Pantelis Loupos
Assistant Professor of Marketing and Business Analytics
University of California, Davis Graduate School of Management
I study how networks and algorithms shape behavior and performance on digital platforms.
My research combines causal inference, econometrics, machine learning, and large-scale platform data to examine how users influence one another, how network structure predicts customer behavior and value, and how firms should design policies and algorithmic systems.
Much of my work focuses on peer-to-peer and networked markets, including fintech, social payments, crypto ecosystems, and digital communities. Across these settings, I study adoption, engagement, fraud, recommendation systems, demand allocation, and long-run growth.
A central premise of my work is that customers are not independent decision-making units. Their behavior and value emerge from interactions among users, network structure, and platform algorithms. I develop empirical and analytical frameworks to understand these interdependencies and design policies that improve both customer-level and system-level outcomes.
Research Areas
Networked Digital Platforms
I study digital platforms where users are connected, behavior is visible, and value depends on social structure. My work examines peer-to-peer fintech platforms, social payment systems, digital communities, and crypto ecosystems to understand adoption, engagement, customer value, and platform risk.
Causal AI and Network Analytics
I develop and apply causal inference, machine learning, graph neural networks, and econometric methods for settings where users influence one another. A central theme in my work is how to estimate causal effects and predict behavior when outcomes are shaped by network spillovers, peer effects, and endogenous platform activity.
Platform Strategy and Decision Systems
I study how firms should use platform data to make better decisions about growth, retention, risk detection, recommendation ranking, and resource allocation. This stream connects academic research with practical problems in customer analytics, fintech, digital strategy, and AI-driven decision-making.
Selected Publications and Current Papers
- Fighting Fire with Fire: Infusing AI into Peer Review to Sustain Quality Scholarship. Joint work with Hemant Bhargava et al. Management Science, 2026.
- Social Drug Dealing: How Peer-to-Peer Fintech Platforms Have Transformed Illicit Drug Markets. Joint work with Jörn Boehnke and Ying Gu. Annals of Operations Research, 2023.
- What Reviews Foretell about Opening Weekend Box Office Revenue: The Harbinger of Failure Effect in the Movie Industry. Joint work with Yvette Peng, Sute Li, and Hao Hao. Marketing Letters, 2023.
- Graph Neural Networks for Causal Inference Under Network Confounding. Joint work with Michael Leung. Revise and resubmit at The Review of Economic Studies.
Teaching
My teaching focuses on how data, analytics, and AI can improve managerial decision-making. In the MBA program, I teach core Operations Management and Customer Analytics. In the MSBA program, I teach Big Data Analytics, covering modern data systems, machine learning, deep learning, network analytics, Spark, Kafka, graph neural networks, and large language models. I am also a certified NVIDIA instructor in deep learning.
Contact
Pantelis Loupos
Graduate School of Management
University of California, Davis
Email: ploupos [at] ucdavis [dot] edu