Chief Technology Officer & Co-Founder | Postdoctoral Research Fellow | ERC Seconded National Expert
Specializing in Medical AI, Clinical Genomics, Explainable AI, Federated Learning, and Multi-Omics Integration
Location: Greater Linköping Metropolitan Area, Sweden
Address: Fårsaxvägen 31, 586 66 Linköping
Phone: +46 76 236 80 88 | Email: [email protected]
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As a Postdoctoral Research Fellow, Chief Technology Officer, and Research Scientist, I advance interdisciplinary research at the convergence of artificial intelligence, computational biology, and translational medicine. My research program focuses on developing explainable, ethically-grounded AI systems that demonstrably enhance clinical decision-making and patient outcomes while ensuring algorithmic transparency and regulatory compliance. Current Academic & Professional Appointments:
Currently pursuing dual doctoral degrees in Systems & Molecular Biomedicine (University of Luxembourg) and Human-XAI Collaboration (Technical University of Denmark), complementing my MSc in Statistics & Machine Learning (Linköping University) and BSc in Computing & Electrical Engineering (Tampere University). This interdisciplinary foundation enables bridging technical innovation with clinical translation and evidence-based policy development. |
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January 2025 - Present | Baku Economic Zone, Azerbaijan
As CTO and Co-Founder of Skolyn, I lead the technology strategy and R&D for our mission: eliminating diagnostic error through explainable clinical AI. Our platform serves as a "Clinical Co-Pilot," combining multimodal medical imaging with transparent reasoning.
Key Achievements:
- Architected Skolyn AI Platform processing 127+ pathological indicators in <3 seconds
- Achieved >95% diagnostic accuracy with full XAI interpretability
- Scaled to 50,000+ scans across Nordic and DACH hospital pilots
- Delivered <8% churn, 85% gross margins, and strong expansion revenue
- Implemented GDPR/HIPAA-compliant federated learning pipelines
- Led toward CE Mark, ISO 13485, and FDA 510(k) readiness
- Positioned for $2M Seed round and forthcoming Series A
Technical Leadership:
- Directed team of ML engineers, backend developers, and clinical data scientists
- Integrated HL7/FHIR data pipelines with enterprise-grade APIs
- Deployed distributed inference systems (X-ray, CT, MRI modalities)
- Built privacy-preserving training infrastructure
November 2025 - Present | Brussels Metropolitan Area
As a Seconded National Expert at the European Research Council Executive Agency (ERCEA), I focus on evidence-based policy development and strategic evaluation of Horizon Europe's €16B research portfolio.
Key Responsibilities:
- Analyze 12,000+ ERC projects, 75,000+ researchers, 200,000+ publications
- Develop ML-based evaluation workflows for research impact assessment
- Prepare policy briefs translating complex data into actionable insights
- Coordinate initiatives on gender equality, open science, early-career researcher participation
- Support ERC Scientific Council and European Commission decision-making
Technical Expertise:
- Advanced statistical modeling and bibliometric network analysis
- Machine learning for research evaluation and impact prediction
- Data governance and strategic foresight for EU research policy
July 2025 - Present | Greater Uppsala Metropolitan Area
At Uppsala University's Division of Visual Information and Interaction (Vi3), I lead research in medical imaging, computer vision, and explainable AI for neuroradiology.
Research Highlights:
- Developed 3+ image processing pipelines for MRI analysis
- Reduced manual annotation effort by 25-30%
- Achieved >95% AUC for neurodegenerative biomarker detection
- Led federated learning benchmark across 5 Swedish hospitals (>0.9 accuracy under GDPR)
- Submitted 2 manuscripts (Medical Image Analysis, NeuroImage)
- Presented at MICCAI 2025, ECR 2026, Nordic AI in Medicine Summit
- Mentor 2 PhD candidates in deep learning for brain tumor detection
Technical Stack:
- 3D CNNs and transformer architectures
- Explainable AI (SHAP, Integrated Gradients, Captum)
- Multi-institutional data harmonization
- Privacy-preserving AI workflows
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University of Luxembourg Thesis: "Integrative Network Analysis of Transcriptomic and Proteomic Data to Uncover Dysregulated Signaling Cascades in Early-Stage Neurodegeneration" Focus:
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DTU - Technical University of Denmark Thesis: "Designing Adaptive Human-AI Systems for Collaborative Problem Solving in Fetal Ultrasound Imaging" Focus:
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Linköping University Thesis: "Application of Explainable AI for Predictive Diagnostics in Oncology using Clinical Data" Coursework:
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Tampere University Thesis: "SecureSense: Design of Wireless Sensor Network for Intelligent Safety Applications" Key Projects:
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International School of Helsinki Higher Level: Mathematics, Biology, English |
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| Language | Proficiency | Level |
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| Finnish | Native or Bilingual | 100% |
| Azerbaijani | Native or Bilingual | 100% |
| English | Full Professional | 95% |
| Swedish | Full Professional | 95% |
| Danish | Professional Working | 85% |
| Turkish | Professional Working | 85% |
| French | Professional Working | 75% |
| German | Limited Working | 60% |
| Norwegian | Limited Working | 60% |
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Enterprise AI Platform Multimodal medical imaging analysis with XAI
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Published Research Explainable AI framework for biometry prediction
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Google Health AI Large-scale safety evaluation framework
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Privacy-Preserving AI Multi-institutional collaborative training
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Neurodegeneration Research Integration of transcriptomic & proteomic data
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Neuroradiology Pipeline Automated MRI segmentation with 3D U-Net
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Imanov, O. Y. L., Chen, J., & Sharma, R. (Forthcoming). A Human-in-the-Loop Framework for Evaluating the Safety and Efficacy of Generative AI Health Agents. Journal of the American Medical Informatics Association (JAMIA).
