معرفی
Panagiotis G. Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also affiliated with the Center for Data Science and the Computer Science department at NYU. His work bridges computer science, data science, and business analytics, with a focus on integrating human and machine intelligence.
- PhD in Computer Science, Columbia University (2004)
- MSc in Computer Science, Columbia University (2001)
- BSc in Computer Engineering & Informatics, University of Patras, Greece (1999)
His research centers on crowdsourcing, human-AI collaboration, online labor markets, and social media analytics. He is widely recognized as a pioneer in human computation and has developed foundational techniques for ensuring data quality in crowd-sourced environments. His interdisciplinary work combines insights from computer science, economics, and social psychology to model user behavior and improve decision-making systems.
His recent publications explore algorithmic fairness in hiring, theoretical voting models in crowdsourcing, and the economic impact of user-generated content. These works reflect a consistent trend toward building robust, ethical, and scalable human-machine systems that enhance data quality and decision accuracy across domains.
Notable scientific awards include:
- Lagrange Prize in Complex Systems (2015)
- SIGKDD Test of Time Award (2020)
- NSF CAREER Award
- Best Paper Award at WWW 2011
- Best Paper at SIGMOD 2006
- Multiple best paper recognitions in Management Science and KDD
Professor Ipeirotis has led significant research initiatives, including a $1.5 million Google Research Grant to integrate crowdsourcing with machine learning for visual search. He advises graduate students and contributes to academic leadership through editorial and collaborative roles. His work has been featured in prominent media outlets such as Forbes, WIRED, and Bloomberg Businessweek, highlighting its real-world impact.
He is actively involved in research labs and initiatives at NYU, particularly those focused on data science, behavioral research, and computing. His affiliations with the Center for Research Computing and the Center for Behavioral Research underscore his interdisciplinary approach to solving complex problems at the intersection of technology and human behavior.
