About

Ian Quinn is the Allen Forte Professor of Music Theory and serves as the Chair of the Department of Music at Yale University. He holds degrees from Columbia University (B.A., 1993) and the Eastman School of Music of the University of Rochester (M.A., 1998; Ph.D., 2004). Prior to his appointment at Yale, he taught at the University of Chicago and the University of Oregon. In 2008-09, he was a Residential Fellow at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford. Quinn has made significant contributions to the fields of music theory, music cognition, and mathematical music analysis. He has served as editor of the Journal of Music Theory and is on the editorial board of the Journal of Mathematics and Music. His research has been recognized with awards from the Society for Music Theory, including the Emerging Scholar Award and the Outstanding Publication Award. His work often explores the relationships between tonal harmony, harmonic function, and the application of computational and mathematical methods to music analysis. Quinn has advised numerous dissertations in music theory and history, and he actively participates in scholarly organizations such as the Society for Mathematics and Computation in Music and the Northeast Music Cognition Group. Additionally, he organizes the Yale-New Haven Regular Singing, a weekly shape-note singing group.

Research topics

• Political Science
• Cardiology
• Medicine
• Physical therapy
• Internal medicine
• Radiology
• Virology
• Intensive care medicine

Selected publications

• Acknowledgments

  University of California Press eBooks · 2023

  • Political Science
  • Political Science
  DOI

• Cardiac Troponin Testing as a Component of Return to Play Cardiac Screening in Young Competitive Athletes Following SARS‐CoV‐2 Infection

  Journal of the American Heart Association · 2022 · 6 citations

  • Medicine
  • Cardiology
  • Internal medicine

  Background Initial protocols for return to play cardiac testing in young competitive athletes following SARS-CoV-2 infection recommended cardiac troponin (cTn) to screen for cardiac involvement. This study aimed to define the diagnostic yield of cTn in athletes undergoing cardiovascular testing following SARS-CoV-2 infection. Methods and Results This prospective, observational cohort study from ORCCA (Outcomes Registry for Cardiac Conditions in Athletes) included collegiate athletes who underwent cTn testing as a component of return to play protocols following SARS-CoV-2 infection. The cTn values were stratified as undetectable, detectable but within normal limits, and abnormal (>99% percentile). The presence of probable or definite SARS-CoV-2 myocardial involvement was compared between those with normal versus abnormal cTn levels. A total of 3184/3685 (86%) athletes in the ORCCA database met the inclusion criteria for this study (age 20±1 years, 32% female athletes, 28% Black race). The median time from SARS-CoV-2 diagnosis to cTn testing was 13 days (interquartile range, 11, 18 days). The cTn levels were undetectable in 2942 athletes (92%), detectable but within normal limits in 210 athletes (7%), and abnormal in 32 athletes (1%). Of the 32 athletes with abnormal cTn testing, 19/32 (59%) underwent cardiac magnetic resonance imaging, 30/32 (94%) underwent transthoracic echocardiography, and 1/32 (3%) did not have cardiac imaging. One athlete with abnormal troponin met the criteria for definite or probable SARS-CoV-2 myocardial involvement. In the total cohort, 21/3184 (0.7%) had SARS-CoV-2 myocardial involvement, among whom 20/21 (95%) had normal troponin testing. Conclusions Abnormal cTn during routine return to play cardiac screening among competitive athletes following SARS-CoV-2 infection appears to have limited diagnostic utility.