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Imanov, O. Y. L., & Nielsen, M. B. (2025). Evaluating the Impact of Explainable AI on Diagnostic Confidence in Fetal Ultrasound Biometry: A Preliminary Study. Ultrasound in Obstetrics & Gynecology. DOI: 10.1002/uog.24589
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Jensen, L., Rasmussen, S., & Imanov, O. Y. L. (2025). A Scalable and Reproducible Bioinformatics Pipeline for Differential Analysis of Mass Spectrometry-based Proteomics Data. Journal of Proteome Research, 24(2), 112-125. DOI: 10.1021/acs.jproteome.4c00123
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Laitinen Imanov, O. Y., & Virtanen, A. (2024). Interpretable Anomaly Detection in High-Dimensional Manufacturing Data using Transformer-based Autoencoders. IEEE Transactions on Industrial Informatics, 20(4), 3145-3154. DOI: 10.1109/TII.2023.1234567
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Schmidt, K., Imanov, O. Y. L., & Schneider, I. (2024). Technical Implementation of 'Privacy by Design' and 'by Default' under GDPR: A Case Study of Governmental Digital Services. Proceedings on Privacy Enhancing Technologies (PoPETs), 2024(3), 45-62. DOI: 10.56553/popets-2024-0071
Book Chapters & Conference Papers
- Imanov, O. Y. L., & Kumar, S. (2025). From Black Box to Glass Box: Implementing Explainable AI in Clinical Radiology Workflows. In A. Gupta & L. Wang (Eds.), Artificial Intelligence in Medical Diagnostics: A Practical Guide (pp. 145-168). Springer Nature.
- Imanov, O. Y. L. (2024). Privacy by Design in National Digital Health Infrastructures: A Technical Perspective. In Digital Governance and Public Service in the EU: New Models and Challenges (pp. 88-105). Luxembourg: Publications Office of the European Union.
- MICCAI 2025 - "Federated Learning for Multi-Institutional Brain Tumor Segmentation"
- ECR 2026 - "Explainable AI in Neuroradiology: Clinical Validation Study"
- Nordic AI in Medicine Summit 2025 - "Privacy-Preserving AI in Swedish Healthcare"
- EuPA 2025 - "ML-based Biomarker Discovery in Proteomics Data"
- Google Cloud Professional Machine Learning Engineer
- Google Cloud Professional Data Engineer
- Google Cloud Professional Cloud Architect
- Google Cloud Professional DevOps Engineer
- Google Cloud Professional Security Engineer
- AWS Certified Machine Learning - Specialty
- AWS Certified Solutions Architect - Professional
- AWS Certified DevOps Engineer - Professional
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: DevOps Engineer Expert
- Certified Kubernetes Administrator (CKA)
- Certified Kubernetes Application Developer (CKAD)
- Deep Learning Specialization (DeepLearning.AI)
- Natural Language Processing Specialization (DeepLearning.AI)
- Computer Vision Specialization (Vanderbilt University)
- Reinforcement Learning Specialization (University of Alberta)
- TensorFlow Developer Professional Certificate
- XAI: Explainable Artificial Intelligence (H2O.ai)
- GANs Specialization (DeepLearning.AI)
- Probabilistic Graphical Models (Stanford)
- Genomic Data Science Specialization (Johns Hopkins)
- Bioinformatics Specialization (UC San Diego)
- Single-Cell RNA-Seq Analysis (Wellcome Sanger Institute)
- NextFlow & nf-core for Reproducible Workflows
- Proteomics: Methods and Applications in Medicine (KAIST)
- AlphaFold & Protein Structure Prediction (EMBL-EBI)
- QIIME 2 for Microbiome Analysis
- FAIR Data Principles for Life Sciences
- Certified Information Systems Security Professional (CISSP)
- Certified Information Privacy Professional/Europe (CIPP/E)
- Certified Information Privacy Manager (CIPM)
- Certified Ethical Hacker (CEH)
- CompTIA Security+
- GDPR Practitioner Certificate
mindmap
root((Olaf Yunus<br/>Laitinen-Imanov))
Medical AI
Explainable AI
SHAP
LIME
Integrated Gradients
