Vlad Eidelman is the Chief Scientist at FiscalNote, where he leads production data science and AI 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. As the 10th employee, he developed the first version of the company’s patented technology to help organizations understand and act on policy changes, led major technical partnerships and data augmentation and integration initiatives, and as part of the executive team helped secure over $250 million in funding from Series A on and grow the business from pre-revenue to over 4,000 customers.
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. He has more than a decade of experience developing 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 8 patents, 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.