  PublisherOA PDFDOI

• SARS-CoV-2 Cardiac Involvement in Young Competitive Athletes

  Circulation · 2021 · 263 citations

  • Medicine
  • Internal medicine
  • Cardiology

  BACKGROUND: Cardiac involvement among hospitalized patients with severe coronavirus disease 2019 (COVID-19) is common and associated with adverse outcomes. This study aimed to determine the prevalence and clinical implications of COVID-19 cardiac involvement in young competitive athletes. METHODS: In this prospective, multicenter, observational cohort study with data from 42 colleges and universities, we assessed the prevalence, clinical characteristics, and outcomes of COVID-19 cardiac involvement among collegiate athletes in the United States. Data were collected from September 1, 2020, to December 31, 2020. The primary outcome was the prevalence of definite, probable, or possible COVID-19 cardiac involvement based on imaging definitions adapted from the Updated Lake Louise Imaging Criteria. Secondary outcomes included the diagnostic yield of cardiac testing, predictors for cardiac involvement, and adverse cardiovascular events or hospitalizations. RESULTS: Among 19 378 athletes tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 3018 (mean age, 20 years [SD, 1 year]; 32% female) tested positive and underwent cardiac evaluation. A total of 2820 athletes underwent at least 1 element of cardiac triad testing (12-lead ECG, troponin, transthoracic echocardiography) followed by cardiac magnetic resonance imaging (CMR) if clinically indicated. In contrast, primary screening CMR was performed in 198 athletes. Abnormal findings suggestive of SARS-CoV-2 cardiac involvement were detected by ECG (21 of 2999 [0.7%]), cardiac troponin (24 of 2719 [0.9%]), and transthoracic echocardiography (24 of 2556 [0.9%]). Definite, probable, or possible SARS-CoV-2 cardiac involvement was identified in 21 of 3018 (0.7%) athletes, including 15 of 2820 (0.5%) who underwent clinically indicated CMR (n=119) and 6 of 198 (3.0%) who underwent primary screening CMR. Accordingly, the diagnostic yield of CMR for SARS-CoV-2 cardiac involvement was 4.2 times higher for a clinically indicated CMR (15 of 119 [12.6%]) versus a primary screening CMR (6 of 198 [3.0%]). After adjustment for race and sex, predictors of SARS-CoV-2 cardiac involvement included cardiopulmonary symptoms (odds ratio, 3.1 [95% CI, 1.2, 7.7]) or at least 1 abnormal triad test result (odds ratio, 37.4 [95% CI, 13.3, 105.3]). Five (0.2%) athletes required hospitalization for noncardiac complications of COVID-19. During clinical surveillance (median follow-up, 113 days [interquartile range=90 146]), there was 1 (0.03%) adverse cardiac event, likely unrelated to SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection among young competitive athletes is associated with a low prevalence of cardiac involvement and a low risk of clinical events in short-term follow-up.

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• Tonal Harmony

  2019-01-08 · 20 citations

  reference-entry1st authorCorresponding

  This chapter introduces a novel explanatory model for the tonal grammar of music from the thoroughbass era, encompassing baroque and galant practices. The framework models the internalized knowledge of a skilled continuo player improvising at the keyboard prior to Rameau’s invention of the fundamental-bass concept. It makes predictions about the tonal tendency of a chord based on the interaction of its constituent scale-degrees. The framework models something like Schenker’s “will of the tones,” predicting whether individual tones in a chord will tend to “feel” stabilized or mobilized. Stabilized tones tend to remain in place, and mobilized tones tend to move by step. These tendencies are regulated by the intervallic relations among notes in a chord, and can be expressed as two simple laws: a Law of Counterpoint that applies to generic pitch-class intervals regardless of which specific scale degrees they span, and a Law of Harmony that makes scale-degree-specific predictions.

  PublisherDOI

• Chord Context and Harmonic Function in Tonal Music

  Music Theory Spectrum · 2018-01-01 · 44 citations

  articleSenior author

  Journal Article Chord Context and Harmonic Function in Tonal Music Get access Christopher WM White, Christopher WM White Email: chriswmwhite@gmail.com Search for other works by this author on: Oxford Academic Google Scholar Ian Quinn Ian Quinn Search for other works by this author on: Oxford Academic Google Scholar Music Theory Spectrum, Volume 40, Issue 2, Fall 2018, Pages 314–335O, https://doi.org/10.1093/mts/mty021 Published: 08 November 2018

  PublisherDOI

• Corpus-Derived Key Profiles Are Not Transpositionally Equivalent

  Music Perception An Interdisciplinary Journal · 2017-05-22 · 34 citations

  article1st authorCorresponding

  A fundamental assumption of distributional key-finding methods is that the frequency distributions of pitch classes in all keys are transpositionally equivalent. We tested this assumption with three experiments. First, using data from the openings of 995 major-key pieces and 596 minor-key pieces in the Yale-Classical Archives Corpus, we found that scale-degree distributions differ significantly from one key to another, and further analysis revealed that pieces keys with signatures having relatively more accidentals exhibit significantly more chromaticism than keys with fewer accidentals. Second, we examined whether these data might be accounted for by different keys’ varying modulation tendencies, and found this to be the case: keys with more accidentals modulate more frequently to more distant keys. Finally, we attempted to exclude modulatory passages from our data using a key profile analysis to identify key and mode within our dataset; however, the results of Experiment 1 still held. In sum, even when using a method that assumes transpositional equivalence, we found a difference between key profiles of different keys.

