Description: Ride-hailing platforms like Uber, Lyft, Didi Chuxing, and Ola have achieved explosive growth, in part by improving the efficiency of matching between riders and drivers, and by calibrating the balance of supply and demand through dynamic pricing. We survey methods for dynamic pricing and matching in ride-hailing, and show that these are critical for providing an experience with low waiting time for both riders and drivers. We also discuss approaches used to predict key inputs into those algorithms: demand, supply, and travel time in the road network. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing dynamic pricing and dynamic waiting, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective. Bio: Dawn Woodard received her Ph.D. 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 creates the 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.