Tung-Hui Hu

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Hu is a media scholar interested in the historical and theoretical context of digital culture and AI. His first monograph, A Prehistory of the Cloud (MIT Press, 2015), explored the material infrastructure of the cloud, while Digital Lethargy (MIT Press, 2022), explored the racialized dimensions of labor within AI through key works of art and literature. He is at work on a new project, A History of the World in 7 Datasets. He is also a core faculty member of the Helen Zell Writers’ Program, U-M’s MFA program in creative writing.

Hao-Wen Dong

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My research aims to empower music and audio creation with machine learning. My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation. I am broadly interested in music generation, audio synthesis, generative AI, multimodal learning, and music information retrieval. I study a wide range of topics centered around Generative AI for Music and Audio, including multitrack music generation, automatic instrumentation, automatic arrangement, automatic harmonization, music performance synthesis, text-queried sound separation, text-to-audio synthesis and symbolic music processing software.

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

Catherine Leonhard

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Catherine is a junior at the University of Michigan’s STAMPS School of Art & Design majoring in Art & Design and minoring in Computer Science. Her interests include Web Design, Graphic Design, and Photography and she is studying to become a Web Designer.

Áine Heneghan

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Professor Heneghan’s research interests include music analysis, study of archival documents, the history of music theory, and the Second Viennese School. Her new research project examines the corpus of piping tunes collected by James Goodman in south-west Ireland during the mid-1800s. Funded by MIDAS, this work is part of a larger project with colleagues in music theory, statistics, and linguistics entitled “A Computational Study of Patterned Melodic Structures across Musical Cultures.”

Mousumi Banerjee

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My research is primarily focused around 1) machine learning methods for understanding healthcare delivery and outcomes in the population, 2) analyses of correlated data (e.g. longitudinal and clustered data), and 3) survival analysis and competing risks analyses. We have developed tree-based and ensemble regression methods for censored and multilevel data, combination classifiers using different types of learning methods, and methodology to identify representative trees from an ensemble. These methods have been applied to important areas of biomedicine, specifically in patient prognostication, in developing clinical decision-making tools, and in identifying complex interactions between patient, provider, and health systems for understanding variations in healthcare utilization and delivery. My substantive areas of research are cancer and pediatric cardiovascular disease.

James Kibbie

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James Kibbie, DMA, is Professor and Chair of the Department of Organ in the School of Music, Theatre & Dance and University Organist at the University of Michigan, Ann Arbor.

Professor Kibbie’s current research will develop and analyze a library of digitized performances of Bach’s Trio Sonatas, applying novel algorithms to study the music structure from a data science perspective. The team’s analysis will compare different performances to determine features that make performances artistic, as well as the common mistakes performers make. Findings will be integrated into courses both on organ performance and on data science. The project Investigators are Daniel Forger, professor of mathematics and computational medicine and bioinformatics and Professor Kibbie.

 

Ramon Satyendra

Ramon Satyendra

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Ramon Satyendra, PhD, is Associate Professor of Music Theory and Director of Graduate Studies in the School of Music, Theatre & Dance at the University of Michigan, Ann Arbor.

Professor Satyendra holds a doctorate from the University of Chicago in music theory and history. Before coming to Michigan, he taught at Yale University and the University of Chicago. He currently serves on the editorial boards of The Journal of Mathematics and Music, Intégral, and Analytical Approaches to World Music. Highlights of previous service to the field include Executive Committee of the Society of Music Theory, editorial board of Music Theory Spectrum, and editor of the Journal of Music Theory.  Among his awards are the Merten Hasse Award in Mathematics from the Mathematical Association of America and the Clauss Prize for Teaching Excellence in the Humanities from Yale University. He is a three-time fellow of the Mannes Institute for Advanced Studies in Music Theory. Satyendra’s research interests include music and mathematics, late nineteenth-century music, jazz, South Asian music, and compositional theory. He plays piano, organ, tabla, and guitar and has published in Music Theory Spectrum, Music Analysis, Journal of Music Theory, American Mathematical Monthly, and elsewhere.