Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Swiss Federal Institute of Technology in LausanneSwitzerland
Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.
Matthew Santa serves as Professor of Music Theory and Chair of the Music Theory and Composition Area at Texas Tech University School of Music, with prior teaching appointments at Queens College and Hunter College. His leadership shapes curriculum development and academic initiatives within the department. Academic credentials include advanced degrees from Louisiana State University and The City University of New York, establishing foundational expertise in theoretical frameworks and analytical methodologies. Research focuses on post-tonal analysis , diatonic set theory , and parsimonious voice leading , bridging complex theoretical concepts with practical pedagogy. His work integrates popular music analysis and metrical studies, emphasizing accessibility for diverse learners through innovative teaching resources. Publications demonstrate evolving scholarly trends from late-20th century set theory toward contemporary applications in musical form and rhythm. Recent textbooks synthesize Rothstein, Krebs, and Mirka's theories into unified analytical approaches for undergraduate and graduate education. Scientific recognition includes: MTSNYS Young Scholar Award (1998) Mentorship encompasses graduate and undergraduate students across composition and theory disciplines, though specific advisees aren't documented in source materials. No grant funding details appear in available records. Collaborative projects include the Flute/Theory Workout series with Lisa Garner Santa and Thomas Hughes, blending performance technique with theoretical concepts through MIDI accompaniment systems.
Simon Dixon is a Professor of Computer Science and Director of the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM CDT) at Queen Mary University of London. He also serves as Deputy Director of the Centre for Digital Music (C4DM). His work focuses on music informatics, AI, and computational musicology, with emphasis on music signal analysis, performance modeling, and MIR applications. He leads projects funded by UKRI, Innovate UK, and industry partners like Yamaha and Spotify. Dixon has supervised over 20 PhD students and contributed to major initiatives like the Jazz Digital Archives Project and the Dig that Lick study on jazz melodic patterns. His research has been recognized with awards including the Peter Claricoats Award and Turing Fellowship. Education: PhD in Computer Science, BSc(Hons) in related field, with music qualifications (AMusA LMusA) Roles: AIM CDT Director, C4DM Deputy Director, EU H2020 MIP-Frontiers PI Research: Music transcription, expressive performance analysis, MIR, and AI applications in music education Key projects include industry collaborations (e.g., Yamaha for jazz piano modeling), semantic audio analysis, and large-scale music corpus studies. His team has pioneered methods in chord detection, source separation, and alignment algorithms, with top rankings in MIREX evaluations. Publications span journals like TISMIR and ICASSP, with a focus on foundational MIR techniques and AI-driven music systems. His work bridges technical innovation with cultural heritage through projects like JazzDAP and the Dig that Lick analysis of jazz solos.
Guy Azebové Tetang is a Lecturer at the Faculty of Law of the University of Montreal, specializing in comparative constitutional law with a focus on sub-Saharan Africa. His research explores the interplay between constitutional 'standards' (universalism) and 'localisms' (relativism), particularly in Francophone Africa. He holds a PhD from the University of Montreal (2023), supervised by Professors Jean-François Gaudreault-Desbiens and Pierre Noreau. Education: Doctor of Law (PhD), Université de Montréal (2023). Research Interests: Constitutional transplants, African political imaginaries, legal hybridity, postcolonial constitutionalism, and the tension between global legal norms and local practices. His work analyzes how African states navigate constitutional borrowing from Western models while reinterpreting them through local contexts. Key themes include constitutional polymorphism, legal reappropriation, and the 'constitutional threshold' as a space of cultural negotiation. Notable Achievements: 2024 AHJUCAF Special Mention Prize (for doctoral thesis), exceptional mention for thesis defense (2023), and Pierre Noreau/Jean-François Gaudreault-Desbiens doctoral scholarship (2016). His publications include chapters in *From One Threshold to Another* (2017) and *Secrets of States* (2017), exploring constitutional dynamics in Africa and Canada. He has presented at conferences such as *Les après-midis de la Justice* (2022) and the CRDP Young Researchers' Lunchtime Conferences (2020), discussing topics like Cameroon's social justice challenges and judicial power excess. Current Affiliations: CRDP (Centre de recherche en droit public), where he contributes to research groups on law and new social relations. His work intersects with legal pedagogy, interdisciplinary collaboration, and global justice studies.
