**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).