Navneet Potti
Navneet Potti completed his PhD in Data Management in University of Wisconsin - Madison in 2018, under the guidance of Prof. Jignesh Patel. During his graduate study, he worked on various aspects of building high-performance data management systems, as well as intuitive user interfaces for data analytics. His thesis research is being commercialized at a startup, DataChat. His current research focuses on information extraction from text documents. In the past, he worked as a quantitative analyst for Goldman Sachs, and did internships at IBM Almaden Research Center, Pivotal and Google. He holds a BTech and an MTech in Electrical Engineering from Indian Institute of Technology - Madras.
Authored Publications
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Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, ACL, pp. 3847-3860
Data-Efficient Information Extraction from Form-Like Documents
Document Intelligence Workshop @ KDD 2021
Glean: Structured Extractions from Templatic Documents
Proceedings of the VLDB Endowment (2021), pp. 997-1005
Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Majumder
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 6495-6504
Hidden in Plain Sight: Classifying Emails Using Embedded Image Contents
Proceedings of the 2018 World Wide Web Conference (WWW 2018), pp. 1865-1874