Sageev Oore is an Associate Professor in the Faculty of Computer Science at Dalhousie University, a Research Faculty Member at the Vector Institute for Artificial Intelligence, and a Canada CIFAR AI Chair. He previously served as Associate Professor and Chairperson in the Department of Mathematics & Computer Science at Saint Mary’s University and spent 2016–2018 as a Visiting Research Scientist at Google Brain, working on the Magenta team. Faculty of Computer Science, Dalhousie University Vector Institute for Artificial Intelligence Google Brain (2016–2018) Saint Mary’s University (former) Sageev Oore's research centers on machine learning and deep learning, with a strong focus on creative applications in music, audio processing, and computational creativity. His work bridges the gap between technical innovation and artistic expression, developing systems that generate and interact with music using neural networks. He has made significant contributions to generative models for music, including the development of PerformanceRNN and other interactive systems. His recent publications highlight advancements in out-of-distribution detection (Gram-OOD), interactive music generation, and deep learning tools for creative domains. These works reflect a consistent trend toward building intelligent, user-centered systems that enhance human creativity through AI. Canada CIFAR AI Chair (2018) Best Paper Award, CVPR ISIC Workshop (2020) Outstanding Demonstration Award (Runner-up), NeurIPS (2020) Best Demonstration Award, AAAI (2017) Best Demonstration Award, NeurIPS (2016) Sageev Oore actively mentors graduate and undergraduate students, with well-funded research positions available for motivated candidates. His collaborations span academia and industry, including major projects with Google Brain and interdisciplinary work with artists. He leads research initiatives in AI-driven creativity and is deeply involved in the Canadian AI ecosystem through the Vector Institute and CIFAR. His work is supported by significant grants and affiliations, including the Canada CIFAR AI Chair program, which funds his research in foundational AI and its applications. He is also part of the Magenta project at Google, contributing to open-source tools for art and music generation. Sageev Oore leads a research group focused on deep learning for creative applications, with projects in music generation, audio synthesis, and human-AI interaction. His lab collaborates with musicians, artists, and healthcare researchers, fostering a transdisciplinary approach to AI innovation.
Shawn Greenlee is a Professor and Department Head for Digital + Media at Rhode Island School of Design (RISD), where he also leads the Studio for Research in Sound & Technology (SRST). His academic and artistic career spans over two decades, with significant contributions to the fields of sound art, computer music, and spatial audio research. Greenlee's educational background includes: BFA from Rhode Island School of Design MA from Brown University PhD in Computer Music and New Media from Brown University Greenlee's research interests focus primarily on spatial audio , high density loudspeaker arrays , and erratic sound synthesis techniques . His work explores the intersection of technology and sound art, with particular attention to how sound behaves in physical space and how it can be manipulated through computational methods. He has developed custom software for rhythm composition based on geometric principles from Godfried T. Toussaint's work, demonstrating his interest in algorithmic approaches to music creation. His research also extends to soundscape ecology, as evidenced by his Polar Lab Residency project "Edges in the Alaskan Soundscape," where he conducted ambisonic field recordings in Alaska's Kenai Peninsula and Denali National Park. Analysis of Greenlee's recent artistic output reveals a consistent exploration of spatial sound phenomena and computational approaches to sound creation. His works span from live electronic improvisation to carefully composed spatial sound installations. A notable trend is his focus on environmental sound documentation and transformation, particularly evident in projects like "Sifting" which emerged from his Alaskan field recordings. His technical expertise in ambisonic recording and high-density speaker arrays enables him to create immersive sound experiences that challenge conventional listening perspectives. Greenlee's recognition includes: Polar Lab Residency supported by the Anchorage Museum (2018) for "Edges in the Alaskan Soundscape" As Department Head for Digital + Media at RISD and leader of the Studio for Research in Sound & Technology, Greenlee guides multiple research initiatives and mentors students in sound art and technology. His upcoming presentations at ICMC 2025 in Boston demonstrate ongoing research collaborations, particularly in loudspeaker array design and spatial audio techniques. His work with students is evident in projects like the "Sound Design for Mobility" group, which produced binaural field recordings during the 2020 pandemic. Greenlee directs the Studio for Research in Sound & Technology (SRST) at RISD, which serves as a hub for experimental work in spatial audio, sound installation, and computational sound design. The studio supports research in high-density loudspeaker arrays, ambisonic recording techniques, and novel approaches to sound synthesis. Recent projects from the studio include work presented at major conferences like ICMC and NIME, demonstrating its active research agenda and technical innovation in the field of computer music.
National Higher School of Electronics and its ApplicationsFrance
Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.
