Senior Data Science Manager of Maps
Dawn Woodard received her PhD in statistics from Duke University, after which she became a faculty member in the School of Operations Research and Information Engineering at Cornell. There, she developed collaborative relationships with several ambulance organizations, and focused her work on statistical methods for ambulance decision support systems. After receiving tenure at Cornell, she spent her sabbatical at Microsoft Research, where she developed travel time prediction methods for use in Bing Maps. She then transitioned to a role at Uber, building and leading their Marketplace Optimization Data Science organization. The team creates Uber’s marketplace-related technologies, such as dispatch, pricing, and incentives. It is now one of the premier data science teams at Uber and includes specialists in statistics, economics, operations research, and machine learning. Currently, Dr. Woodard leads data science for Maps at Uber, which createsthe mapping platform used in Uber's rider app, driver app, and decision systems (such as pricing and dispatch). The team's technologies include road map and points of interest definition, map search, route optimization, travel time prediction, and navigation.
"Dynamic Pricing and Matching in Ride-Sharing"
Ride-sharing platforms like Uber, Lyft, Didi Chuxing, and Ola are transforming urban mobility by connecting riders with drivers via the sharing economy. These platforms have achieved explosive growth, in part by dramatically improving the efficiency of matching, and by calibrating the balance of supply and demand through dynamic pricing. The dynamic adjustment of prices ensures a reliable service for riders, and incentivizes drivers to provide rides at peak times and locations. Dynamic pricing is particularly important for ride-sharing, because pricing too low causes pickup ETAs to get very long, which reduces the efficiency of the platform and causes a poor experience for riders and drivers. We review the literature on matching and pricing techniques in ride-sharing. We also discuss how to estimate several key inputs to those algorithms: predictions of demand, supply, and travel time in the road network.