Scholar
Farhad Rezazadeh
Google Scholar ID: 4V5XoVAAAAAJ
Sr. Applied AI/GenAI | ACM Pro. | MSCA & IEEE & COST Grantee | Government Ph.D. Grant
Citations & Impact
Citations
458
H-index
13
i10-index
16
Publications
20
Co-authors
19
list available
Publications
Resume (English only)
Academic Achievements
- His AI innovation in B5G/6G resource allocation recognized as a top EU-funded innovation by the European Commission’s Innovation Radar
- Holds 2 patents linked to EU-H2020 projects
- Published over 34 top-tier journal/conference papers and book chapters
- Completed 240+ verified peer reviews for academic publications
- Serves as Organizer, Chair, TPC member for IEEE conferences, and Guest Editor for Elsevier journals
- Active member of IEEE Young Professionals and IEEE Spain – Technical Activities and Standards
- ACM Professional Member and Coordinator of the IEEE Trustworthy Internet of Things (TRUST-IoT) working group
- Recently invited as reviewer for Springer Nature’s Artificial Intelligence journal and TPC member for ICN 2025
Research Experience
- Currently Sr. Applied AI/GenAI Scientist in Product and Growth at Hostelworld Group
- Former Visiting Associate Professor at UPC
- Previously Senior Applied AI Engineer at the Services as NetworkS (SaS) Research Unit, CTTC
- Served as Research Assistant at the Advanced Broadband Communications Center (CCABA)
- Secondee at NEC Lab Europe under supervision of Prof. Xavier Costa-Pérez
- Conducted scientific missions at Technical University of Munich (TUM), Hamburg University of Technology (TUHH), and Universitat de Girona (UdG)
- Participated in 8 European and National R&D projects on 5G/B5G/6G, leading technical tasks in Applied AI and XAI
Background
- Research interests include Applied AI (eXplainable AI, Neuro-Symbolic AI, Generative AI, Quantum AI, Graph Neural Networks, Lifelong RL, Mixture of Experts, Reservoir Computing, etc.)
- Focuses on advancing Large Language Models (LLMs) through fine-tuning, RAG, alignment, and optimization for simulation automation, content generation, and decision-making
- Designs end-to-end MLOps pipelines using Azure Machine Learning and Azure DevOps with CI/CD workflows
- Works on data science and engineering using Azure Databricks, Data Lake, Delta Lake, and Lakehouse architectures
- Researches 5G/6G topics including Open RAN, Zero-Touch Networking, Softwarization, Cloudification, Massive Network Slicing, MEC, O-RAN-xAPPs, and Smart Grid
