

Professional Experience

Technical Analyst – Machine Learning & Complex Systems

Imperial College London, U.K. (2023 – Ongoing)

• Championed evidence-based advocacy, influencing global policies on malaria prevention strategies.
• Engineered machine learning algorithms, focusing on Gaussian Processes and Neural Network emulators, to study malaria dynamics in Africa.
• Led research projects emphasizing statistical evaluations of bed net strategies for malaria control, revealing potential improvements via data-driven approaches.

Research Scientist – Machine Learning & Applied Mathematics

German Centre for Artificial Intelligence (DFKI), DE (2022 – 2023)

• Spearheaded research using Deep Neural Networks for complex system modelling, including predictive tumour growth tools using Deep Neural Universal Differential Equations.
• Extended machine learning tools to model criminal behaviour and gun violence dispersion.
• Advocated and practiced AI ethics and safety in all research dimensions.
• Enhanced modelling techniques using open-source frameworks like SciML and TuringLang.

Research Assistant – COVID-19 Real Time Modelling

Imperial College London, U.K. (2021 – 2023)

• Awarded the “SPI-M-O Award for Modelling and Data Support” presented by the UK Government’s Chief Scientific and Medical Officers.
• Produced comprehensive weekly reports for SPI-M-O & SAGE during the COVID-19 pandemic.
• Developed models for infectious disease transmission.
• Supervised open-source package developments, ensuring rigorous statistical consistency.

MRC GIDA Internal Seminar Series Co-Organiser

Imperial College London, U.K. (2023 – Ongoing)

• Orchestrated weekly seminar events, promoting intellectual discussions among department members.

Education

MSc Epidemiology

Imperial College London, U.K. (2020 – 2021)

• Notable Modules; Advanced Regression, Bayesian Modelling for Spatial and Spatio-temporal Data, Research Methods, Introduction to Statistical Thinking and Data Analysis, Further Methods in Infectious Disease Modelling, Outbreaks
• Dissertation Project; “Investigating co-existence dynamics of plankton biotic resistance on Batrachochytrium dendrobatidis as a food-web based ecological management strategy for mountainous aquatic ecosystems.”

BSc Mathematics with Economics

Aston University, U.K. (2015 – 2019)

• Notable Modules; Statistical Machine Learning, Partial Differential Equations, Numerical Methods (I & II), Mathematical Algorithms
• Dissertation Project; “From Concept to Simulation: Designing and Conducting CFD Analysis on Auger Reactors through Granular and Volumetric Approaches”

Technical Skills & Languages

• Programming: R, Python, Julia, C#/C++, SQL, Matlab, SAS, Latex
• Research Software: PyTorch, TensorFlow, Keras, RStan, Julia SciML, TuringLang
• Software Management: Git Version Control, Bash Shell Scripting (Linux/Unix)
• Languages: Fluent in English & Spanish, Working proficiency in German & French

Publications & References

• Publications:
  • Imai, N., Rawson, T., et. al., Quantifying the impact of delaying the second COVID-19 vaccine dose in England.
  • Perez-Guzman, P. N., Knock, E., et. al., Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England.
• Manuscripts in Preparation:
  • “Applying Neural Network Emulation to Assess the Impact of Pyrethroid-Pyrrole Bed Nets on Malaria in Africa.”
  • “Applying Deep Neural Universal Differential Equations: A Novel Approach for Tumour Volume Growth in Complex Mathematical Systems.”
  • “Investigating Parameterisation and Inference Trade-Offs in Stochastic and Deterministic Models.”
• Software & Tools:
  • siremu; A (WIP) Python Package for Epidemic Simulation and Emulation utilising advanced Neural Network architectures.
  • sircovid; Tools to perform Bayesian analysis of complex stochastic models using adaptive Metropolis-Hastings and particle MCMC.
  • spimalot; The models in this package can be used to estimate key epidemic parameters and predict the course of the epidemic under different intervention scenarios.
  • MCState; Allows users to infer parameters for stochastic, compartmental models from data, using Monte Carlo methods.

Conferences & Presentations

• Upcoming Conferences:
  • 9th International Conference on Infectious Disease Dynamics, Bologna, Italy, November 2023
    • “Evaluating improved malaria control by switching to pyrethroid-pyrrole insecticide treated nets in Africa”
    • “Investigating Parameterisation and Inference Trade-Offs in Stochastic and Deterministic Epidemic Models”
• Past Presentations:
  • MRC GIDA Seminars, London, U.K., December 2022
    • Topic: Parameterisation and Inference in Epidemic Models

Teaching

• Imperial College London, Department of Infectious Disease Epidemiology

  • Advanced Regression

  • Outbreaks

• Aston University, School of Engineering and Applied Science

  • Vector Algebra & Geometry

  • Numerical Methods II

  • Numerical Methods I

  • Multivariate Calculus

  • Foundation Mathematics

Voluntary & Leadership

• Authored ‘30 Machine Learning Algorithms in 30 Days’, an interactive educational series, 2023 – Ongoing
• MSc. Epidemiology Graduate Teaching Assistant, Imperial College London, U.K., 2021 – 2022
• NHS COVID-19 Clinical Vaccinator, NHS England, U.K., 2021
• Lay Grant Reviewer, Parkinson’s UK, U.K., 2019 – Ongoing
• BSc. Mathematics Undergraduate Teaching Assistant, Aston University, U.K., 2017 – 2019
• Engineering and Applied Sciences Research Assistant (Year in Industry), Aston University, U.K., 2017 – 2018