A true expert who inspires confidence.
Always positive and motivating in class.
Makes every class a memorable experience.
Passionate about student development.
Makes learning feel rewarding and fun.
Dr. Lee How Chinh is a Senior Lecturer (Practice) in the Malaysia School of Business at Monash University, specializing in quantitative research that integrates advanced statistical modelling, machine learning, and econometrics to address challenges in finance, economics, and business analytics. His work emphasizes robust and interpretable methods for high-dimensional and heterogeneous data, developing data-driven frameworks that provide actionable insights for industry and public-sector decision-making in areas such as financial risk assessment, market behavior analysis, and evidence-based policy formulation. He holds a Doctor of Philosophy in Statistical Quality Control from Universiti Sains Malaysia, awarded in 2017 for his thesis on new univariate synthetic double sampling X-bar and multivariate synthetic double sampling T^2 control charts; a Master of Applied Statistics from Universiti Putra Malaysia in 2008 on robust least median squares latent root regression; a Master of Science in Dynamical Systems Theory from Universiti Kebangsaan Malaysia in 2006 analyzing Lorenz-like models for Marangoni convection; and a Bachelor of Science (Honours) in Mathematical Sciences in 2001. As an HRD Corp certified trainer and SAS accredited trainer since 2015, he delivers specialized workshops on applied econometrics, machine learning for business analytics, SAS programming, model validation, and data-driven decision frameworks to agencies, companies, and financial institutions.
Dr. Lee How Chinh teaches units including ETW3482 Data Mining and Predictive Modelling, ETM3800 Text Analytics for Business, and ETM5800 Text Analytics for Business, while supervising PhD and postgraduate students on methodologically rigorous, application-driven research. His key publications include 'GraphAT Net: a deep learning approach combining TrajGRU and graph attention for accurate cumulonimbus distribution prediction' (2023), 'Stochastic frontier models with varying frequencies' (2023), 'Virtual reality (VR) in econometrics and analytics: interactive learning of vector geometry and data visualisation' (2024), and 'Phylogeny of cultural heritage in Southeast Asia: a computational analysis of artefact evolution' (2026). He received the Commendation Prize for his PhD thesis in 2018 and the SOB Team Excellence Award on 4 December 2025. Dr. Lee has served as Chief Judge for UTAR DataFest 2021, technical reviewer for conferences such as SCDS2021 and ICMSA 2020, and committee member for ICMSA 2010, and currently leads the project 'Building a Data-Driven Organisation with Data Warehouse, Machine Learning and Artificial Intelligence for the Habib Group' as Chief Investigator.