About

Dr. Courtney Snyder is an Associate Professor of Music and Associate Director of Bands at the University of Michigan School of Music, Theatre & Dance. She conducts the University Concert Band, teaches undergraduate and graduate courses in instrumental conducting, and assists in administering the Michigan Bands program. Additionally, she serves as the Music Director of the Michigan Youth Symphonic Band. Her previous experience includes roles at the University of Nebraska-Omaha, where she was the Assistant Director of Bands and Director of Athletic Bands, conducting the Maverick Marching Band and Concert Band, and serving as Associate Conductor of the Symphonic Wind Ensemble. She also directed the Nebraska Wind Symphony, Omaha’s oldest community band, which has performed at various state, regional, and international conferences. Snyder's background includes teaching high school and middle school band and orchestra in Michigan public schools, notably serving as Director of Bands at Ypsilanti High School. An active guest conductor and clinician, she has presented at numerous national and international conferences, focusing on topics such as improvisation in large concert ensembles, conducting pedagogy, band music of South and Central America, and promoting equity for women and BIPOC composers and conductors. Her scholarly contributions include publications in various music education journals and chapters on pioneering female band directors. She has received multiple awards, including the University of Michigan DEI Breaking Ground Award, Tau Beta Sigma’s Paula Crider Award, and the Silver Baton Award from Women Band Directors International.

Research topics

• Computer Science
• Engineering
• Mechanical engineering
• Marketing
• Business
• Labour economics
• Economics
• Operations management

Selected publications

• On Worker Behavior in the AI-Enabled Service Triad

  Deep Blue (University of Michigan) · 2025-01-01

  dissertationOpen access1st authorCorresponding

  AI algorithms are playing an increasingly important role in modern service provision. In this dissertation, I develop and explore a concept I term the “AI-enabled service triad,” to describe the interdependent relationships between human workers, human customers, and algorithms inside AI augmented service systems. Although each pairwise interaction in the triad is well-understood, the three-way interaction has remained relatively unstudied. With results from experiments, interviews, observations, and survey questions, I examine the triad across three contexts—personalized recommendation, K12 education, and lending—each characterized by distinct objectives. In Chapter 2, I explore the triad in the context of a queueing system. Using a novel laboratory experiment, I study how system loads influence workers’ decisions to either default to or deliberate on algorithmic advice. Results from two studies reveal system load and algorithm quality jointly shape workers’ algorithm-use behavior, with downstream effects on service quality and customer throughput times. In Chapter 3, following the evolution of algorithmic decision-support from conventional AI to generative AI, I shift my focus to worker productivity in an entire, discretionary workflow. Through a longitudinal case study of 24 US public school teachers, I identify a new use case for generative AI associated with higher reported productivity—not only outsourcing effort for task outputs (e.g., material creation), but also guiding inputs to workflow planning. In Chapter 4, building on findings from the previous chapters, I consider broader implications of AI decision-support for another operationally relevant service outcome: fairness. Specifically, I use a laboratory experiment to study the effect of algorithm design on fairness and accuracy, as mediated by worker use of (fair) AI recommendations. I show patterns in users’ deviation and reliance result in less fairness than either humans or fair algorithms acting alone. Together, my dissertation provides insights into the nuanced ways in which human behavior—beyond rate of algorithm use—mediates the intended or assumed benefits from AI—beyond accuracy. While it is motivated by service examples, the implications of the triad framework extend further, setting the stage for future work beyond service about new outcome measures, about dynamic behavior associated with learning over time, and about the decision subjects’ response to algorithm-augmented decision-making.

  PublisherOA PDFDOI

• Algorithm Reliance: Fast and Slow

  Management Science · 2025-05-06 · 7 citations

  article1st authorCorresponding

  In algorithm-augmented service contexts where workers have decision authority, they face two decisions about the algorithm: whether to follow its advice and how quickly to do so. The pressure to work quickly increases with the speed of arriving customers. In this paper, we ask the following. How do workers use algorithms to manage system loads? With a laboratory experiment, we find that superior algorithm quality and high system loads increase participants’ willingness to use their algorithm’s advice. Consequently, participants with the superior algorithm make higher-quality recommendations than those with no algorithm (participants with the inferior algorithm make slightly lower-quality recommendations than those without). However, participants do not necessarily speed up by using algorithms’ advice; their throughput times only decrease compared with the no-algorithm baseline when the system load is high and algorithm quality is superior, although participants would benefit from working faster in all treatments. This happens in part because participants in the high-load, superior-algorithm treatment serve customers more quickly than participants in the other treatments, conditional on using the algorithm. Participants in the high-load, superior-algorithm treatment work especially quickly in later periods as they increasingly default to their algorithm’s advice. Our findings show that algorithms can have benefits for both decision quality and speed. Quality benefits come from workers’ decision to use their algorithms’ advice, whereas speed benefits depend on workers’ algorithm use and the time they spend deliberating about their algorithm use. Ultimately, algorithm quality and system load are mutually reinforcing factors that influence both service quality and especially speed. This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01989 .

