Vlad Eidelman is the VP of Research at FiscalNote, where he leads the Data Operations and Research teams, focusing on using machine learning and natural language processing (NLP) to create practical applications for analyzing, modeling, and extracting knowledge from the growing amount of mostly unstructured data related to government, policy and law. He created the first version of the company’s patented technology to help organizations understand and act on policy changes.
Prior to FiscalNote, he worked as a researcher in a number of academic and industry settings, completing his Ph.D. in CS, as an NSF and NDSEG Fellow, at the University of Maryland and his B.S. in CS and Philosophy at Columbia University. His research focuses on machine learning algorithms for a broad range of natural language processing applications, including entity extraction, machine translation, text classification and information retrieval, especially applied to computational social science. His work has led to more than 10 patent applications, he has published more than 20 peer-reviewed articles in and serves on the program committees for top-tier conferences, such as ACL, NAACL, and EMNLP, and has been covered by media such as Wired, Vice News, and Washington Post.