PhD Students

AIM CDT Cohorts
First Cohort - 2019-2024
Second Cohort - 2020-2025
Third Cohort - 2021-2026
Fourth Cohort - 2022-2027
Fifth Cohort - 2023-2028
Sixth+ Cohort - 2024-2029 and 2025-2030

 

First AIM cohort students (2019-2024):

PhD Student	Project title
Adan Benito	Beyond the fret: gesture analysis on fretted instruments and its applications to instrument augmentation
Berker Banar	Towards Composing Contemporary Classical Music using Generative Deep Learning
Marco Comunità	Machine learning applied to sound synthesis models
David Foster	Modelling the Creative Process of Jazz Improvisation
Lele Liu	Automatic music transcription with end-to-end deep neural networks
Ilaria Manco	Deep learning and multi-modal models for the music industry in collaboration with Universal Music Group
Andrea Martelloni	Real-Time Gesture Classification on an Augmented Acoustic Guitar using Deep Learning to Improve Extended-Range and Percussive Solo Playing
Mary Pilataki-Manika	Polyphonic Music Transcription using Deep Learning in collaboration with Apple
Saurjya Sarkar	New perspectives in instrument-based audio source separation
Pedro Sarmento	Guitar-Oriented Neural Music Generation in Symbolic Format in collaboration with Holonic Systems Oy.
Elona Shatri	Optical music recognition using deep learning in collaboration with Steinberg Media Technologies GmbH
Cyrus Vahidi	Perceptual end to end learning for music understanding in collaboration with MUSIC Tribe Brands UK Limited

Second AIM cohort students (2020-2025):

PhD Student	Project title
Corey Ford	Artificial Intelligence for Supporting Musical Creativity and Engagement in Child-Computer Interaction
Max Graf	AI-Based Musical Co-Creation in Extended Realities: PERFORM-AI
Madeline Hamilton	Perception and analysis of 20th and 21st century popular music
Benjamin Hayes	Perceptually motivated deep learning approaches to creative sound synthesis
Jiawen Huang	Lyrics Alignment For Polyphonic Music
Harnick Khera	Informed source separation for multi-mic production in collaboration with BBC
Yin-Jyun Luo	Industry-scale Machine Listening for Music and Audio Data in collaboration with Spotify
Luca Marinelli	Gender-coded sound: A multimodal data-driven analysis of gender encoding strategies in sound and music for advertising
Xavier Riley	Digging Deeper - expanding the “Dig That Lick” corpus with new sources and techniques
Eleanor Row	Automatic micro-composition for professional/novice composers using generative models as creativity support tools
Shubhr Singh	Audio Applications of Novel Mathematical Methods in Deep Learning
Christian Steinmetz	Deep learning for high-fidelity audio and music production
Jingjing Tang	End-to-End System Design for Music Style Transfer with Neural Networks
Lewis Wolstanholme	Real-time instrument transformation and augmentation with deep learning
Yixiao Zhang	Machine Learning Methods for Artificial Musicality in collaboration with Apple

Third AIM cohort students (2021-2026):

PhD Student	Project title
Katarzyna Adamska	Predicting hit songs: multimodal and data-driven approach
Sara Cardinale	Character-based adaptive generative music for film and video games using Deep Learning and Hidden Markov Models
Franco Caspe	AI-assisted FM synthesis for sound design and control mapping
Ruby Crocker	Time-based mood recognition in film music
Carlos De La Vega Martin	Neural Drum Synthesis
Bleiz MacSen Del Sette	The Sound of Care: researching the use of deep learning and sonification for the daily support of people with Chronic Pain
Rodrigo Mauricio Diaz Fernandez	Hybrid Neural Methods for Sound Synthesis
Andrew Edwards	Computational Models for Jazz Piano: Transcription, Analysis, and Generative Modeling
Oluremi Samuel Oladotun Falawo	Embodiment in Intelligent Musical Systems
Mariam Fayaz Torshizi	Music mood modelling using Knowledge graphs and Graph Neural Nets
Yazhou Li	Virtual Placement of Objects in Acoustic Scenes
Jackson Loth	Time to vibe together: cloud-based guitar and intelligent agent in collaboration with Hyvibe
Teresa Pelinski Ramos	Sensor mesh as performance interface in collaboration with Bela
Soumya Sai Vanka	Smart Channel strip using Neural audio processing in collaboration with Steinberg
Chris Winnard	Music Interestingness in the Brain
Xiaowan Yi	Composition-aware music recommendation system for music production in collaboration with Focusrite
Huan Zhang	Computational Modeling of Expressive Piano Performance