  PublisherDOI

• The Yale-Classical Archives Corpus

  Empirical Musicology Review · 2016-07-08 · 39 citations

  articleOpen accessSenior author

  The Yale-Classical Archives Corpus (YCAC) contains harmonic and rhythmic information for a dataset of Western European Classical art music. This corpus is based on data from classicalarchives.com, a repository of thousands of user-generated MIDI representations of pieces from several periods of Western European music history. The YCAC makes available metadata for each MIDI file, as well as a list of pitch simultaneities ("salami slices") in the MIDI file. Metadata include the piece's composer, the composer's country of origin, date of composition, genre (e.g., symphony, piano sonata, nocturne, etc.), instrumentation, meter, and key. The processing step groups the file's pitches into vertical slices each time a pitch is added or subtracted from the texture, recording the slice's offset (measured in the number of quarter notes separating the event from the file's beginning), highest pitch, lowest pitch, prime form, scale-degrees in relation to the global key (as determined by experts), and local key information (as determined by a windowed key-profile analysis). The corpus contains 13,769 MIDI files by 571 composers yielding over 14,051,144 vertical slices. This paper outlines several properties of this corpus, along with a representative study using this dataset.

  PublisherOA PDFDOI

• Reviews: Hidden Structure: Music Analysis Using Computers and Music21: A Toolkit for Computer-Aided Musicology

