Curriculum
Vitae
RESEARCH EXPERIENCE
Visiting Researcher
Department of Music Technology, Norwegian University of Science and Technology (NTNU)
- Conducted research on human-computer interaction in AI music systems, enhancing artistic control in neural audio synthesis
- Collaborated with Prof. Andreas Bergsland to explore user interaction modalities and collaborative music-making environments
- Evaluated AI-driven music generation systems through structured experiments, integrating artist perspectives for improved performance
- Demonstrated application of neural audio synthesis and interactive performance systems
Doctoral Research Fellow
Department of Computer Science and Computational Engineering, UiT - The Arctic University of Norway
- Conducted interdisciplinary research in multi-agent systems and reinforcement learning for music generation
- Developed user-centric AI frameworks for creative applications
- Developed and implemented a representational learning model for unsupervised pattern detection for music analysis
- Established and developed workflow-based evaluation methodology for music generation systems
- Authored and presented peer-reviewed research in conferences and journals
- Led and managed interdisciplinary projects between AI research and artistic practice, ensuring real-world applicability
- Taught 4 graduate and undergraduate courses and supervised over 15 BSc and MSc thesis projects
Research Assistant
UiT - The Arctic University of Norway
- Contributed to the Smart Charge project by developing forecasting models for E-mobility, solar energy consumption and market production
- Developed multi-agent systems using zero-intelligence and reinforcement learning to optimize energy market simulations
- Delivered and organized events, lectures, and workshops on machine learning and deep learning techniques
SELECTED PROJECTS
Neural Audio Synthesis for Interactive Performance
NTNU Music Technology Department
- Investigated latent space representations in neural audio models for enhanced artistic control
- Developing user interaction modalities for enhanced expressive capabilities in neural audio synthesis systems for live electronics
- Advanced understanding of neural audio synthesis applications in live performance contexts
Latent Expressions
Interdisciplinary Art-AI Project with Pei-han Lin
- Developed generative models with StyleGAN by manipulating latent space vectors to control semantic features
- Created a framework for encoding features to explore generative dimensions in synthetic image creation
- Manipulated latent vectors along chosen directional paths with specific boundary conditions for effective generation control
- Integrated AI-generated content into multi-disciplinary installations spanning painting, sculpture, and sound art
- Featured in multiple exhibitions and various art galleries
Smart Charge
Interreg-funded EU Collaboration | UiT & Lapland University of Applied Sciences
- Implemented CNN and LSTM neural networks for single-step and multi-step energy load forecasting
- Created multi-agent systems, utilizing zero-intelligence and reinforcement learning for energy markets and V2G (Vehicle-to-Grid) energy optimization
- Incorporated Arctic-specific variables including temperature fluctuations, occupancy patterns, and tourism demand
- Presented findings at CIRED 2023 conference in Rome
PUBLICATIONS
- Dadman, S. & Bremdal, B. (2026). ARIA: Autonomous Reinforcement-Learning with Intelligent Abstraction for User-Centric Symbolic Music Generation. ResearchGate. https://doi.org/10.13140/RG.2.2.22913.31843
- Dadman, S., Bremdal, B., Bang, B. & Dalmo, R. (2025). Learning Normal Patterns in Musical Loops. Northern Lights Deep Learning Conference. https://openreview.net/forum?id=Pr22XVnMW1
- Dadman, S., Bremdal, B. & Bergsland, A. (2025). Workflow-Based Evaluation of Music Generation Systems: Open-Source Case Study. arXiv. https://doi.org/https://doi.org/10.48550/arXiv.2507.01022
- Dadman, S. & Bremdal, B. (2024). Crafting Creative Melodies: A User-Centric Approach for Symbolic Music Generation. Electronics. https://doi.org/10.3390/electronics13061116
- Dadman, S. (2023). Boosting Creativity with AI: Exploring Advanced Models, Multi-Agent Systems, and Design Grammar. . https://doi.org/10.13140/RG.2.2.24877.67041
- Dadman, S. & Bremdal, B. (2023). Multi-Agent Reinforcement Learning for Structured Symbolic Music Generation. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. https://doi.org/10.1007/978-3-031-37616-0_5
- Naeimaei, A. & Dadman, S. (2023). Mindful Integration of AI in the Design Industry: Opportunities and Implications. NORA Annual Conference.
- Dadman, S. & Bremdal, B. (2023). Using Light Weight Electric Vehicles for V2G Services in the Arctic. IET Conference Proceedings. https://doi.org/10.1049/icp.2023.1230
- Bremdal, B. & Dadman, S. (2023). Predicting Peak Prices in the Current Day-Ahead Market. IET Conference Proceedings. https://doi.org/10.1049/icp.2023.1244
- Bremdal, B., Ilieva, I., Tangrand, K. & Dadman, S. (2023). E-Mobility and Batteries—A Business Case for Flexibility in the Arctic Region. World Electric Vehicle Journal. https://doi.org/10.3390/wevj14030061
- Dadman, S., Bremdal, B., Bang, B. & Dalmo, R. (2022). Toward Interactive Music Generation: A Position Paper. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3225689
- Dadman, S., Bremdal, B. & Tangrand, K. (2021). The Role of Electric Snowmobiles and Rooftop Energy Production in the Arctic: The Case of Longyearbyen. J. Clean Energy Technol.
OPEN SOURCE
- music-anomalizer — Unsupervised anomaly detection for variable-length audio loops using HTS-AT and Deep SVDD.
- midi-loop-extractor — MIDI segmentation and loop extraction pipeline for symbolic music.
