Sarah Kaufman

Portrait of Sarah Kaufman
NYU Rudin Center for Transportation
Director

Sarah M. Kaufman is the Director of the NYU Rudin Center for Transportation, where she researches, advocates for and educates about cutting-edge technologies in transportation. She is also an Assistant Clinical Professor of Public Service.

Ms. Kaufman directs several projects related to improving transportation for contemporary needs: adapting to the increasing frequency and impact of extreme weather events on mobility; an autonomous vehicles policy framework for U.S. cities; The Pink Tax on Transportation, an analysis of how safety concerns impact women's travel patterns in New York City; Intelligent Paratransit, to rethink how we transport seniors and disabled residents; and the Emerging Leaders in Transportation Fellowship, a program to enhance innovation at all levels of transportation planning and policymaking.

Ms. Kaufman serves on the Board of Commissioners of the New York City Taxi and Limousine Commission, as well as on the advisory board of Transportation Alternatives. 

Ms. Kaufman was honored with a Transportation Power 100 Award in 2023, 2022 and 2021, a Responsible 100 Award in 2018 and a Tech Power 50 Award in February 2019, by City & State New York. She is a member of The List and a contributor to Forbes.com. She has been cited in The New York Times, The Wall Street Journal, NBC, CNN, Curbed and Urban Omnibus.

Ms. Kaufman joined NYU Wagner after nearly five years at the Metropolitan Transportation Authority, where she led the open data program, created a conference and online exchange between the MTA and software developers, and assisted in developing the agency's social media program.

Ms. Kaufman earned a Master of Urban Planning from NYU’s Wagner School in 2005, specializing in infrastructure, transportation and telecommunications, and wrote an award-winning thesis designing a bus arrival time signage system. She earned her BA from Washington University in St. Louis, majoring in science writing and concentrating in computer science.