Academic Projects

LRMoE

In this project, we develop software packages in Julia and R which implement the Logit-weighted Reduced Mixture-of-Experts model, a flexible framework for insurance loss modelling.

• Badescu, A.L., Fung, T.C., Lin, X.S. and Tseung, S.C. 2021. A better fit: presenting an intuitive and flexible non-linear regression model, The Actuary, pp.24-26. (Magazine article, not peer-reviewed)

• Tseung, S.C., Badescu, A.L., Fung, T.C. and Lin, X.S., 2021. LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model. Annals of Actuarial Science, pp.1-22.

• Tseung, S.C., Badescu, A., Fung, T.C. and Lin, X.S., 2020. LRMoE: an R package for flexible actuarial loss modelling using mixture of experts regression model. Available at SSRN 3740215.

• Presentation: Actuarial Research Conference, 2020

Industry Experience

Applied Data Scientist, Lydia.ai (Aug 2021 – Present):

• Presentation: Opening the Machine Learning Black Box in Regulated Industries, 5th Annual Toronto Machine Learning Summit, 2021 (with Hanieh Arjmand)

Personal Notes

All / Julia / Python / R / Tools / Random

Latest:

• (2022-04-23) Running Code in Parallel
• (2022-04-16) KL Divergence of Normal Distribution and Matrix Algebra
• (2022-04-09) Choosing between .ipynb and .py Files
• (2022-04-02) Creating and Using Reproducible Environments in Julia, Python and R
• (2022-03-27) Inconsistent Package Names in Python: pip install and import
• (2022-02-27) Principal Component Analysis on the US Yield Curve
• (2022-02-04) Book Review: Data Science at the Command Line
• (2022-01-02) Parsing Command Line Arguments in Julia
• (2021-09-06) Building R Packages: In-Use / Access-Denied Error
• (2021-08-24) Sync shared Google calendars