- scope — Templates and infrastructure for process-oriented evaluation of music generation systems.
- ghsom-py — Python implementation of Growing Hierarchical Self-Organizing Maps for unsupervised clustering.
- ghsom-toolkits — Utilities for GHSOM visualization, analysis, and automatic curriculum extraction.
- aria — RL-based combinatorial music generation with hierarchical curriculum and inference-time adaptation.
PRESENTATIONS & MEDIA
- poster Learning Normal Patterns in Musical Loops — Northern Lights Deep Learning Conference 2026 (Tromsø, NO) [2026.JAN] [paper]
- lecture Control and Explore: Neural Audio Synthesis with VAEs for Live-Electronics and Interactive Performances — Faglig Forum, Music Technology Department, NTNU, Norway (Trondheim, NO) [2025.FEB] [slides]
- lecture Artificial Intelligence and Music: Deep Learning and Agents for Music Generation — SINTEF-ZEB Lab, Trondheim, Norway (Trondheim, NO) [2025.JAN] [slides] [video]
- podcast A user-centric approach for symbolic music generation — CreateMe podcast, University of Agder (Agder, NO) [2024.OCT] [audio]
- lecture Melody and Machine: Exploring AI's Role in Music Creation — Algoritmi, UiT The Arctic University of Norway (Tromso, NO) [2024.MAR] [slides] [video]
- poster Integration and influence of artificial intelligence in the design industry — NORA Annual Conference 2023 (Oslo, NO) [2023.NOV] [paper]
- demo Application of multi-agent systems and reinforcement learning methods in interactive music generation — AI+ Conference (Oslo, NO) [2023.SEP] [video] [code]
- lecture Interactive music generation with Artificial Intelligence — University of Oslo, Brain Talk webinar (Oslo, NO) [2023.MAY] [video]
- lecture Application of multi-agent systems and reinforcement learning methods in computational creativity and music generation — UiT, The Arctic University of Norway, Bodo (Bodo, NO) [2022.OCT] [slides]
- exhibition Art x AI — UiT, The Arctic University of Norway, Narvik (Narvik, NO) [2022.JUN] [video]
- exhibition If I Were Standing in your Shoes — Tromsø kunstforening (Tromsø, NO) [2022] [article]
- exhibition This is a Protest Gesture to Showcase Norway's Violation of Basic Human Rights — Storgata (Tromsø, NO) [2022] [article]
- presentation Application of deep learning methods in music generation — UiT, The Arctic University of Norway, Narvik PhD. Conference (Narvik, NO) [2021.NOV] [slides] [paper]
- demo Application of deep learning methods in symbolic music generation — AI+ Conference (Oslo, NO) [2021.SEP] [video] [code]
- demo Deep Jazz Composer — UiT, The Arctic University of Norway, Narvik Research Week (Narvik, NO) [2020.OCT] [video] [code]
- interview Composing Jazz with Deep Learning — NRK P3 and Nyheter (Norway) [2020.SEP] [audio]
- media Har utviklet kunstig intelligens som lager jazzmusikk — Forskning.no (Oslo, NO) [2020.AUG] [article]
- media Utvikler kunstig intelligens som komponerer jazzmusikk — UiT Highlights (Oslo, NO) [2020.JUL] [article]
- media Narvik-studenten utvikler kunstig intelligens som komponerer musikk — Fremover.no (Narvik, NO) [2020.JUN] [article]
- media Musikk i koder — Jurnalen.oslomet.no (Oslo, NO) [2020.MAY] [article]
TEACHING & SUPERVISION
Courses Taught
- MSc [2024] DTE-3608-1 24V — Artificial Intelligence and Intelligent Agents - Concepts and Algorithms
- BSc [2021-2023] DTE-2501-1 21H — AI Methods and Applications
- BSc [2021-2023] DTE-2502-1 21H — Neural Networks
- BSc [2021] DTE-2602-1 21H — Introduction to Machine Learning and AI
Supervised Projects
- MSc [2021] Investigating representation of tablature data for NLP music prediction — Tor Eldby
- MSc [2022] Automatic Generation of Custom Image Recognition Models — Magnuss Fredheim Hanssen
- MSc [2024] Application of Change Point Detection Algorithms in Adaptable Symbolic Music Segmentation Task Using MIDI Representation — Sakib Mukter
- MSc [2024] Development of a Music Education Framework Using Large Language Models (LLMs) — Mudassar Amin
- MSc [2024] Application of LLMs and Embeddings in Music Recommendation Systems — Abu Mohammad Taeif
- MSc [2024] Fine-tuning Large Language Models on historical causes of death data — Kristoffer Berg Wilhelmsen
- MSc [2024] AI in the Sky: Diverse Approaches to Drone Swarm Command, Control, Connection and Communication — Modhubroty Dey Barnile
- BSc [2022] Spotify 'music taste' matching — Group 7
- BSc [2022] UiT rollespill — Group 10
- BSc [2022] Predikering av driftsforstyrrelser på vifter — Group 16
- BSc [2023] Felles observasjonskort for Varsom — Group 9
- BSc [2023] Developing a machine learning app for seaspray icing — Group 14
- BSc [2024] Sintef Nord: Using Novel ML to determine species of fish within a school and their biomass — Group 9
- BSc [2024] Sintef: Keeping the operator in the loop with autonomous robots for inspection and maintenance — Group 16
- BSc [2024] UiT - IBEM: Development of AI applications in radiology for medical imaging — Group 19
- BSc [2025] Machine Learning-Based Stock Correlation Analysis and Pattern Recognition System for Oslo Børs — Group 15