Teaching

Current teaching

Courses

I’m currently an instructor for:

  • the Statistics & Analysis and Business Analytics (Visual & Predictive Techniques) courses at the Nanyang Business School
  • the Linear Algebra for Computing course at the School of Computer Science and Engineering, Nanyang Technological University

Former teaching

Lecture courses

As a co-instructor for the ‘Data Science for Earth and Environmental Sciences’ class at the Asian School of the Environment, Nanyang Technological University, Singapore, I:

  • Developed and taught time series analysis (with theory and application in R) and LaTeX.
  • Coordinated the project component of the course and mentor several student projects.
  • Set up GitHub classroom for disseminating and collecting assignments.
  • Led assignment overview and feedback sessions.
  • Facilitated fun, educational activities with the rest of the teaching team. For 2021 and 2022, we conducted a music critic competition based on Spotify API data and generated our very own:
class of 2021 playlist
class of 2022 playlist

I have also taught a short lecture on the “The Value of Data in Design” for the Nanyang Business School, Singapore. The slides are available here:

THE VALUE OF DATA IN DESIGN

Project supervision

  • Undergraduate research modules including Final Year Projects (FYPs).
  • PhD supervision in research lab and Data Science class.

Teaching assistant

As a graduate teaching assistant at Imperial College London during my PhD (2013-2017), I taught Mathematics undergraduate and Year 2 Computing students in Probability and Statistics problem classes. I also marked assessed work for Year 3 and M.Sc. courses, and invigilated examinations. Topics covered in the classes include:

  • 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).

Mentoring

During my post-doc at the University of Oxford (2017-2019), I supervised research assistants on a project comparing the rates of decrease in malaria across species and countries.