Massachusetts Institute of TechnologyUnited States
Cheng Zhi Huang is the Robert N. Noyce Career Development Professor and Assistant Professor at MIT, holding a shared appointment between the departments of Music and Theater Arts and Electrical Engineering and Computer Science (EECS). His work bridges artificial intelligence, music technology, and computer science to advance human-AI collaboration in musical creativity. Huang leads research in generative models for music composition, real-time interactive systems, and expressive performance synthesis. His contributions include tools like ReaLJam for AI-assisted jamming and the MAESTRO dataset for piano performance modeling. His research interests span AI-driven music generation, human-AI interaction frameworks, and culturally-aware music technologies. Notable projects include The Bach Doodle—an accessible web-based composition tool—and MIDI-DDSP for detailed performance control. Huang’s work emphasizes ethical and creative applications of AI in arts, fostering collaborations between musicians, engineers, and computer scientists. His publications highlight advancements in hierarchical generative modeling, source separation techniques, and co-creation interfaces for novices. Huang’s research has been showcased in venues like TISMIR and IEEE conferences, reflecting his interdisciplinary impact on music technology and machine learning.
Dr. Philippe Pasquier is a Professor at Simon Fraser University's School of Interactive Arts and Technology, where he directs the Metacreation Lab for Creative AI. His research-creation program integrates scientific research on generative AI and machine learning with artistic practice in computer music and interactive art. Research focuses on creative AI systems for artistic tasks, including multi-track music composition (Calliope), timbre synthesis, visual synthesis (Autolume), and cross-modal generation. Applications span creative software tools, interactive installations, and audiovisual performances studied through HCI methodologies. Publications demonstrate consistent innovation in generative systems, with recent work exploring controllable music generation (MIDI-GPT), GAN-based visual synthesis, and evaluation frameworks for creative AI. Artistic works have been exhibited globally at venues including Ars Electronica, Centre Pompidou, and ZKM. Secured research funding from NSERC, SSHRC, CFI, and international agencies. Founded key academic initiatives including the International Workshop on Musical Metacreation (MUME), Movement and Computation conference (MOCO), and chaired ISEA2015. Teaches creative AI, sound design, and interdisciplinary computing approaches.
Pasquale Lisena is a Research Fellow in the Data Science department at EURECOM, where he contributes to the Data2Knowledge research group. His work bridges academic research with practical applications in knowledge-intensive domains. Education: PhD in Computer Science from EURECOM / Sorbonne University (2019), thesis: "Knowledge-based music recommendation: Models, algorithms and exploratory search" supervised by Raphaël Troncy His research centers on Knowledge Graphs, Knowledge Engineering, and Recommender Systems with strong applications in cultural heritage and music. Key specializations include: Semantic Web technologies for domain-specific knowledge representation Information extraction from multimedia and text sources Development of ontologies for complex cultural domains Music metadata modeling and recommendation systems Recent publications reveal a trajectory from foundational music metadata work (2018-2019) toward cutting-edge applications in olfactory heritage (Odeuropa) and language model-driven recommendation (2025). His research consistently integrates knowledge graphs with machine learning, demonstrating expertise in both classical music informatics and emerging sensory heritage domains. Awards: Best Resource Paper Award (2022) for Odeuropa Data Model publication Funding and Collaboration: ANR JCJC project coordinator for kFLOW (2022-2024) Key contributor to EU H2020 projects including Odeuropa, SILKNOW, and MeMAD Extensive cross-institutional collaboration across European cultural heritage initiatives He operates within EURECOM's Data2Knowledge group, participating in international research consortia focused on knowledge-intensive applications in cultural heritage preservation and digital humanities.
Massachusetts Institute of TechnologyUnited States
Anna Huang is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the PI Core/Dual program. Her research focuses on AI-driven music technologies, including human-AI collaboration, generative music models, and interactive creative tools. She specializes in developing frameworks for real-time music jamming, adaptive accompaniment systems, and novice-friendly AI co-creation platforms. Huang has contributed to projects like the Bach Doodle and the AI Song Contest , demonstrating scalable applications of machine learning in music composition. Her work bridges computer science and musicology, with a particular emphasis on cross-cultural music generation (e.g., Hindustani classical music modeling) and expressive control mechanisms for generative systems. Key areas include MIDI signal processing, source separation algorithms, and the design of user interfaces that empower both professionals and novices to co-create with AI. Huang’s publications emphasize interdisciplinary innovation, with trends spanning reinforcement learning for music performance, hierarchical generative modeling, and ethical considerations in AI-assisted creativity. Though no awards are explicitly listed, her impactful projects suggest recognition in computational music research. Her research also involves dataset development (e.g., MAESTRO dataset) and open-source tools like Coconet, fostering reproducibility and community engagement in music technology.
