Engineering Creative AI @UAL Creative Computing Institute (UK)
Engineer and Scientific Researcher working with Agents and Creative AI. Specialist in AI applications for signal processing with prior experience in audio and video computing, adaptive models and inference-based automation. Proven track record of driving innovative interdisciplinary research and high-resource deep learning deployment.
Previous participation in EvoStar (Italy), ICCC (Portugal), ACC (Sweden), VAF (Denmark), ICLI and WAC (Norway). In production has collaborated with entities Aardman Animations, Channel 4, Braga Media Arts UNESCO, Criatech, Openfield Creativelab, Charismatic Studio, and founding member of Supernova Ensemble.
Current practice: Agents and MLLMs, AI Platform Design, MAS and MCP Systems, Integration Networks, Real-Time Generative AI and Cognitive Simulators.
Research Projects
Collections Transformer: PDRF extending "Transforming Collections: Reimagining Art, Nation and Heritage", 1 of 5 'Discovery Projects' part of the 5-year Towards A National Collection (TaNC) programme. Funded by UKRI's Arts and Humanities Research Council (AHRC). In partnership with Tate and Decolonising Arts Institute (AH/W003341/1).
Charismatic Project: PDRF "AI-driven Storytelling for TV and Film Industries" led by Charismatic.AI, Channel 4, UAL Creative Computing Institute, Falmouth University, Aardman Animations, Sound Reactions and digital ethicist Lisa Talia Moretti, supported by InnovateUK (UKRI).
Xperimus Project: AR “Experimentation in music in Portuguese culture: History, contexts and practices in the 20th and 21st centuries” (POCI-01-0145-FEDER031380) co-funded by the European Union through the Operational Program Competitiveness and Internationalisation, in its ERDF component, and by national funds, through the Portuguese Foundation for Science and Technology (FCT/PT).
Selected Publications
Sutskova, O., & Arandas, L. (2026). Developing a GDPR Compliant Locally Deployable Experimentation Toolkit for Human to AI-Agent Interaction Testing. Cyberpsychology Section Annual Conference, The British Psychological Association (BPS), York St John University.
Arandas, L., Ionescu, I., Khan, M., Grierson, M., Carvalhais, M. (2025). All YIN No YANG: Geometric Abstraction of Oil Paintings with Trained Models, Noise and Self-reference. In: Machado, P., Johnson, C., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2025. Lecture Notes in Computer Science, vol 15611. Springer, Cham. https://doi.org/10.1007/978-3-031-90167-6_17.
Arandas, L., Sarmento, P., Grierson, M., Carvalhais, M.(2024) Editing Video Streams Through Natural Language Embeddings:
Keyframe Sequencing Using Transformers. In Adriana Sá (ed) Live
Interfaces journal vol. 2 (Univ. Lusófona/ CICANT). ISSN: 2975-9943.
Arandas, L., Grierson, M. & Carvalhais, M. (2023). Computing Short Films Using Language-guided Diffusion and Vocoding Through Virtual Timelines of Summaries. INSAM, Journal of Contemporary Music, Art and Technology, ISSN: 2637-1898.
Panal, G., & Arandas, L. (2021). Beat Byte Bot: A Chatbot Architecture for Web-based Audio Management. In Proceedings of the 11th Workshop on Ubiquitous Music (UbiMus 2021) (pp. 72-82). g-ubimus. ISBN: 978-65-00-30880-8.
Research Statement
My scientific research focuses on robust, interpretable, and multimodal Creative AI, particularly under real-world constraints such as induced hallucination, time-critical operations, and privacy requirements.
I have developed a sustained research agenda at the intersection of:
- AI-assisted signal processing
- Multimodal agents and real-time inference
- Computing networks and system integration
With applications in audio, video and critical environments.
My current goal is to develop reliable multimodal agents that can reason from data in a robust, interpretable, and verifiable manner.
My work spans:
- Computer engineering advances
- System-level integration
- Deployment-oriented research
With the aim of ensuring reliability and transparency beyond controlled benchmark conditions.