Note: I am a United States Permanent Resident with full work authorization and no need for employer sponsorship.
About Me
Welcome to my academic webpage!
I am a Ph.D. candidate in Information Systems at the DeGroote School of Business, McMaster University, advised by Professor Milena Head. My research examines socio-technical challenges at the intersection of human–AI interaction, cybersecurity, and digital misinformation, focusing on human information processing, trust formation, and decision-making under uncertainty. To investigate these issues, I employ a multi-method empirical approach, leveraging behavioral experiments, NeuroIS, and computational data science to develop theory-driven insights that guide trustworthy technology design and strengthen user resilience to digital deception. I analyze this data using advanced statistical methods for hypothesis testing and computational techniques, including machine learning and explainable AI (XAI) for pattern recognition. I welcome discussions about research, teaching, and collaboration opportunities.
At McMaster Digital Transformation Research Centre (MDTRC), I have conducted experimental and data-driven studies in consumer behavior, digital user experience (UX) analytics, and human–computer interaction (HCI), integrating behavioral and neurophysiological data. I have also contributed to developing grant proposals, securing external research funding, supporting industry partnerships, and mentoring MBA and undergraduate students. My data science research experience at Capital One has also provided me with practical exposure to the governance and deployment of AI and machine learning in risk modeling and fraud detection. These experiences have strengthened my research leadership, methodological rigor, and interdisciplinary collaboration capabilities.
Research Interests
Topic:
Human-AI Interaction, Cybersecurity, Digital Misinformation, AI Governance & Ethics, Aging and Digital Divide, Behavioral Information Systems, Human-Computer Interaction (HCI), Computational Data Science
Methodology:
Behavioral Experiments, Quantitative Analysis, Survey Research, Statistical Modeling, Applied Econometrics, Causal Inference, Explainable AI (XAI), Interpretable Machine Learning, Content Analysis and Text Mining, NeuroIS (Eye-Tracking & Physiological Measures)