I am currently a PhD student (in Statistics) in Quebec City. Previously I worked in a fintech company in Ottawa as a data scientist. I am originally from Singapore and moved to Canada in 2013. I speak English, Chinese and an intermediate level of French. My research interests are in survival analysis, regression analysis, missing data and their applications in the healthcare and financial domain.

- Survival Analysis
- Longitudinal Data Analysis
- Missing data

PhD in Statistics, 2024

Université Laval

Master of Mathematics (Biostatistics), 2018

University of Waterloo

Bachelor of Science (Honors) in Statistics, 2016

Memorial University of Newfoundland

Diploma in Biomedical Science, 2013

Ngee Ann Polytechnic

I am currently taking Thompson River’s Data Structures and Algorithms course. To help me with my understanding, I am also doing Algorithmic Toolbox course on Coursera simultaneously. Short summary of this Coursera course: Insight into different algorithm design paradigm (something which I am weak at), for example, Greedy Algorithms, Dynamic Programming, Divide and Conquer, etc.

From Page 11 of Elements of Statistical Learning
The linear model is given by
$\hat{Y} = \hat{\beta_0} + \sum_{j = 1}^{p}X_j \hat{\beta}_j$
and if we include the intercept (known as bias in machine learning) into the matrix of input vectors (X) (as a constant variable 1), then the linear model can be written in the vector form of inner product :

In classification problems, linear discriminant analysis and logistic regression are methods of establishing linear decision boundaries to delineate data into classes. Separating hyperplanes could also be constructed to classify data and when they are linearly separable, we have a hard margin support vector machine.

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