Fourth AIM cohort students (2022-2027):

PhD Student	Project title
James Bolt	Intelligent audio and music editing with deep learning
Carey Bunks	Cover Song Identification in collaboration with Apple
Adam Garrow	A computational model of music cognition using statistical learning of structures
Ashley Noel-Hirst	Latent Spaces for Human-AI music generation
Alexander Williams	User-driven deep music generation in digital audio workstations in collaboration with Sony
Yinghao Ma	Self-supervision in machine listening in collaboration with Bytedance
Jordan Shier	Real-time timbral mapping for synthesized percussive performance in collaboration with Ableton
David Marttila (Südholt)	Machine learning of physical models for voice synthesis in collaboration with Nemisindo
Tyler McIntosh	Expressive Performance Rendering for Music Generation Systems in collaboration with DAACI
Christopher Mitcheltree	Deep Learning for Time-varying Audio and Parameter Modulations
Louise Thorpe	Using Signal-informed Source Separation (SISS) principles to improve instrument separation from legacy recordings
Ioannis Vasilakis	Active learning for interactive music transcription
Chin-Yun Yu	Neural audio synthesis with expressiveness control
Ningzhi Wang	Generative models for music audio representation and understanding in collaboration with Spotify
Aditya Bhattacharjee	Self-supervision in Audio Fingerprinting

Fifth AIM cohort students (2023-2028):

PhD Student	Project title
Bradley Aldous	Advancing music generation via accelerated deep learning
Keshav Bhandari	Neuro-Symbolic Automated Music Composition
Louis Bradshaw	Neuro-symbolic music models
Julien Guinot	Beyond Supervised Learning for Musical Audio (in collaboration with Universal Music Group)
Zixun (Nicolas) Guo	Towards Tonality-Aware Music Understanding: Modeling Complex Tonal Harmony
Adam He	Neuro-evolved Heuristics for Meta-composition (in collaboration with DAACI)
Gregor Meehan	Representation learning for musical audio using graph neural network-based recommender engines
Marco Pasini	Fast and Controllable Music Generation (in collaboration with Sony SCL)
Christos Plachouras	Deep learning for low-resource music
Pablo Tablas De Paula	Machine Learning of Physical Models
Haokun Tian	Timbre Tools for the Digital Instrument Maker
Qing Wang	Multi-modal Learning for Music Understanding
Yifan Xie	Film score composer AI assistant: generating expressive mockups (in collaboration with Spitfire Audio)
Ece Yurdakul	Emotion-based Personalised Music Recommendation (in collaboration with Deezer)
Farida Yusuf	Neural computing for auditory object analysis
Qiaoxi Zhang	Multimodal AI for musical collaboration in immersive environments (in collaboration with PatchXR)
Shuoyang Zheng	Explainability of AI Music Generation
Xavi D'Cruz	Multimodal techniques for the control of procedural audio

Sixth+ AIM cohort students (2024-2029 and 2025-2030):

PhD Student	Project title
Jinwen Zhou	Combining Deep Learning and Music Theory
Minhui Lu	Differentiable Physical Modelling for Singing Voice Synthesis
Shangxuan Luo	Adaptive Music Generation for Video Game
Yorgos Velissaridis	Smart EQ: Personalizing Audio with Context-aware AI using Listener Preferences and Psychological Factors
Shanti Stewart	AI-Powered Audio Loop Generation for Assistive Music Production in collaboration with Steinberg