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Michelle Brown is a Senior Advisor for Elections at the National Democratic Institute (NDI) with nearly two decades of experience in assessing and verifying elections. To date, she has worked with 15 different civil society organizations and helped them to systematically observe more than 35 elections in places such as Azerbaijan, Burma, Egypt, Ukraine, Nigeria, Kenya, Uganda, and South Sudan. Throughout her career, she has provided guidance to organizations on topics such as strategic planning, community organizing, information visualization, and statistically based observation techniques. Ms. Brown is an expert in teaching organizations to use quantitative methods as they evaluate their elections. This includes using advanced election methodologies known as a Voter List Verification (VRV) for evaluating the quality of the voter list, and a Parallel Vote Tabulation (PVT) for assessing the quality of election day as well as verifying (or calling into question) the results. Ms. Brown is also co-author of an NDI manual that explores the use of quantitative research methods in the design of democracy building programs. Ms. Brown collaborates with several academics to apply cutting edge computer vision and deep learning methods to electoral integrity problems. Recently, she co-authored the published research paper "Hidden in plain sight? Irregularities on statutory forms and electoral fraud," which piloted the use of deep learning methods to quickly evaluate thousands of election results images for signs of malfeasance. She also founded the Open Election Data Initiative, which promotes accountability through the use of open election data. In addition to her work at NDI, Ms. Brown works as a data scientist and runs a creative technology studio. Ms. Brown has a B.A. from the University of Arizona and a M.P.P from Georgetown University.