معرفی
Ryan Dew is an Assistant Professor of Marketing and the Govil Family Faculty Scholar at the Wharton School of the University of Pennsylvania. He is affiliated with the Wharton AI and Analytics Initiative, Wharton Human-AI Research, and the Penn Eidos LGBTQ+ Health Initiative.
His research lies at the intersection of marketing, data science, and machine learning. Key areas include marketing analytics, Bayesian statistical methodologies, customer preference measurement, and data-driven design. He leverages techniques such as Bayesian nonparametrics, deep generative models, and representation learning to develop interpretable tools for customer analytics and branding.
The recent publications reflect a strong trend in applying probabilistic machine learning to marketing problems, especially in using unstructured data (text, images) for preference modeling, influencer marketing analytics, and AI-enhanced design. His work bridges theoretical rigor with practical applications in digital and customer-centric marketing.
Scientific Awards:
- 2022 Frank M. Bass Award
- 2018 INFORMS Doctoral Dissertation Award
- 2018 ASA Marketing Section Doctoral Research Award
- Finalist, 2019 Frank M. Bass Award
- Finalist, 2020 Paul Green Award
- Finalist, 2022 John D.C. Little Award
- 2023 MSI Young Scholar
- Wharton Govil Family Faculty Scholar (2024–2025)
- Wharton Teaching Excellence Award
Ryan Dew advises PhD students and collaborates widely across institutions. He co-organizes an interschool virtual reading group on probabilistic machine learning for junior faculty and PhD students. His research has been supported by recognition from leading marketing science organizations and has direct implications for data-driven decision-making in branding, CRM, and digital marketing. He teaches courses such as Data and Analysis for Marketing Decisions and Empirical Models in Marketing.
He is actively involved in research groups including the Wharton AI and Analytics Initiative and Wharton Human-AI Research, where he contributes to advancing the integration of AI into marketing science and practice.
