Vice President of Research, FiscalNote
Vlad Eidelman is the VP of Research at FiscalNote, where he leads AI R&D into advanced methods for analyzing, modeling, and extracting knowledge from unstructured data related to government, policy, and law and built the first version of the companies patented technology to help organizations predict 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 10 patent applications, has been published in conferences like ACL, NAACL and EMNLP, and has been covered by media such as Wired, Vice News, Washington Post and Newsweek.
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