  PublisherDOI

• Making ChatGPT Work for Me

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02

  articleSenior author

  Increasingly, work happens through human collaboration with generative AI (e.g., ChatGPT). In this paper, we present a qualitative study of this collaboration for real-life work tasks. We focus our study on US K12 public school teachers (N = 24) who regularly design and complete text-generation tasks such as creating quizzes, slide decks, word problems, reading passages, lesson plans, classroom activities, and projects. In one-on-one video- and audio-recorded virtual sessions, we observe each teacher using ChatGPT-4 for work tasks of their choosing for 15 minutes, then debrief their experience. Analyzing 201 prompts inputted by the 24 teachers, we uncover four main modes with which the teachers request support from ChatGPT: (1) make for me (55% of prompts), (2) find for me (15%), (3) jump-start for me (10.5%), and (4) iterate with me (15.5%). The first three modes (make, find, and jump-start) are often requests of generative AI to do something, whereas the fourth mode (iterate) is a request of generative AI to think. In a follow-up survey of the same 24 teachers, most report using multiple modes for their work, but infrequently. Our study contributes new data and knowledge about how teachers are coming to understand whether and how to integrate generative AI into their teaching preparation routines.

  PublisherDOI

• Making ChatGPT Work For Me

  SSRN Electronic Journal · 2024

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Engineering
  PublisherDOI

• Algorithm Reliance Under Pressure: The Effect of Customer Load on Service Workers

  SSRN Electronic Journal · 2022 · 9 citations

  1st authorCorresponding
  • Business
  • Operations management
  • Labour economics
  PublisherDOI

• Algorithm development for intrafraction radiotherapy beam edge verification from Cherenkov imaging

  Journal of Medical Imaging · 2018-01-02 · 13 citations

  articleOpen access1st authorCorresponding

  Imaging of Cherenkov light emission from patient tissue during fractionated radiotherapy has been shown to be a possible way to visualize beam delivery in real time. If this tool is advanced as a delivery verification methodology, then a sequence of image processing steps must be established to maximize accurate recovery of beam edges. This was analyzed and developed here, focusing on the noise characteristics and representative images from both phantoms and patients undergoing whole breast radiotherapy. The processing included temporally integrating video data into a single, composite summary image at each control point. Each image stack was also median filtered for denoising and ultimately thresholded into a binary image, and morphologic small hole removal was used. These processed images were used for day-to-day comparison computation, and either the Dice coefficient or the mean distance to conformity values can be used to analyze them. Systematic position shifts of the phantom up to 5 mm approached the observed variation values of the patient data. This processing algorithm can be used to analyze the variations seen in patients being treated concurrently with daily Cherenkov imaging to quantify the day-to-day disparities in delivery as a quality audit system for position/beam verification.

  PublisherOA PDFDOI

• Characterization of degradation mechanisins in GaAs/AlGaas heterojunction bipolar transistors

  2005-08-25 · 2 citations

  article
  PublisherDOI

• Realization of in-situ sub two-dimensional quantum structures by strained layer growth phenomena in the InxGa1- xAs/GaAs system

  Journal of Electronic Materials · 1996-03-01 · 14 citations

  article
  PublisherDOI

• Catastrophic degradation lines at the facet of InGaAsP/InP lasers investigated by transmission electron microscopy

  Applied Physics Letters · 1995-07-24 · 23 citations

  article1st authorCorresponding

  Material transformations occurring at the facets of optically ‘‘stressed’’ planar InGaAsP/InP diode lasers have been investigated by transmission electron microscopy and energy dispersive x-ray spectroscopy. Catastrophic degradation lines (CDLs) which are characteristic of catastrophic optical damage are observed for optical power densities ∼107 W/cm2. Analysis of the microstructure reveals a series of 150 nm wide GaAs-rich tracks and the formation of unique void/InGa-rich precipitate pairs within the InGaAsP active layer. These observations suggest that the formation of local group III-rich regions is the first stage in the formation of CDLs. Subsequently, the strong absorption of the impinging laser beam leads to propagation of an InGa-rich melt, thereby producing the GaAs-rich tracks through a process similar to liquid phase epitaxy. These results are discussed in the context of standard physical models for CDLs.

  PublisherDOI

• Combining transmission electron microscopy with focused ion beam sputtering for microstructural investigations of AlGaAs/GaAs heterojunction bipolar transistors

  Journal of Vacuum Science & Technology B Microelectronics and Nanometer Structures Processing Measurement and Phenomena · 1995-07-01 · 4 citations

  article1st authorCorresponding

  Transmission electron microscopy (TEM) and focused ion beam sputtering have been used for investigations of microstructure and defect formation in AlGaAs/GaAs heterojunction bipolar transistors. The use of focused ion beam sputtering to prepare nearly ideal thin membranes within the active region of transistors for cross-sectional electron microscopy is described. Results are obtained from as-fabricated devices, devices annealed at temperatures of 400 °C, and devices for which ‘‘aging’’ is accelerated by operating at high-bias conditions and heating in combination. We find that electrical operation leads to significant microstructural changes which are distinct from those observed in devices which are annealed. TEM images of the metal/semiconductor contact reveal ‘‘metal spiking’’ and facetted structures associated with alloy interpenetration. Characterization of the semiconductor heterojunctions reveal precipitates at the emitter-base junction. Mechanisms for the formation of these defects under high-biased operation are discussed.

  PublisherDOI

Frequent coauthors

• B. G. Orr

  University of Michigan–Ann Arbor

  9 shared

• Bradford G. Orr

  University of Michigan–Ann Arbor

  6 shared

• Matthew D. Johnson

  University of Minnesota

  5 shared

• Samantha Keppler

  5 shared

• J. Sudijono

  Applied Materials (Germany)

  4 shared

• Stephen Leider

  4 shared

• P. Bhattacharya

  3 shared

• Brian W. Pogue

  Dartmouth College

  3 shared

Awards & honors

• University of Michigan School of Music, Theatre & Dance DEI…
• Tau Beta Sigma’s Paula Crider Award
• Women Band Directors International’s Silver Baton Award
• National Band Association’s Citation of Merit
• 2nd Place in the 2018 American Prize in Conducting, Band/Win…