Scott D Barton is an Associate Professor in the Department of Humanities & Arts at Worcester Polytechnic Institute (WPI), with affiliations in Robotics Engineering, Psychology, and Interactive Media & Game Development. His work bridges music, robotics, and cognition, focusing on robotic musical instruments, human-robot interaction, and perceptual studies. He holds a BA from Colgate University (1998), an MMus from Brooklyn College (2006), and a PhD from the University of Virginia (2012). Research interests include designing automated musical instruments, exploring how cognitive processes shape musical perception, and applying AI to music creation. Key projects involve the Music, Perception and Robotics Lab , where he develops systems like Cyther (a self-tuning robotic zither) and Parthenope (a robotic siren). His work has been showcased in venues like the International Conference on New Interfaces for Musical Expression. Barton emphasizes interdisciplinary collaboration, teaching students to merge technical skills with creative expression. Media coverage includes interviews in The New York Times and The Telegram & Gazette , discussing topics like urban sound phenomena and robot-human concerts. His performances blend algorithmic composition with live robotics, exemplifying WPI's focus on innovative engineering and art. Grants and collaborations are central to his work, though specific funding details are not provided. Advising focuses on guiding students in creating novel musical technologies and understanding perceptual frameworks. Upcoming events include performances at the International Computer Music Conference and workshops on computer music technology.
Erik Nystrom is a composer and academic at City, University of London, specifically affiliated with City St George's, where he conducts research and artistic work in electroacoustic and computer music. His practice centers on multichannel sound, spatial texture, and live interactive performance using algorithmic systems. He previously held a Leverhulme Early Career Fellowship at the University of Birmingham’s BEAST studio, contributing significantly to spatial synthesis and performance aesthetics. Education: PhD in Electroacoustic Music, City University, London (supervised by Denis Smalley) MA in Electroacoustic Music, City University, London (supervised by Denis Smalley) Erik’s research explores the intersection of human and machine agency in improvisation, focusing on post-human cognition, nonconscious processes, and the concept of intra-action. His work integrates machine learning, agent-based systems, and real-time synthesis, particularly through the SuperCollider environment. He investigates how algorithmic systems can co-create with human performers, generating emergent sonic textures through feedback and listening behaviors. Key themes include spatial sound, acousmatic improvisation, and the philosophical underpinnings of technological creativity. His 15 most recent works and publications reveal a strong trend toward algorithmic improvisation, cognitive assemblages, and spatial synthesis. The articles and compositions emphasize machine learning, real-time interaction, and the blurring of composition and performance. Subfields such as post-human attractors, topographic synthesis, and listening agents dominate his recent output, reflecting a deep engagement with both technical innovation and philosophical inquiry in music. Scientific Awards: Leverhulme Early Career Fellowship (2015–2018) The Merciful Company Cordwainer's Prize for Outstanding Achievement on the MA Programme Mercer's Music Prize for Most Outstanding Achievement on the PhD Programme Audience Prize at Metamorphoses International Electroacoustic Composition Competition, Brussels (2010) Erik has advised no formal students listed in the text, but his research has been supported through competitive grants such as the Leverhulme Fellowship. He has presented his work globally at major conferences including ICMC, SMC, NIME, and Beyond Humanism. His compositions have been released by empreintes DIGITALes and performed at institutions like the University of Oxford and De Montfort University. He is actively involved in creative labs and research teams, notably through his work with BEAST (Birmingham Electroacoustic Sound Theatre) and participation in symposia such as the IKO/OSIL Symposium in Graz, Austria. His current practice involves developing interactive systems for live spatial performance, often involving collaborative electroacoustic composition with intelligent agents.
Cristina Emma Margherita Rottondi is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . She is a member of the Photonext Interdepartmental Center and contributes to research in telecommunications, computer music, and network optimization. Her work spans privacy-preserving protocols, smart grid communication, and low-latency audio streaming. Research Interests : Networked Music Performance, Optical Networks, Smart Grid Privacy, Machine Learning. Education : Not explicitly listed. Research Areas include: Smart Grid Privacy : Developing secure protocols for data aggregation and distributed energy optimization. Optical Network Design : Investigating machine learning-driven solutions and spatial division multiplexing. Networked Music Performance : Addressing latency and inclusivity in remote musical collaboration. Publication Trends highlight interdisciplinary work at the intersection of telecommunications , machine learning , and music technology . Recent articles focus on privacy-preserving smart grids , 5G-enabled musical IoT , and UDP packet trace datasets . Scientific Awards : 2020 Charles Kao Award Best Paper Awards at IEEE Online Greencomm (2014), DRCN (2017), and others N2Women Rising Star (2020) Advising includes PhD candidates working on networked music performance , accessible musical education , and medical wearable devices . She has contributed to national patents for inclusive audio hardware.
