معرفی
Dr. Sash Vaid is a tenured Associate Professor of Marketing at McMaster University's DeGroote School of Business and an Affiliate Faculty member with the School of Computational Science and Engineering. He also holds a Research Affiliate position at the University of Michigan, specializing in econometrics and quantitative marketing of dual-use industrial and cyber technologies.
His educational background includes:
- Ph.D. from the University of Houston
- MBA from Duke University
- Graduate studies at Rice University
- Graduate studies at the Institute for the Study of Business Markets, Penn State University
Dr. Vaid's research revolves around a 'Vaid Venn Diagram' intersecting political economy of marketing, dual-use industrial and cyber technologies, and exogenous shocks. He investigates regulatory contexts impacting firms, defense-derived technologies (AI, cybersecurity, geodata) repurposed for commercial use, and macro/micro-level shocks affecting marketing interfaces. His methodology leverages field experiments and programming languages (SQL, Python, Java, C++, CSS, R) to quantify cyber-driven consumption.
Analysis of his 2020-2025 publications reveals strong interdisciplinary trends spanning marketing analytics, executive turnover dynamics, AI applications, and pandemic modeling. His work bridges business, computational science, and public health, with significant focus on B2B executive transitions, lead generation technologies, and Covid-19 infection rate estimation using AI.
No scientific awards were mentioned in the provided text.
Dr. Vaid advises students across undergraduate, graduate, and doctoral programs. He teaches Marketing Analytics (the first undergraduate business course integrating dual-use technology industry), Synthetic Consumer Analytics in the Master of Management in AI and Analytics program, and doctoral courses in Causal Inference. His research is supported by grants including SSHRC funding, and he co-leads development of Canada's 1st Sales & Data Science Lab.
He directs the Vaid Lab for Dual-use Technologies and co-leads the Sales & Data Science Lab at the McLean Centre for Collaborative Discovery, providing students with training in high-performance computing, data visualization, deep learning hardware, and collaborative data analysis systems.