  Journal of the American Musicological Society · 2014-01-01 · 1 citations

  article1st authorCorresponding

  The Age of Big Data is here, and we in music studies find ourselves, as usual, both ahead of the curve and behind it. Like our cognate cousins in art history, film studies, and performance studies, we have reasonable excuse for lagging behind computational literati like Franco Moretti and Matthew Jockers, whose Stanford Literary Lab has been opening debates across the humanities1 and making headlines in the popular press. The excuse is technical: it is easy to transform literary texts into searchable data, and hard to do so for music. Insofar as the content of a novel is made out of words, it can be encoded as a long string of small numbers (representing letters and spaces) without losing any information. Even if we allow ourselves to believe that a musical score is an adequate representation of the content of a work, the encoding problem is vastly more complex. Strings of characters are monophonic, which most music is not. Thus music notation also specifies a coordination of temporal structure within and between the simultaneously sounding parts. Music notation, like written language, is very good at what it does, but while written language is easily turned into standardized, manipulable data, written music is not.2 (I have in mind here even very basic forms of manipulation, analogous to changing the type size and style on an e-book reader.)When it comes to literary texts and historical documents, the simplicity and standardization of encoding, together with optical character recognition (OCR) technology, has made it possible to assemble staggeringly large repositories of symbolically encoded, searchable text. The Google Ngram project's English-language corpus, for example, has processed 4.5 million books containing 500 billion words.3 A separately available subset devoted to fiction weighs in at 630,000 books containing 65 billion words. Literary scholars wanting to construct their own corpora can do so with relative ease; scanners today are ubiquitous, fast, and reliable.Musicologists are at a distinct disadvantage here. Existing mass-market technologies for music recommendation and recognition, while successful at what they do, rely on purpose-built databases that are of limited interest and/or availability to musicologists. Music-recommendation applications depend primarily on user ratings to track which songs tend to be preferred by the same users. Pandora's service also uses a rich collection of metadata (the so-called Music Genome Project), which associates individual tracks with 150–500 pieces of information about characteristics like genre, style, mood, and instrumentation. (This metadata is generated by trained analysts, and is guarded by the company as valuable intellectual property.) Music-recognition applications typically use timbral information, a sort of audio fingerprint, to identify recorded music. For an application like Shazam, Andras Schiff's two recordings of the Goldberg Variations are as different from each other as either is to the latest pop chart-topper. The recording-based ontology that lies at the heart of these mass-market technologies is in opposition to the work- or score-based ontology typical of musical analysis. This latter ontology requires a type of data known as symbolic encoding, a machine-readable equivalent to a musical score.Algorithms for transcribing audio files into symbolic data akin to music notation are far off on the horizon. Optical music recognition technology is further along, but substantial technical hurdles involved in score scanning make for slow progress, particularly given the lack of the kind of market pressures that drove the development of OCR technology. Vladimir Viro's Peachnote project has created the largest corpus of symbolic music data: 160,000 public-domain scores containing 370 million notes.4 For research purposes, however, this project is still at the proof-of-concept stage, even if one denies oneself the luxury of worrying about editions. On one hand, the low quality of the underlying score images compounds the difficulty of the automated transcription task, resulting in unreliable data. On the other hand, even perfect musical data is difficult to search, for all the same reasons it is difficult to encode. A musical surface of even moderate complexity is not easily divided up into melody and accompaniment, even when symbolically encoded; what is readily heard—a cadence, a varied repetition—may be impossible to specify as a search query.Even in the absence of large, reliable corpora of symbolically encoded music—perhaps because of it—it is not clear what kinds of queries could be performed on musical data. The comparison with language is again apposite. An entire field, computational linguistics, has been devoted for over half a century to the development of methodologies for mining text data.5 Industrial and military organizations interested in applications such as machine translation, speech transcription, and document summarization have supported much of this work. These methods are easily transferred over to literary studies, as Jockers has recently shown in detail.6 Computer-savvy music theorists have been working on extensions of computational-linguistics methods, but this effort is still in its infancy.7And yet, at least since the middle of the last century, the Big Data spirit has been apparent in the work of musicologists like Leonard Meyer, Jan LaRue, Robert Gjerdingen, and James Hepokoski. Before Moretti turned “distant reading” into a brand name for his paradigm of literary historiography, these scholars were interested in questions about the nature and evolution of musical styles and genres. Such questions focus not (or not only) on detailed accounts of individual works, but on trends manifested in large quantities of music, studied systematically. Only recently, however, have we seen the widespread release of software to aid in such study, including the two systems reviewed here.No systematic musicologist has been anywhere near as successful at capturing the popular imagination as the composer David Cope. Cope's Experiments in Musical Intelligence (EMI) project has sought to understand musical styles so well that one could write new music in that style: piano concertos in the style of Mozart, sonatas in the style of Beethoven, chorales in the style of Bach. (Cope has published a set of 5000 of the latter.) The subtitle of Cope's most recent scholarly book, Hidden Structure, is “Music Analysis Using Computers.” The sort of analysis he has in mind in this book, however, is not the distant reading of corpus analysis, but good old-fashioned close reading of musical structure as promulgated in standard theory curricula. Cope's analytical project owes more to Allen Forte than to Meyer. The “hidden structures” revealed by his computer programs are pitch-class sets à la Forte, serial fragments à la Babbitt, chord roots and tensions à la Hindemith, and models of registral space à la Bernard.Cope's point of departure is a set of four principles: “(1) all music consists of patterns; (2) all pitch patterns can be reduced to scales; (3) all elements of scales have different functions; and (4) all patterns, scales, and functions in music are best understood by modeling their processes” (p. 1). His first chapter outlines a history of music theory from Pythagoras to David Huron, framed in terms of these principles, and placing special emphasis on the sort of algorithmic thinking that gave rise to musical Würfelspielen and other automated systems for generating music. The chapter concludes with a very brief (non-computational) analysis of the third of Stravinsky's Three Pieces for String Quartet, emphasizing harmony, polytonality, and form; and an equally brief description of two computer programs on the accompanying CD-ROM, one of which is an interactive set-class calculator and the other of which draws analytical graphs of certain features (pitch, texture, duration, dynamics, and “relation to beat”) in an encoded score.Each chapter follows an identical (and equally disjointed) format to the first: a lengthy exposition of one or two technical topics is followed by a short analytical excursus of a small number of pieces, and finished with a description of a handful of programs contained on the CD-ROM. The analytical procedures deployed in the second part of the chapter may or may not be related to the expository material they follow, and they may or may not involve procedures that are accomplished by the programs whose descriptions they precede.Consider chapter 5 (“Function and Structure in Post-Tonal Music”), which presents some of the most novel material in the book.8 Cope begins with an eight-page summary of object-oriented programming, a topic commonly taught in introductory computer-science courses. He then turns, somewhat precipitously, to the exposition of a general theory of harmonic function for both tonal and post-tonal music. For Cope, the harmonic function of a chord (understood generally as a set of pitches in a particular registral configuration) depends on the chord's root, tension, and context. The root is determined using Hindemith's voicing-dependent method, which identifies a single root for any arbitrary collection of pitches.9 The tension of a chord is equal to the sum of the tension values for each interval in the chord above the bass; Cope specifies tension values for intervals ranging from the unison to