Heterogeneous Insurance Risk with Random Effects
In this project, we propose a flexible regression model that incorporates random effects to account for heterogeneous risks observed in large insurance portfolios. This is still work in progress, and more is to come!
LRMoE Software Packages
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.
Sr Applied Data Scientist, Lydia.ai (May 2022 – Present):
Junior position as Applied Data Scientist from Aug 2021 to Apr 2022
Presentation: Opening the Machine Learning Black Box in Regulated Industries, 5th Annual Toronto Machine Learning Summit, Nov 18, 2021 (with Hanieh Arjmand)
Presentation: How to create systematically sampled datasets that solve business problems, TMLS Machine Learning in Finance & Insurance Micro Summit, May 11, 2022 (with Hadi Moghadas)
All / Julia / Python / R / Tools / Random
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