Hello! I am a Dean's Assistant Professor in Business Administration within the department of Data Sciences and Operations (DSO) at the Marshall School of Business of the University of Southern California. Prior to joining DSO, I completed my PhD at the School of Operations Research and Information Engineering at Cornell University, where I was supported through a National Science Foundation Graduate Research Fellowship.
What's my research about?
Recently, I wrote a note in Queueing Syst. on some of my current interests.
My research focus is in operations research and operations management, with a methodological emphasis on stochastic models and applied probability and a contextual emphasis on services. On a technical level, my work deals with queueing theory, point processes, and history-dependent stochastic processes. I am particularly interested in models shaped by interactions, in which the history of activity in one process can drive future activity within itself and reciprocally in other inter-related processes. Because these are stochastic models, the conceptual forebearer is the self-exciting Hawkes process, which has many natural and relevant connections.
Forms and friends of self-excitement: history-dependent; history-driven; ripple effect; domino effect; virality; contagion; infection; influence; interaction; chain-reaction; rich-get-richer; virtuous cycle; "when it rains, it pours"1; "what's past is prologue"2,3; "the past is never dead. it's not even past."4
Contextually, my interests in these probability models stem from their uses in service operations, where the progression of a service interaction is shaped by the history of the customer-agent exchange so far. We have seen strong empirical evidence for this phenomenon through data from co-produced services in customer contact centers. Recently, we've also found surprising connections between Hawkes processes and well-known objects in enumerative combinatorics, which supports analysis of the proposed cluster models of service. For example, these techniques allow us to explore this model's insights for the impact of distinguishing customer and agent contributions, which contrasts stochastic models for services from those that might be used in other contexts, like computation or production.
What's operations management about?
I like to tell my classes that in operations management we are both poets and quants. I've described operations research as a form of 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.
Asymmetries of Service: Interdependence and Synchronicity
Daw and Yom-Tov
(2024)
rotation figures : ∩/∪, 𝒦
The Hybrid Hospital: Balancing On-Site and Remote Hospitalization
Zychlinski, Mendelson, and Daw
(2024)
Price-Delay Trade-offs in Services: Customers, Servers, and the Firm-Platform Distinction
Giannoutsou and Daw
(2024)
Journal Publications
How to Staff When Customers Arrive in Batches
Daw, Hampshire, and Pender
Management Science
(2024, to appear)
The Co-Production of Service: Modeling Services Using Hawkes Processes
Daw, Castellanos, Yom-Tov, Pender, and Gruendlinger
Management Science
(2024)
Convergence of Batch Arrival Queues to Shot-Noise Processes
Daw, Fralix, and Pender
Operations Research
(2024)
Conditional Uniformity and Hawkes Processes
Daw
Mathematics of Operations Research
(2024)
Matrix Calculations for Moments of Markov Processes
Daw and Pender
Advances in Applied Probability
(2023)
Services Shaped by History
Daw
Queueing Systems
(2022)
An Ephemerally Self-Exciting Point Process
Daw and Pender
Advances in Applied Probability
(2022)
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)
Contrasting Activity-Based and Time-Based Systematic Closure Policies
Castellanos, Daw, Ward, and Yom-Tov
Winter Simulation Conference
(2024)
Scorigami: Simulating the Distribution and Assessing the Rarity of NFL Scores
Moyer*, Railey*, Daw, and Gutekunst
Winter Simulation Conference
(2024)
Markovian Simulations of Systems with Concurrent Hawkes Service Interactions
Daw and Yom-Tov
Winter Simulation Conference
(2023)
Analyzing the Spotify Top 200 Through a Point Process Lens
Harris*, Liu*, Park*, Ramireddy*, Ren*, Ren*, Yu*, Daw, and Pender
(2019)
Queue Length Rounding and Delayed Information in Disney World Queues
Nirenberg*, Daw, and Pender
Winter Simulation Conference
(2018)
* Undergraduate student
BUAD 313
(USC Marshall)
Advanced Operations Management and Analytics
Instructor
(Fall 2024, 2023+)
BUAD 311
(USC Marshall)
Operations Management
Instructor
(Fall 2022, 2021, 2020)
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, 2016)
+ New core course which debuted Fall 2023!