About
AI Leader, Researcher & Engineer
I build AI systems that are reliable, interpretable, and deployable in the real world , across clinical medicine, bioinformatics, geospatial domains, and strategic technology.
Background
I'm Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, a Finland-based AI researcher and engineer currently completing dual PhD programmes at the Technical University of Denmark in Human-XAI Collaboration for Fetal Ultrasound, and at the University of Luxembourg in Systems and Molecular Biomedicine.
My career spans academia, industry, and government, from Google Health and the Finnish Center for Artificial Intelligence to the European Research Council and the Swedish Security Service. I co-founded Skolyn, a clinical AI platform for radiology and pathology, where I served as CTO and shaped the architecture, go-to-market strategy, and clinical validation pipeline.
Across these settings, I've consistently worked at the intersection of rigorous machine learning and real-world deployment constraints, designing systems that clinicians can trust, regulators can audit, and engineers can maintain. My research touches multimodal AI, federated learning, explainability, bioinformatics, and geospatial intelligence, with over 11 publications spanning these domains.
Education
Technical University of Denmark
Human-XAI Collaboration for Improved Fetal Ultrasound Imaging
Feb 2025 - Mar 2028
University of Luxembourg
Systems and Molecular Biomedicine
Feb 2025 - Jan 2028
Linköping University
Statistics and Machine Learning
Aug 2024 - Feb 2026
Tampere University
Computing and Electrical Engineering
Aug 2021 - Jun 2024
International School of Helsinki
International Baccalaureate
Jul 2019 - Jun 2021
Expertise
Research & Practice Domains
Medical & Clinical AI
Design and evaluation of AI systems for radiology, pathology, and clinical decision support. Human-AI collaboration studies, federated learning for privacy-preserving clinical research.
Explainable AI
Building AI systems that clinicians and domain experts can understand, trust, and meaningfully override. XAI methods, uncertainty quantification, and human-in-the-loop workflows.
Foundation Models & LLMs
Evaluation methodology for generative and multimodal models in high-stakes settings. Clinical NLP, model cards, transparency frameworks, and continual learning.
Bioinformatics & Omics
Large-scale proteomics pipelines, WGS/WES workflows, multi-omics integration, and ML-driven biomarker discovery for neurodegenerative disease.
Geospatial & Urban AI
GeoAI frameworks for urban mobility, climate resilience, housing, and public-sector analytics. Spatio-temporal modeling and evidence-based policy.
AI Strategy & Leadership
Technical strategy, R&D leadership, startup co-founding, and enterprise SaaS. Cross-functional execution at Google Health, ERC, and Skolyn.
Approach
How I Work
Rigorous by default
Every claim is evaluated, every model is tested, every assumption is made explicit.
Team-first execution
Effective across research and engineering teams. Comfortable leading and being led.
End-to-end thinking
From raw data through deployment. I care about systems that actually work in production.
Domain-aware
Deep context in medicine, bioinformatics, defence, geospatial, and policy environments.
Interested in collaborating?
Open to research partnerships, advisory roles, and technical consulting.