New book about the sharing economy, coming May 2016

Launch 2015 talk about the sharing economy and blockchain markets

Conference celebrating Roy Radner

The May 2014 Collaborative-Peer-Sharing Economy Summit

Congressional testimony on peer-to-peer business and the sharing economy, 01/15/2014

Selected recent academic papers (back to top)

Selected recent op-eds (back to top)

Selected recent interviews/profiles (back to top)

Television and web video

Selected recent media coverage (back to top)
Google News feed

Selected recent and forthcoming speaking engagements (back to top)

Brief bio (back to top)

Arun Sundararajan is Professor and the Robert L. and Dale Atkins Rosen Faculty Fellow at New York University's Leonard N. Stern School of Business. He is also an affiliated faculty member at NYU's Center for Urban Science+Progress, and at NYU's Center for Data Science.

Professor Sundararajan's research program studies how digital technologies transform business and society. Some of his current and recent research focuses on the governance of digital spaces, collaborative consumption and the sharing economy, social media and cities, digital institutions, contagion in networks, privacy strategy, pricing in digital markets and managing online piracy. He has published over 50 scientific papers in peer-reviewed academic journals and conferences, and has given more than 200 invited talks at industry, government and academic forums internationally. His research has been recognized by six Best Paper awards, two Google Faculty awards, and a variety of other grants. He has served on numerous editorial boards for scholarly journals. He has provided expert input about the crowd-based sharing economy as part of Congressional testimony and to a variety of city, state and federal government agencies, including the Presidential Council of Advisors on Science and Technology, the National Economic Council and the Federal Trade Commission.

His op-eds and expert commentary have appeared in TIME Magazine, the New Yorker, the New York Times, the Guardian, the Washington Post, Le Monde, El Pais, Wired, TechCrunch, the Wall Street Journal, the Financial Times and Harvard Business Review, and on Al Jazeera, BBC News, Bloomberg, CNN, CNBC, Fox, NPR, PBS and a variety of non-English language TV networks. He has served as Director of NYU Stern's IS Doctoral Program since 2007, is one of the founders of the Workshop on Information in Networks, is a member of the City of New York's Technology Advisory Group, is on the Scientific Advisory Board for Cisco Systems, and is an advisor to OuiShare, the Center for Global Enterprise, and the National League of Cities. He holds degrees from the Indian Institute of Technology, Madras and the University of Rochester. Follow him on Twitter @digitalarun

Socialter cover - speaking headshot - serious headshot - smiling headshot - pronounce my complicated name

Directions to my office (back to top)

(I frequently email some variant of the text in the following paragraph to my visitors and am reproducing it here for convenience. If you do want to visit me, please don't simply show up and knock on my door. Rather, make sure we have been in touch to schedule a meeting time first, or contact Sharon Kim. For interview requests and other media inquiries, contact Rika Nazem (212-998-0678) or Jessica Neville (416-516-7677) in the NYU Stern Office of Public Affairs.)

Stern office: The Kaufman Management Center is the building on the right when you face Gould Plaza, at the intersection of West 4th Street and Greene Street. Once you get past the security desk, take the elevators to the 8th floor. Turn left when you exit the elevators, walk down the corridor and through the glass door marked 8-190, veer to the right of the reception desk, walk past a set of mailboxes, then turn left around the corner into an area that looks a lot less new and shiny than the area you just walked through. I'm in 8-90, the corner office that has the door with a large printout of this on it, and adjoins a small waiting area with a big book about big data on a small table.