Panos Ipeirotis is a Professor of Information Systems and Data Science at New York University. He is also the George Kellner Fellow at Leonard N. Stern School of Business. He is the recipient of the 2015 Lagrange Prize for his contributions to the field of social media and crowdsourcing. He has authored more than 100 scientific publications and his work has been cited more than 16,000 times, according to Google Scholar.
Panos's research has been multi-disciplinary and has won awards in multiple fields. He has received best paper awards in IEEE ICDE and ACM SIGMOD for his work on text databases and information extraction; the best paper award in ACM WWW and at AIS ICIS for his work on product search; the best paper at INFORMS Management Science for his work on the pricing power of product reviews and their effect on online marketplaces; the best paper at ACM KDD for his work on integrating human and machine intelligence, and the best paper at AAAI HCOMP for his work on automated testing for skills in online labor markets. He has chaired the ACM Economics and Computation conference, the AAAI Human Computation Conference, and the WWW2018 conference. He has been also a member of the editorial board for the Management Science, IEEE Transactions of Knowledge Engineering, INFORMS Journal of Computing, and ACM Journal of Data and Information Quality.
Panos is working closely with industrial partners to both transfer his research to practice and for getting inspired by real-world problems that remain unaddressed by existing research. He has been a founding member of the data science team at Integral Ad Science in 2008, where he built the workflow that integrates crowdsourcing and machine learning to automatically build machine learning models by letting the stakeholder simply describe what type of content the models should detect. In parallel, he worked with Facebook in order to automate their content moderation system that detects inappropriate Facebook posts. He has also worked with UpWork (then oDesk) to improve the matching process between contractors and employers, and to build scalable and cheating-proof systems that assess the competency of contractors for various skills. Panos has also worked with the World Bank to assess the macro-level view of online labor and project trends for the future. In 2013-14, he worked at Google, building a scalable crowdsourcing system that integrated humans and information extraction algorithms at Google, to improve the coverage and quality of Google's Knowledge Graph.
In 2015, Panos co-founded Detectica, which uses deep learning and network analysis to discover and investigate various types of employee misconduct. Detectica's co-founders include Foster Provost, a world-expert in data science and machine learning, and Josh Attenberg, previously the Director of Data Science at Etsy.
Panos's work is frequently mentioned in the press, including venues such as The New York Times, the Economist, Financial Times, Wired, Foreign Policy, Wall Street Journal, Bloomberg BusinessWeek, TIME Magazine, and many others. He received his PhD in Computer Science from Columbia University in 2004.
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Detecting Misconduct and Malfeasance within Financial Institutions
Misbehavior in the online world manifests itself in several forms, and often depends on the domain at hand. In the financial domain, firms have the regulatory obligation to self-monitor the...
Targeted Crowdsourcing with a Billion (Potential) Users
We describe Quizz, a gamified crowdsourcing system that simultaneously assesses the knowledge of users and acquires new knowledge from them. Quizz operates by asking users to complete short...
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