Teaching

Now

• Adjunct Lecturer, since March 2022

  NUS Institute of Systems Science

  AI in Financial Services and Responsible AI for graduate and executive cohorts.

• Mentor and industry speaker

  NUS Master of Science in Business Analytics

  Mentoring students, and in 2025 the opening industry seminar for the incoming cohort.

• InsurTech mentor

  Plug and Play APAC

  Working with insurance technology startups on data and AI.

• MS and PhD

  Decision Making Under Uncertainty

  Markov chains, Markov decision processes, reinforcement learning, game theory and Nash equilibria

• Undergraduate, MS, and PhD

  Operations Research II

  Random variables, Markov chain modeling, Markov decision processes, queueing

• MS and PhD

  Engineering Statistical Analysis

  Hypothesis testing, confidence intervals, linear, piecewise, and logistic regression in Excel and R

• Graduate

  Stochastic Optimization

  Optimization under uncertainty

• Undergraduate

  Statistics for Engineers

  Probability, distributions, hypothesis testing, confidence intervals

• Undergraduate

  Operations Analysis

  Inventory control, scheduling, forecasting, queueing

Industry programs

• Singapore FinTech Association, FinTech Talent Program

  Big Data Analytics and Machine Learning in FinTech, a module taught to three cohorts.

• Industry training

  Supervised and unsupervised machine learning, text analytics, predictive analytics, and Watson Analytics for practitioners.

• Experfy

  Analytics for the Internet of Things, an online course.

• National University of Singapore
• Singapore Management University
• IIT Kharagpur, ISI Kolkata, and IIM Calcutta, inaugural address for the PG Diploma in Business Analytics
• University of South Florida, Del and Beth Kimbler Lecture Series
• Korean Actuarial Society

1. Introduction to Markov Decision Processes
2. Solving Markov Decision Processes using Discounted Reward Value Iteration
3. Probability Mass Functions
4. Cumulative Distribution Functions
5. Expected Value and Variance of Random Variables
6. Discrete Probability Distributions
7. Continuous Probability Distributions: the Exponential Distribution
8. Basics of Hypothesis Testing
9. Hypothesis Testing, Type 1 and Type 2 Errors
10. Examples of Type 1 and Type 2 Errors and the Power of a Test
11. A 7-Step Procedure for Hypothesis Testing
12. Hypothesis Testing Procedure, with an Example