Andrew Daw


Andrew Daw

Hello! I am an assistant professor in the Data Sciences and Operations department within the Marshall School of Business at the University of Southern California. Prior to joining USC Marshall, I completed my PhD at the School of Operations Research and Information Engineering at Cornell University, where I was advised by Jamol Pender and supported as a National Science Foundation Graduate Research Fellow.

What's my research about?

My research focus is in operations management, most often in service operations. I am particularly interested in interaction models, in which the activity in one process can drive future activity within itself and reciprocally in other inter-related processes. These interactions can affect service systems on the arrival side, such as I've recently studied in the context of bursts of autonomous vehicle disengagements, and on the service side, which is the focus of our recent work on co-produced service in customer contact centers. Because these are stochastic models, the conceptual forebearer of the interaction models is the self-exciting Hawkes process, which has natural and relevant connections to virality, contagion, preferential attachment, and rich-get-richer models. My goal is to use these models to better understand interactive operations both analytically and empirically, and to use these insights for informed operational decisions.

What's operations management about?

I like to tell my classes that in operations management we are both poets and quants. I see operations management as a mathematical poetry, in which we describe scenarios and problems with models and data. Like the best poems can, our descriptions are meant to give us new perspectives on the systems and processes that we study, and these perspectives are likewise meant to guide and shape our decisions. We have a responsibility to remember, though, that decisions on processes often become policies, and only an exclusive few have the power to make policy.

Working Papers

The Co-Production of Service: Modeling Service Times in Contact Centers using Hawkes Processes
Daw, Castellanos, Yom-Tov, Pender, and Gruendlinger (2021)

Non-Stationary Queues with Batch Arrivals
Daw, Fralix, and Pender (2020)

Matrix Calculations for Moments of Markov Processes
Daw and Pender (2020)

Can Teleoperations Systems Efficiently Support Autonomous Vehicles? A Critical Staffing Question
Daw, Hampshire, and Pender (2020)

Journal Publications

An Ephemerally Self-Exciting Point Process
Daw and Pender Advances in Applied Probability (2021, to appear)

Beyond Safety Drivers: Applying Air Traffic Control Principles to Support the Deployment of Driverless Vehicles
Hampshire, Bao, Lasecki, Daw, and Pender PLoS ONE (2020)

On the Distributions of Infinite Server Queues with Batch Arrivals
Daw and Pender Queueing Systems (2019)

New Perspectives on the Erlang-A Queue
Daw and Pender Advances in Applied Probability (2019)

Queues Driven by Hawkes Processes
Daw and Pender Stochastic Systems (2018)


BUAD 311   (USC Marshall)
Operations Management Instructor (Fall 2020, 2021)

MATH 112   (Cornell Prison Education Program)
Contemporary Mathematics Instructor (Fall 2019, Spring 2019)

ORIE 3510/5510   (Cornell Engineering)
Introduction to Engineering Stocastic Processes I Instructor (Summer 2017, Summer 2016)

Undergraduate Advising

Analyzing the Spotify Top 200 Through a Point Process Lens
Harris*, Liu*, Park*, Ramireddy*, Ren*, Ren*, Yu*, Daw, and Pender Submitted (2019)

Queue Length Rounding and Delayed Information in Disney World Queues
Nirenberg*, Daw, and Pender Winter Simulation Conference (2018)

* Undergraduate student


307A Bridge Hall     •