The MIT Music Technology and Computation (MTC) Graduate Program, a collaborative initiative between the School of Humanities, Arts, and Social Sciences (SHASS) and the School of Engineering (SoE), held its inaugural research showcase on May 13, marking a significant milestone for the institution’s ambitious foray into the intersection of artificial intelligence, engineering, and musical expression. The event, which drew a standing-room-only crowd to the Edward and Joyce Linde Music Building’s Thomas Tull Concert Hall, featured research presentations and live performances from the program’s first cohort of five master’s students, alongside PhD candidates and faculty. The showcase demonstrated how the program is cultivating a new generation of researchers who are not merely applying technology to music but are fundamentally rethinking the creative process through machine learning, neural interfaces, and generative systems.
MIT’s New Graduate Program in Music Technology and Computation
Launched in fall 2024, the MTC Graduate Program was designed to bridge the gap between MIT’s renowned engineering disciplines and its music and theater arts programs. The showcase served as a public debut for the program’s first five enrollees, all of whom were previously MIT undergraduates. The 90-minute event blended technical presentations with live performance, reflecting the program’s core philosophy that music technology is as much about artistic practice as it is about algorithmic rigor. SHASS Dean Agustín Rayo set the tone for the evening, stating that the program’s goal is for MIT to lead the world in music technology theory and application, emphasizing that the work is about shaping the future of expression in an AI-driven world.
Student Research Projects: From Brain-Computer Interfaces to Generative Dance Music
The research on display covered a remarkable breadth of topics, each project demonstrating a sophisticated integration of machine learning, signal processing, and creative design. One of the most striking presentations came from Claire Southard, who developed a machine-learning model capable of identifying musical notes from electroencephalogram (EEG) signals. Southard’s work aims to help musicians with movement disorders—such as Parkinson’s disease or dystonia—by translating the music they imagine directly from their brain activity, bypassing the need for motor control. Her system trains models to predict imagined music, with early results showing that many predicted pieces are recognizable representations of the user’s intended melody. This research represents a tangible step toward a more accessible future for music performance, where physical ability is no longer a barrier to creative expression.
Another standout project was presented by Mariano Salcedo, who built a custom web application that generates unique, emergent visuals driven by real-time streaming music. Salcedo’s algorithms leverage the complex visual behavior of self-organized systems—specifically neural cellular automata—to create aesthetically synergetic visualizations that respond dynamically to audio input. In his address at the SHASS Advanced Degree Ceremony, Salcedo urged his peers to lead the way in human- and humane-centered technology, asking not just what can be built, but who it will affect and who it will benefit.
Other projects included a real-time visualization of an AI co-improvising agent’s internal state, a sound-art installation based on noisy network communication, and a hip-hop dance circle where music is generated from the dancers’ movements. A particularly novel project involved a musical dialogue between a cello and a real-time diffusion model trained on whale songs, exploring cross-species musical communication.
How Does MIT’s Music Technology Program Train Researchers in Human-AI Collaboration?
The program’s approach to training researchers is deeply rooted in co-design and interdisciplinary collaboration. Associate Professor Anna Huang, a leading researcher in collaborative human-AI music-making and a graduate of the MIT Media Lab, delivered a keynote address titled “In Search of Resonance in Human-AI Interaction.” Huang emphasized the importance of centering the human musician in all AI-related work, while also making efforts to include musical traditions from around the world. She described the unique environment at MIT, where researchers work directly with extremely talented musicians in the studio, iterating weekly on creative processes. This co-design approach ensures that the technology grows alongside the creative practice, pushing both forward simultaneously. Huang will co-teach a new subject in the fall, 21M.369/569 (Tuning Attention: Creative Practices in Movement, Sound, and AI), which will introduce students to motion-capture technologies, critical interaction design, and generative modeling through the lens of improvisation and somatics.
Program Growth and Future Directions
The MTC program is already scaling rapidly. Director Eran Egozy reported that the program admitted 10 master’s students for the 2026-27 academic year from over 100 applicants. Unlike the inaugural cohort, which consisted entirely of recent MIT graduates, next year’s class will include students from other institutions, bringing a wider range of perspectives and experiences. Additionally, three shared faculty members between the Music and Theater Arts Section and the Department of Electrical Engineering and Computer Science—Mark Rau, Paris Smaragdis, and Anna Huang—are inviting new PhD students to their labs. This expansion signals that the program is not a one-off experiment but a sustained institutional commitment to building a vibrant, multidisciplinary research community at the intersection of music, computation, and AI.
What This Means for the Future of AI in Creative Fields
The inaugural showcase made clear that the MIT Music Technology and Computation Graduate Program is already producing research that challenges conventional boundaries between human creativity and machine intelligence. The projects presented are not merely academic exercises; they address real-world problems—from accessibility for musicians with disabilities to new forms of human-AI co-creation. For professionals and developers in the AI and software space, the program’s output offers a compelling model for how to build technology that is both technically sophisticated and deeply human-centered. The emphasis on co-design, where musicians and engineers work together in iterative cycles, provides a practical framework for developing AI tools that augment rather than replace human creativity.
For readers interested in exploring these ideas further, the program’s research projects and faculty publications are publicly available through MIT’s Music Technology and Computation website. Developers and researchers can also experiment with open-source tools and models related to neural audio synthesis, real-time generative systems, and brain-computer interfaces for music. The key takeaway is that the most impactful AI applications in creative fields will likely emerge from environments where technical rigor is matched by a deep understanding of artistic practice—a balance that MIT’s new program is demonstrating with impressive results.
