Joshua Reed

Joshua Reed is an Associate Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He has been a faculty member since 2007 and currently serves as Deputy Chair of his department. His work bridges operations research, stochastic modeling, and financial applications.

• Ph.D., Industrial and Systems Engineering, Georgia Institute of Technology, 2007
• B.S., Industrial Engineering, University of Illinois at Urbana-Champaign, 2000

Professor Reed's research centers on the performance analysis and control of stochastic networks, with applications in service systems like call centers and internet congestion control. His expertise includes heavy-traffic approximations, diffusion models, and reflected stochastic processes. He has made significant contributions to the theory of queueing systems, particularly in many-server and infinite-server regimes.

His recent publications span topics such as limit order books, dynamic pricing, and bandwidth sharing networks. These works often employ advanced probabilistic methods and asymptotic analysis to model complex real-world systems. The dominant themes include stochastic optimization, scaling limits, and financial operations.

• Optimal Cash Management Using Impulse Control (2023)
• Reflected Brownian Motion with Drift in a Wedge (2023)
• Large-scale Bundle Size Pricing: A Theoretical Analysis (2021)
• Scaling Limits for Limit Order Book Models (2017)
• High Frequency Asymptotics for the Limit Order Book (2016)

Professor Reed has collaborated with leading researchers such as Peter Lakner, Assaf Zeevi, and Benjamin Reed. He has not received any explicitly mentioned scientific awards in the provided text. He advises graduate students and is involved in ongoing research projects related to financial queueing models and dynamic pricing. His work is supported by theoretical grants in operations research and applied probability, though specific grant details are not listed.

He is associated with research groups focused on stochastic modeling and financial engineering at NYU Stern. His team explores high-frequency trading dynamics, limit order book modeling, and large-scale service system optimization. Current preprints indicate active work in regime-dependent pricing and decentralized exchange arbitrage.