the perfect twenty-ninth (four octaves). For Cope context is an umbrella term for additional sources of chordal tension that come from meter, rhythm, and root motion. Figure 1 reproduces Cope's figure 5.12, which calculates the total tension (inherent and contextual) for each chord in fragments from Bach and Schoenberg.The chapter continues with a discussion of Cope's taxonomy of functions. Intriguingly, he makes no theoretical distinction between harmonic function and formal function, defining both in identical terms. He distinguishes only between structural levels: harmonic function is a local feature of surface events, and formal function a feature of larger spans. His system, named SPEAC after the initials of his five functional categories, does not make direct reference to a tonic like standard systems of harmonic function (e.g., Riemannian function or Roman numerals). Instead his functions are determined by tension and named after common phrase types; in order of increasing tension, the functions are C onsequent, S tatement, E xtension, P reparation, and A ntecedent. Figure 2 reproduces Cope's figure 5.13, which provides functional analysis of the chords in Figure 1. Subscripts indicate a hierarchy; “a C1 has more C-ness than a C2” (p. 214). Unfortunately, though Cope claims the functions in Figure 2 depend on the tension values in Figure 1, they do not seem to obey Cope's tension ordering as described earlier in this paragraph. Both examples, for example, begin with the function P2, a relatively high-tension function, even though in both cases the calculated tension value for the first chord is among the lowest in the passage. In the accompanying text, Cope implies that his functional analysis is handmade (on the basis of the analyst's priorities) rather than calculated by the computer (on the basis of latent structures in the music): “Analysts have the right (even the obligation) to assign SPEAC symbols as appropriate to their personal interpretations. … This strategy allows one to interpret numerical scores more liberally, a fact that may give some readers pause” (p. 214).The reader hoping for a computer program that makes SPEAC analysis will not find one described in this chapter. Of the two programs described at the end of the chapter, one calculates roots according to the Hindemith procedure and the other creates pictures called Structure Maps, which are not described in any detail anywhere. (They seem to be diagrams of notes with lines connecting every note to every other note; certain notes that are deemed hierarchically superior according to some unspecified procedure are circled.) A program for making SPEAC analyses is, in fact, included on the CD-ROM, but only in chapter 7, which does not describe it. The index does not help: the headword “SPEAC” indexes the pages in chapter 5 that describe the theory, and includes an instruction to “see also SPEAC Program.” The headword “SPEAC Program,” though it appears in the index, has no page references whatsoever.Despite the befuddling organization of the first six chapters, everything does come together in the end (chapter 7, “A Look to the Future”). Here the program Cope presents is Muse, a system that processes a piece (provided it has been input in Cope's format) and produces a comprehensive analysis of its pitch sets, their roots and SPEAC functions, the scales they instantiate, together with an analysis of the “information content” of pitch, dynamics, rhythm, and texture. Cope, following the semiotics of Jean-Jacques Nattiez,10 seeks to produce a characterization of the “neutral level” of a piece of music, aiming for as unbiased a description of the music as possible: “Although nonetheless not without biases, the Muse program described here will hopefully, by applying relevant but thorough analytical processes to the music in its database, reveal previously undetected and useful results” (p. 305). Here Cope suggests that the great possibility of computational analysis is the ability of the “machine eye” to detect regularities and trends that might go unseen by an analyst bound by the usual biases and predilections. The reader interested in examples of such results, alas, will not find them in Cope's book.Even if one were interested in a Muse-generated account of a work's otherwise undetected neutral-level structure, or of structural trends across a corpus, the program provided on the CD-ROM turns out to be of very limited value. Cope's programs are written in two dialects of Lisp, a recondite language popular among artificial-intelligence researchers in the seventies and eighties, but no longer used much.11 (MIT's basic computer science courses were for years conducted in Scheme, a close cousin of Lisp, but right the Cope's out they to a language that is both to work with and more make all of Cope's programs that involve were written in Lisp, which can be only on only with of this book, this will be to use the though it is possible to the with its the of Hidden Structure, Cope that “a common data structure and software such a structure can the perfect for all of analytical programs to any style of (p. Cope is not his own system, but a for the of algorithmic analysis that is further out in chapter He may as well have been software has made a and in Cope, he does not a particular theory of musical structure or a of analysis. is not to a set of but to a that provides to and corpora of symbolically encoded music. its to research programs of their In this most David a of that a of analytical first in and last in is and all systems and the two systems is their basic on the software of small to do one and to do it is the of a data or or all of a These software work together in a that is described using the of a has an input into which data is and an from which the data The can be together in with the of one into the input of the This has the great of easy to use and the of such a is limited by the very same that make the easy to string as an musical could not be used to a or an is as a collection of extensions to a language used by and in makes it possible for the user to more easily than with A program that uses can easily and data structures without them into of data that from one into however, the of with makes it possible to of the the has created for text, and even heart of is a and general data structure a are that arbitrary of data, each of which is with a particular point in the are by a number by is as the number of notes since the of the begins at The data can be of any This includes both musical (e.g., and general data (e.g., also includes a can other each of which begins at a within the larger a typical a score encoded in a it as a special type of called a The score is in made up of to the or which may in be made up of further to different further to the individual of these are the that the and so Thus a piece of music is as a of This representation is of any particular is of reading at least of the most common for musical data, and even more and provides methods for can of to them them well as of a of algorithmic analytical procedures and his have from all of the The user of is to additional to the provided it for and a of the type of research that makes possible in the corpus is a collection of from the we to about the of and over in this The shown in Figure this information from each of the files in the a of all in the corpus that to The lines has two parts. it to the to it from notation into a to the from the The and allow the program to from or which from to in as a each is in the The second part of the and the and and in the first of the first term for an or in a and find the first and in that Both of these are encoded as data structures with to the the our the an that the number of (or in the of a in the the and we them by a them out all the data on a single The of the is shown in Figure the the of a more of the which and data across the corpus, both and in might have that these two are of one is no to believe that are with particular patterns of Figure that this a term from is in or use with or four every in a or is in or as is a large of in in have a of they for example, more than in other to use a about as as and to use a and about as as to and like these do not have much value they are The by this is not particularly and the a number of this of and to this collection of or can we find it in a do the have to the history of the system of this and the of with the any on the be in The of the new availability of musical data, and like for mining the data, is that to such questions can be generated by the relatively of a and it have the part of a to up the and in this corpus of and scholars so much on such an we can go from to (and to more in half an or applications for in are and are the are to use it. is using in his own research on music, and is also software for music theory that will in a has used it to of the using it to an interactive harmonic of chorales that allows to and for particular chord and patterns, then the search in music and have also used it in a in with for computational linguistics, to latent structure in the Bach development is not the focus of work, but comes with a (and corpus of music from including a substantial of music. are by no limited to the corpus that comes with researchers interested in music, for example, can data from or of scores by and of encoded scores are to them to the On the when a project like Peachnote is to produce large of corpus data, of will be well to the The for Big Data project is in

