Relevant Experience
Sony Research – Trainee Intern
Tokyo, Japan | Jan–Apr 2025
Building graph-based deep learning representations for music post-production tasks.
Queen Mary University of London – Teaching Assistant
London, UK | Sept 2024–Jan 2025
Supervising 10 UG students for their final year projects in various computer science and AI topics.
Steinberg Media Technologies GmbH – Trainee Intern
Remote | Aug–Dec 2022, Nov 2023–Apr 2024
Deep learning for music post-production tasks, VST development, Dataset curation.
Education
PhD in AI and Music
Queen Mary Univ. of London, UK, in collaboration with Steinberg Media Technologies GmbH (2021–Present, expected Mar 2026)
Modules: Deep Learning for Music, Machine Learning, Sound Recording and Production, Music Informatics
MSc Physics
Pondicherry Univ., India (2018–2020) | Grade: 8.89/10, First Class
Topics: Non-Linear Dynamics, Electronics, Mathematical Physics I & II
BSc Physics (Hons)
Sri Sathya Sai Inst. of Higher Learning, India (2015–2018) | Grade: 8.6/10, Distinction and Gold Medalist
Topics: Set Theory, Linear Algebra, Electronics, Mathematical Physics, C++, Python
Publications and Presentations
Journal (First Author)
- The Role of Communication and Reference Songs in the Mixing Process, Journal of AES, Jan 2023
Book
- Deep Learning for Automatic Mixing, ISMIR 2022
Conferences (First Author)
- Diff-MST: Differentiable Mixing Style Transfer, ISMIR 2024
- Adoption of AI Technology in Music Mixing Workflow, AES Europe 2023
- Diff-MSTC: A Mixing Style Transfer Prototype for Cubase, ISMIR (LBD) 2024
- Demonstrating Diff-MSTC, CHI EA 2025
Workshops
- AI for Multitrack Music Mixing – ISMIR 2022, AES NYC 2023, DaFx 2024