Prashanth Thattai
R&D Scientist in AI, Software engineering, and Human-Computer Interaction specialized in creating bespoke end-user applications for music generation involving human in the interaction loop.
My software skills include Full-stack development, package development in Python & C++. I am competent in Mixed methods user research, specifically, using ANOVA, and thematic analysis. My teaching experiences include Creative Coding, Games, Interaction design & Computational Art.
I have a strong academic track record of publishing in Computer Science and Music venues such as ICCC, NIME, and Creativity and Cognition, and actively write independent grants scaling up research to industry prototypes.
Grants
Marie Curie Seal of Excellence (2023) for Individual Fellowship in Personalizing Generative Deep Learning systems
MSCA Seal of Excellence
Global Talent Visa (2022-2025) in Computing and Communications, Royal Academy of Engineering, UK
Royal Academic of Engineering
Secured $30k funding from the National Arts Council, Singapore (21-22) to lead music technology commercialization
Arts X Tech Grant
Secured ~$150k scholarship funding for MA and PhD at National University of Singapore (2012-2020)
NUS Research Scholarship & Tuition Fee waiver
Experiences
Professional Experience
POSTDOC
Feb - Nov 2022
DE MONTFORT UNIVERSITY, UK
AI-generated content & movement personalized based on human bio-signals (brain waves, heartbeat, muscles, skeleton..)
GRANT LEAD
2021 - 2022
ARTS X TECH LAB, NATIONAL ARTS COUNCIL, SG
AI composing music to human-generated gestures in harmony with live sounds and music
RESEARCH ENGINEER
2020 - 2022
NATIONAL UNIVERSITY OF SINGAPORE, SG
Simulating audio big datasets to train, develop, and test generation, classification & augmentation of audio textures
Education
PHD
2015 - 2020
NATIONAL UNIVERSITY OF SINGAPORE, SG
AI system co-creating musical duet performances using rhythms captured from live human data
MA
2012 - 2015
NATIONAL UNIVERSITY OF SINGAPORE, SG
AI system generating new rhythmic harmonies using an original audio recording and a user's harmony profile
B.ENGG, COMPUTER SCIENCE & ENGINEERING
2008 - 2012
NATIONAL Institute of Technology, Trichy, India
Algorithm to analyze and recognize musical scale from frequencies in an audio track
Papers
Wyse, L., and Thattai, P. "Syntex: parametric audio texture datasets for conditional training of instrumental interfaces." in New Interfaces for Musical Expression, 2022.
Koch, J., Ravikumar, P., & Calegario, F. "Agency in Co-Creativity: Towards a Structured Analysis of a Concept." In ICCC 2021-12th International Conference on Computational Creativity (Vol. 1, pp. 449-452). Association for Computational Creativity.
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Ravikumar, P.T., and Patel, D. "Non-anthropomorphic evaluation of musical cocreative systems", In 11th International Conference on Computational Creativity, 2020
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Kantosalo, A., Ravikumar, P. T., Grace, K., & Takala, T. (2020, September). Modalities, Styles and Strategies: An Interaction Framework for Human-Computer Co-Creativity. In ICCC (pp. 57-64).
Ravikumar, P. T., and Wyse, L. "MASSE: A system for music action selection through state evaluation", In 10th International Conference on Computational Creativity, ICCC.
Ravikumar, P. T., McGee, K., and Wyse, L. "Back to the experiences empirically grounding the development of musical co-creative partners in co-experiences", In 6th International Workshop on Musical Metacreation. 9th International Conference on Computational Creativity, ICCC (pp. 1-7).
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Ravikumar, P. T. "Notational Communication with Co-creative Systems: Studying Improvements to Musical Coordination." In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (pp. 518-523).
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Mitchell, A., Yew, J., Thattai, P., Loh, B., Ang, D., and Wyse, L. "The temporal window: explicit representation of future actions in improvisational performances." In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (pp. 28-38).
Results & Demos
- All
- Systems
- Results