  PublisherDOI

• Prosodic Boundaries in Singing: An exploration of the temporal effects of linguisti c structure in western music

  2013-01-01 · 1 citations

  articleSenior author
  Publisher

• Ian Quinn Responds

  Music Theory Spectrum · 2011-04-01

  article1st authorCorresponding

  Journal Article Ian Quinn Responds Get access Ian Quinn Ian Quinn Yale University Search for other works by this author on: Oxford Academic Google Scholar Music Theory Spectrum, Volume 33, Issue 1, Spring 2011, Page 106, https://doi.org/10.1525/mts.2011.33.1.106 Published: 01 March 2011

  PublisherDOI

Frequent coauthors

• Dmitri Tymoczko

  9 shared

• Clifton Callender

  Florida State University

  9 shared

• Manesh R. Patel

  Duke University Health System

  6 shared

• Margaret Crandall

  Yale University

  4 shared

• Navarro Mcdonald

  Yale University

  4 shared

• Richard Boston

  University of California System

  4 shared

• Lucy Taylor

  University of California System

  4 shared

• Marc Muench-Nasrallah

  Yale University

  4 shared

Labs

• Ian Quinn LabPI

Awards & honors

• Emerging Scholar Award, Society for Music Theory (2004)
• Outstanding Publication Award, Society for Music Theory (200…
• Outstanding Publication Award, Society for Music Theory (201…