Research
RESEARCH SUMMARY
My work focuses on building Domain Intelligent Digtial Twins and my independent research interests revolve around AI, consumer behavior and education. I work on physics-aware machine learning at Congruence Systems where we build models that utilize known information about system dynamic operating conditions to accurately reflect real-world system operations. My independent research investigates consumer behavior, techonology adoption using data analytics and machine learning to gather insights from data.
Publications
Feature importance analysis using ensemble decision tree models for aspirational luxury purchase intention of young cohorts in emerging market
Millennial and Gen Z consumers in emerging countries form the biggest growth market for luxury businesses. While extant luxury research treats them as a homogenous unit, the Generational Cohort Theory proposes segmenting young consumers based on demographics. Using a conceptual framework developed through an extensive literature review, Random Forest regressors and Gradient Boosted Regression Trees are used to study the feature importances of the factors that drive Consumer Luxury Value and Luxury Purchase Intention. Ensemble tree methods are capable of capturing nonlinearities within the model and leverage the explainability of their decision tree learners to provide insight into the feature importances. Consumer Luxury Value, Other-directed and Experiential values are the most significant predictors of Luxury Purchase Intention across all gender and demographic cohorts with specific differences between cohorts. Gen Z consumers display stronger Other-Directed and Cost Perception values. Cohort-specific findings show Gen Z luxury consumers are more collectivistic than Millennials.
The impact of the COVID-19 pandemic on e-learning adoption in an emerging market: a longitudinal study using the UTAUT mode
Congruence Systems
Congruence Systems is currently building the Dynalitix platform, Domain Intelligent Digital Twins for Industrial and Physiological Systems at scale. At present, we are engaged in filing patents to protect our IP that advances Congruence's vision of physics-aware, dynamics captured machine learning as well as building the MVP for the platform. The platform enables Retrieval Augmented Forecasting of systems that produce sequential or time series signals attuned to identified system operating conditions. Our proprietary IP enables the building of an operating envelope that reflects real-world system operating conditions. Further, we are also developing IP that allows for efficient time-series forecasting of industrial and physiological signals grounded in Koopman Operator Theory.