Profile
Rodrigo Belo is Associate Professor at the Department of Technology and Operations Management at Rotterdam School of Management of the Erasmus University.
Rodrigo's research focuses on the effects of information systems on organizations and on the impacts of social network structures and peer influence on consumer behavior. His work has been published in top journal and peer-reviewed conferences such as Management Science, Marketing Science, and MIS Quarterly. Rodrigo has lead and collaborated in multiple projects with established firms and startups in the online and telecommunications sectors. His engagements include the design and deployment of large-scale real world randomized experiments to assess the effectiveness of marketing campaigns and to optimize online user engagement.
Rodrigo holds a PhD in Technological Change and Entrepreneurship from Carnegie Mellon University, an MSc in Engineering and Public Policy from Carnegie Mellon University, and a BSc in Computer Science and Engineering from Instituto Superior Técnico, University of Lisbon. Before joining the academia Rodrigo worked as a software engineer and analyst in the transportation and government sectors.
Publications
Article (3)
Academic (3)
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Ghiassi-Farrokhfal, Y., Crisostomo Pereira Belo, R., Hesamzadeh, M. R., & Bunn, D. (2023). Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators. Manufacturing and Service Operations Management, 25(5), 1966-1983. https://doi.org/10.1287/msom.2021.0555
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Belo, R., & Li, T. (2022). Social Referral Programs for Freemium Platforms. Management Science, 68(12), 8933-8962. https://doi.org/10.1287/mnsc.2022.4301
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Li, T., & Crisostomo Pereira Belo, R. (2022). To Make a Profit, Dating Apps Must Leverage Data Differently. Harvard Business Review. https://hbr.org/2022/01/to-make-a-profit-dating-apps-must-leverage-data-differently
Conference proceeding (2)
Academic (2)
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Wan, C., Belo, R., Zejnilović, L., & Lavado, S. (2023). The Duet of Representations and How Explanations Exacerbate It. In L. Longo (Ed.), Explainable Artificial Intelligence - 1st World Conference, xAI 2023, Proceedings: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part II (1 ed., pp. 181-197). Springer Cham. https://doi.org/10.1007/978-3-031-44067-0_10
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Wan, C., Crisostomo Pereira Belo, R., & Zejnilović, L. (2022). Explainability's Gain is Optimality's Loss? — How Explanations Bias Decision-making. In AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 778-787). Association for Computing Machinery. https://doi.org/10.1145/3514094.3534156
Courses
Past courses
Big Data Management and Analytics
- Study year: 2023/2024
- Code: BM04BIM
