AI-Music researcher & ML Engineer
Who Am I?
Researcher in artificial intelligence with expertise in human-AI co-creation, multi-agent reinforcement learning, and computational creativity. Extensive interdisciplinary research experience bridging AI, music technology, and environmental systems. Demonstrated success in publishing in peer-reviewed journals and conferences. Proficient in designing systematic evaluation methodologies and user-centric AI frameworks. Experienced in leading research projects, mentoring students, and fostering collaboration with academic and industry partners. Committed to bridging theoretical AI foundations with practical, user-centric, and interdisciplinary applications that generate meaningful impact.
Skills
Development & Tools
AI Research & Development
Audio & Music Technology
Research & Evaluation
Education
PhD in Artificial Intelligence
UiT - The Arctic University of NorwayThesis: "From Algorithm to Artistry: Exploring the Epistemological Gap in Music Generation Systems"
MSc in Computer Science
UiT - The Arctic University of NorwayThesis: "Neural networks for Music Information Retrieval and algorithmic jazz composition"
BSc in Software Engineering
Azad University of Tehran NorthExperience
Visiting Researcher
Trondheim, NODepartment 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
Narvik, NODepartment 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
Narvik, NOUiT - 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
Languages
- Persian Native
- English Fluent
- Norwegian Intermediate
Hobbies
Reading, running, climbing, mountain biking, hiking, fixing stuff, tinkering, playing instruments, wood working, philosophy, psychology, coffee, cats, dogs, life
Research Interests
Selected Projects
Technical Due Deligence: AI Furniture Recognition
Trondheim, NODigital Xalience AS
- Validated AI furniture recognition feasibility, assessing foundation models and zero-shot segmentation to inform strategic R&D roadmap
- Evaluated computer vision frameworks including YOLO and Roboflow for enterprise scalability across company sizes
- Assessed dataset availability and quality from Open Images, ShapeNet, Stanford 3D Scanning Repository for training data pipeline
Neural Audio Synthesis for Interactive Performance
Trondheim, NONTNU 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
Tromsø, NOInterdisciplinary Art-AI Project with Artist, 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
Narvik, NOInterreg-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
Open Source
Unsupervised anomaly detection for variable-length audio loops using HTS-AT and Deep SVDD.
MIDI segmentation and loop extraction pipeline for symbolic music.
Templates and infrastructure for process-oriented evaluation of music generation systems.
Python implementation of Growing Hierarchical Self-Organizing Maps for unsupervised clustering.
Utilities for GHSOM visualization, analysis, and automatic curriculum extraction.
RL-based combinatorial music generation with hierarchical curriculum and inference-time adaptation.
Publications
Dadman, S. & Bremdal, B. (2026). ARIA: Autonomous Reinforcement-Learning with Intelligent Abstraction for User-Centric Symbolic Music Generation. ResearchGate. doi: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. doi: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. doi:10.3390/electronics13061116
Dadman, S. (2023). Boosting Creativity with AI: Exploring Advanced Models, Multi-Agent Systems, and Design Grammar. . doi: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. doi: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. doi:10.1049/icp.2023.1230
Bremdal, B. & Dadman, S. (2023). Predicting Peak Prices in the Current Day-Ahead Market. IET Conference Proceedings. doi: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. doi:10.3390/wevj14030061
Dadman, S., Bremdal, B., Bang, B. & Dalmo, R. (2022). Toward Interactive Music Generation: A Position Paper. IEEE Access. doi: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.
Presentations & Media
poster Learning Normal Patterns in Musical Loops — Northern Lights Deep Learning Conference 2026 (Tromsø, NO) [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) [slides]
podcast A user-centric approach for symbolic music generation — CreateMe podcast, University of Agder (Agder, NO) [audio]
poster Integration and influence of artificial intelligence in the design industry — NORA Annual Conference 2023 (Oslo, NO) [paper]
lecture Interactive music generation with Artificial Intelligence — University of Oslo, Brain Talk webinar (Oslo, NO) [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) [slides]
exhibition Art x AI — UiT, The Arctic University of Norway, Narvik (Narvik, NO) [video]
exhibition If I Were Standing in your Shoes — Tromsø kunstforening (Tromsø, NO) [article]
exhibition This is a Protest Gesture to Showcase Norway's Violation of Basic Human Rights — Storgata (Tromsø, NO) [article]
interview Composing Jazz with Deep Learning — NRK P3 and Nyheter (Norway) [audio]
media Har utviklet kunstig intelligens som lager jazzmusikk — Forskning.no (Oslo, NO) [article]
media Utvikler kunstig intelligens som komponerer jazzmusikk — UiT Highlights (Oslo, NO) [article]
media Narvik-studenten utvikler kunstig intelligens som komponerer musikk — Fremover.no (Narvik, NO) [article]
media Musikk i koder — Jurnalen.oslomet.no (Oslo, NO) [article]
Teaching & Supervision
Courses Taught
MSc DTE-3608-1 24V — Artificial Intelligence and Intelligent Agents - Concepts and Algorithms
BSc DTE-2501-1 21H — AI Methods and Applications
BSc DTE-2502-1 21H — Neural Networks
BSc DTE-2602-1 21H — Introduction to Machine Learning and AI
Supervised Projects
MSc Investigating representation of tablature data for NLP music prediction — Tor Eldby
MSc Automatic Generation of Custom Image Recognition Models — Magnuss Fredheim Hanssen
MSc Application of Change Point Detection Algorithms in Adaptable Symbolic Music Segmentation Task Using MIDI Representation — Sakib Mukter
MSc Development of a Music Education Framework Using Large Language Models (LLMs) — Mudassar Amin
MSc Application of LLMs and Embeddings in Music Recommendation Systems — Abu Mohammad Taeif
MSc Fine-tuning Large Language Models on historical causes of death data — Kristoffer Berg Wilhelmsen
MSc AI in the Sky: Diverse Approaches to Drone Swarm Command, Control, Connection and Communication — Modhubroty Dey Barnile
BSc Spotify 'music taste' matching — Group 7
BSc UiT rollespill — Group 10
BSc Predikering av driftsforstyrrelser på vifter — Group 16
BSc Felles observasjonskort for Varsom — Group 9
BSc Developing a machine learning app for seaspray icing — Group 14
BSc Sintef Nord: Using Novel ML to determine species of fish within a school and their biomass — Group 9
BSc Sintef: Keeping the operator in the loop with autonomous robots for inspection and maintenance — Group 16
BSc UiT - IBEM: Development of AI applications in radiology for medical imaging — Group 19
BSc Machine Learning-Based Stock Correlation Analysis and Pattern Recognition System for Oslo Børs — Group 15