Captum
Medical Imaging
Neuroradiology
Ultrasound
Multi-modal Fusion
Clinical Decision Support
Diagnostic Assistance
Risk Prediction
Treatment Planning
Bioinformatics
Clinical Genomics
Variant Calling
WGS/WES Analysis
Rare Disease Diagnostics
Proteomics
Mass Spectrometry
Differential Analysis
Biomarker Discovery
Multi-Omics
Data Integration
Network Analysis
Systems Biology
Technology Leadership
Product Strategy
Roadmap Planning
Market Analysis
User Research
Team Management
ML Engineers
Data Scientists
Clinical Experts
Fundraising
Pitch Development
Investor Relations
Grant Writing
Research & Policy
European Research
ERC Evaluation
Policy Analysis
Impact Assessment
Science Policy
Evidence-based Policy
Research Evaluation
Strategic Foresight
Publications
Peer Review
Scientific Writing
Conference Presentations
| Category | Metrics |
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| Funding Secured | €3M+ in research grants (ERC, DFF, FCAI, Business Finland) |
| Publications | 6+ peer-reviewed papers + 2 book chapters |
| Awards | DTU Fellowship, President's Medal, Google Peer Bonus, FCAI Spotlight |
| Leadership | Led 15+ engineers, mentored 10+ students, supervised 2 PhDs |
| Clinical Impact | 500+ patient cases analyzed, 50K+ scans processed |
| Data Scale | 2+ TB genomic data, 10+ TB proteomics, multi-TB imaging |
| Accuracy | >95% diagnostic accuracy, >0.9 federated learning performance |
| Efficiency | 25-30% reduction in manual workload, 20% faster pipelines |
| Role | Institution | Courses & Activities |
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| Adjunct Instructor | Linköping University | TAMS11 Probability & Statistics TAMS17 Statistical Theory TAMS39 Multivariate Methods TDDE15 Data Analysis with Python 150+ students per semester |
| PhD Supervisor | Uppsala University | Mentoring 2 PhD candidates in deep learning for medical imaging |
| Research Mentor | FCAI | Guiding junior researchers on RL & computer vision projects |
| Thesis Advisor | Linköping University | Co-supervised MSc thesis on XAI applications |
Pedagogical Contributions & Innovation:
- Developed 25+ interactive Jupyter Notebook modules incorporating authentic biomedical datasets for experiential learning
- Pioneered integration of Explainable AI methodologies into machine learning curriculum, emphasizing interpretability and ethical considerations
- Achieved consistently high student evaluations (>90% satisfaction) across theoretical and applied courses
- Recognized with nomination for Faculty Excellence in Teaching Award (2025) for innovative pedagogical approaches
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Vice President |
Volunteer |
Volunteer |
Scout |
Research Collaborations: Interdisciplinary projects in medical AI, explainable machine learning, federated learning architectures, and multi-omics integration
Strategic Advisory: Consulting engagements for HealthTech ventures, AI product strategy, regulatory compliance (MDR/IVDR, FDA), and clinical validation studies
Academic Partnerships: Joint PhD/postdoctoral supervision, collaborative grant applications (H2020, Horizon Europe, NIH, NSF), and co-authored publications
Policy & Governance: Evidence-based policy development for European research frameworks, AI ethics committees, and healthcare technology assessment
Open-Source Development: Contributions to bioinformatics pipelines, ML infrastructure, reproducible research frameworks, and FAIR data initiatives
If you find my research and contributions valuable, please consider starring relevant repositories or citing my publications.
Last updated: November 2025 | November 2025 | November 2025 | November 2025 | November 2025 | November 2025 | November 2025 | December 2025 | Curriculum Vitae available upon request | ORCID: 0009-0006-5184-0810