Vlad Eidelman

Vlad currently leads the AI Research team at FiscalNote, 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. He has more than a decade of experience developing state-of-the-art machine learning algorithms for a broad range of NLP applications including entity extraction, structured prediction, machine translation, text classification, and information retrieval.

His work has led to 10 patent applications, he has published more than 20 peer-reviewed papers 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. His research awards include the National Science Foundation Graduate Research Fellowship and the National Defense Science and Engineering Graduate Fellowship, and he has conducted research in academia (Columbia University, Johns Hopkins University, University of Maryland), industry (Raytheon, BBN Technologies, JHU Applied Physics Laboratory), and government.

Vlad completed his Ph.D. in Computer Science with Philip Resnik at the University of Maryland, and his B.S. in Computer Science and Philosophy at Columbia University.