Co-Instructor/Co-Mentor, October 2019 – Present
Nanyang Technological University

I conduct assignment overview as well as feedback sessions for the Data Science for Earth and Environmental Sciences course (Semester 2). To familiarise the students with using Git, I set up GitHub classroom for assignment dissemination and collection. Besides marking assignments, I also facilitate in-class activities and mentor graduate projects.

Together with David Lallemant, I supervise two Ph.D. students in the Disaster Analytics for Society lab. In addition to research collaboration, we support their personal development through regular meetings and feedback.

Co-Mentor, June 2018 – September 2019
Malaria Atlas Project

Rosalind Howes and I jointly supervised a project undertaken by MAP’s research assistants. This involves comparing the two most prominent malaria parasites, Plasmodium falciparum and Plasmodium vivax in terms of their difficulties in elimination.

Graduate Teaching Assistant, October 2013 – December 2016
Imperial College London

As a Ph.D. student in the Department of Mathematics, I supported the undergraduate as well as graduate teaching through demonstrating in problem classes and marking. In particular, I helped out with the “Probability and Statistics” courses (Year 1 and 2), the Year 3 “Applied Probability” and “Time Series” courses as well as the Master’s Level “Fundamentals of Statistical Inference” and “Probability for Statistics”courses. I also helped out with the Year 2 “Statistical Methods” course at the Department of Computing. Here is a list of some topics covered:

  • Set theory, probability space and laws  (e.g. Theorem of Total Probability, Bayes theorem).
  • Combinatorics.
  • Random variables and distributions: characterisations, families (e.g. the exponential and location-scale families), transformations.
  • Statistical modelling: Hierarchical and mixture models. Stochastic processes e.g. Poisson processes, Markov chains, Brownian motion, ARMA processes.
  • Statistical inference: Different approaches (Bayesian, Fisherian and Frequentist), decision theory,  confidence intervals and hypothesis testing, asymptotic results (e.g. the central limit theorem and convergence results).