Vlad Eidelman is VP of Data Operations and Research team at FiscalNote. With more than a decade of experience developing machine learning algorithms, he focuses on applying technologies such as machine learning and natural language processing (NLP) to intelligent data aggregation, manipulation, augmentation and generation of the growing amount of 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. His work has led to 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.
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