I am a Senior Researcher at NORCE Research AS in Bergen, working on machine learning and its applications to infrastructure monitoring, healthcare technology, and aquaculture. Most of my research concerns AI systems that have to work reliably with real-world sensor data, from fibre optic sensing in water pipelines to computer vision for subsea inspection. Getting these systems to a high level of precision, however, is rarely trivial, and much of my work sits exactly in that gap between a promising model and a dependable one. Recent directions include physics-informed methods for energy systems, large language model tooling, and international research partnerships with institutions in Europe and Southeast Asia.
Research Interests
Machine Learning · Deep Learning · Computer Vision · Distributed Fiber Optic and Seismic Sensing · Physics-Informed Neural Networks · Knowledge Distillation · Large Language Models and Tool-Calling Agents · 3D Point Cloud Processing · Healthcare Technology
Current Work
- AQUAROM: Aquaculture Disease Management. An international collaboration between partners in Norway, the Philippines, and Indonesia, investigating whether smart algorithms with minimal sensors can match expensive monitoring systems for early disease detection in fish farming. The consortium spans seven institutions and includes three PhD positions across Norway and Southeast Asia, building long-term research capacity in regions that depend on aquaculture.
- NOR-DMT: Digital Music Therapy. An AI-supported platform for virtual music therapy, fully tested and ready for deployment on a secure, GDPR-compliant infrastructure. The platform combines real-time video conferencing with machine learning that analyses patient engagement during sessions, supplying therapists with structured clinical reporting tools. Stack: Jitsi Meet (Prosody, Jicofo, JVB) for video, NestJS and PostgreSQL on the backend, a React frontend over Socket.IO, and Docker Compose for deployment.
- Distributed Sensing for Infrastructure and the Subsurface. Machine learning built on distributed acoustic and temperature sensing (DAS/DTS) for leak detection in water pipelines and for multiphase fluid flow estimation in industrial settings. Recent work extends this to passive seismic sensing for groundwater table estimation using fibre optic instrumentation (S-TRANET), moving the same sensing principle from pipelines into the subsurface. Stack: PyTorch and scikit-learn for modelling, ObsPy and SciPy for seismic and acoustic signal processing, dispersion-curve analysis with disba, and high-performance computing (HPC) for training and ensemble runs.
- Computer Vision for Offshore Inspection. Real-time deep learning that detects and classifies objects in subsea imagery, covering pipelines, equipment, and marine life, including a live streaming inspection pipeline developed with industry partners. Stack: RT-DETR for detection, SAM-3 for weakly supervised annotation, PyTorch for training, and FastAPI with Server-Sent Events for live streaming.
- Physics-Informed ML and High-Performance Computing (NAIC). Contributions to the Norwegian Artificial Intelligence Cloud, including climate teleconnection analysis, physics-informed neural networks for energy systems such as green hydrogen electrolyzers, and hybrid optimization algorithms. Datasets and demonstrators from this work are published openly, providing a basis for reuse beyond the original projects. Stack: PyTorch and scikit-learn for ML, knowledge distillation (teacher-student) for the physics-informed networks, Butler-Volmer and Nernst electrochemistry for physics constraints, hybrid optimisation combining Deterministic Crowding GA with CMA-ES, and NetCDF with HPC for climate ensembles.
- LLM Tooling for Data Exploration and Digital Twins. Chat-driven interfaces that connect natural language to data exploration, visualization, and digital twin systems. The models call tools from a constrained catalog and return typed, auditable outputs rather than free-form text; the constraint is what makes the outputs verifiable. Related work examines detecting and reducing hallucinations in large language model outputs. Stack: Models: locally hosted Mistral for text and Qwen Vision for multimodal input; Tool integration: Model Context Protocol (MCP) for tool catalogs and structured render specifications; Data and transport: NetCDF for scientific datasets and Protocol Buffers for service-to-service communication; Front end: typed React components rendered from tool outputs.
Public Notebook
Outside NORCE, I keep a public research notebook on agentic AI systems and coding agents, recording what I test, what breaks, and what I would do differently. The notebook covers not only day-to-day working notes but also longer reference entries, including OpenAI Codex coding agent documentation, Claude Code agentic coding documentation, and AGENTS.md best practices for coding agents.
Education
Ph.D. in Applied Informatics, Norwegian University of Life Sciences (2020) M.Sc. in Informatics, Bandung Institute of Technology (2016) B.Sc. in Computer Science, Sepuluh Nopember Institute of Technology (2011)
Publications
- HA Arief, PJ Thomas, W Li, C Brekken, M Hjelstuen, IE Smith, S Kragset, AK Katsaggelos. Nonlinear interpolated Variational Autoencoder for generalized fluid content estimation. Geoenergy Science and Engineering, 2025
- F Hasan, H Ali, HA Arief. From Mesh to Neural Nets: A Multi-method Evaluation of Physics Informed Neural Network and Galerkin Finite Element Method for Solving Nonlinear Convection–Reaction–Diffusion Equations. International Journal of Applied and Computational Mathematics, 2025
- HA Arief, PJ Thomas, T Wiktorski. Better modeling out-of-distribution regression on distributed acoustic sensor data using anchored hidden state mixup. IEEE Transactions on Industrial Informatics, 2022
- HA Arief, PJ Thomas, K Constable, AK Katsaggelos. Towards Building a Distributed Virtual Flow Meter via Compressed Continual Learning. Sensors, 2022
- HA Arief, T Wiktorski, PJ Thomas. A survey on distributed fibre optic sensor data modelling techniques and machine learning algorithms for multiphase fluid flow estimation. Sensors, 2021
- HA Arief, M Arief, G Zhang, Z Liu, M Bhat, UG Indahl, H Tveite, D Zhao. SAnE: Smart annotation and evaluation tools for point cloud data. IEEE Access, 2020
- HA Arief, UG Indahl, GH Strand, H Tveite. Addressing Overfitting on Point Cloud Classification using Atrous XCRF. ISPRS Journal of Photogrammetry and Remote Sensing, 2019
- HA Arief, GH Strand, H Tveite, UG Indahl. Land cover segmentation of airborne LiDAR data using stochastic atrous network. Remote Sensing, 2018
Links
Personal website of Hasan A. Arief · Google Scholar · ResearchGate · Cristin · GitHub
Last updated: July 2026
