Loong Kuan Lee (李隆宽)

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Education

2019–2023
Doctor of Philosophy (PhD), Monash University, Melbourne
2015–2018
Bachelor of Informatics and Computation Advanced (Honours), Monash University, Melbourne, Final Mark - 88/100
Specialised in Computer Science and Statistics & Probability. Graduated with First Class Honours.

Doctoral Thesis

title
Computing Divergences between High Dimensional Graphical Models
supervisors
Geoff Webb, Daniel Schmidt, Nico Piatkowski
year
2023
descripion
We develop a method for computing the joint, marginal, and conditional αβ-divergences, a family of divergences that include the Kullback-Leibler divergence and the Hellinger distance. We then apply this method to modifying the parameters of a decomposable model such that the resulting model is some target amount of divergence away from the original.

Honours thesis

title
Generating Concept Drift by Shuffling Instances
supervisors
Geoff Webb
year
2018
description
We propose a method for shuffling the instances in a dataset such that the divergence between the empirical distribution of the first and second half of the resulting dataset reaches some target amount of divergence.

Experience

Academia

2016–2017
Undergraduate Research Assistant, Monash University, Melbourne
Researched Concept Drift , specifically how to measure and visualise concept drift in both streaming and static data.
Tasks:
  • Developed a system to incrementally measure changes to the probability distributions of a data set over time, using Java and the library Weka.
  • Developed a companion web application for the system above using Scala with the Play Framework, Javascript, and HTML.
  • Used R extensively to visualise results and produce reports.

Industry

2016
Winter Research Project, Agilent & Monash University, Melbourne
Developed application to compare and analyse large groups of timeseries data over the same domain, using R and Shiny.
2017
Software Tester, Carsales, Melbourne
Tested backend APIs being developed to move product from a monolith to a microservice architecture. Helped develop a prototype model to predict car deprecation.
Tasks:
  • Created automated tests for APIs in CI pipeline using Postman, Node.js, and Jenkins.
  • Carried out performance testing of APIs using Scala and Gatling.
  • Managed communication across multiple teams to track bugs reported by consumers of the backend APIs and bugs I found in systems the APIs depend on.
  • Developed prototype to predict the depreciation of a car using R, Azure ML Studio, and Vue.js.

Scholarship & Awards

2015
Faculty of IT International Merit Scholarship
2015
Dean's Achievement Award
2016
Summer Research Scholarship — Faculty of IT
2016
Winter Research Scholarship — Faculty of IT
2017
Information Technology IBL (Industry Based Learning) Placement Scholarship
2017
Dean's Achievement Award
2019
Australian Government RTP (Research Training Program) Scholarship

Publications

Published

Computing divergences between discrete decomposable models
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In Proceedings of the AAAI Conference on Artificial Intelligence, 37(10):12243-12251, 2023.

Analyzing concept drift and shift from sample data
, , , .
In Data Mining and Knowledge Discovery, 32(5):1179-1199, 2018.