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Postdoctoral Researcher

KU Leuven - Hasselt University

PhD Researcher

KU Leuven - Hasselt University

Took a two-pronged approach to federated learning (FL), focusing on building the infrastructure for secure data collaboration and advancing the analysis with optimization techniques to improve performance across diverse, fragmented datasets.

• led the FL-MS-RWD study, where we tackled the challenge of predicting disability progression in Multiple Sclerosis (MS) using real-world data from over 26,000 patients—one of the largest datasets ever analyzed in this context. To make this possible, I developed a new federated learning schema and algorithm that allowed global models to adapt to the unique characteristics of local datasets. This not only bridged the gap between centralized and decentralized approaches but also showed that FL can deliver powerful, privacy-preserving models that often outperform traditional methods, all while keeping patient data secure.
• Built a flexible, hybrid data pipeline that integrated diverse datasets and supported multiple data-sharing models. This pipeline powered the Global Data Sharing Initiative for COVID-19 and MS, assembling the *largest-ever dataset* for MS and COVID-19 research and driving insights at scale.
• Developed the FL4E (Federated Learning For Everyone) platform, a comprehensive framework that empowers stakeholders to adapt between centralized and federated analysis. Introduced the concept of the “degree of federation”, a practical tool that allows tailored privacy and data-sharing configurations to meet the needs of specific healthcare projects.
• Collaborated with multidisciplinary teams, including clinicians, data scientists, and engineers, to ensure that technical solutions met real-world healthcare needs and translated into measurable outcomes.
• Presented findings at international conferences and contributed to [15 peer-reviewed papers] , sharing innovations that help advance healthcare data science and beyond.

Research Assistant

Decision Support System Lab – Irandoc

Worked on optimization and simulation projects, leveraging System Dynamics and Agent-Based Modeling to improve logistic operations and information management in Iran’s public sector.

• Developed a hybrid model for Iran Post using multi-criteria decision-making, reducing transportation costs by 15%.
• Integrated non-dominated sorting genetic algorithms into simulation models to optimize hub-location allocation and overall logistics processes.
• Conducted qualitative analyses of records and information management for MAFA, leading to strategic process optimizations and reduced operational bottlenecks.
• Implemented a system dynamics-based simulation model tied to a decision support dashboard for real-time scenario analysis, aiding data-driven decision making.

PhD

KU Leuven

Engineering Science; Supervisor: Prof. Yves Moreau

Predoctoral

KU Leuven

Engineering Science

PhD

Universiteit Hasselt

Biomedical Science; Supervisors: Prof. Liesbet M. Peeters, Prof. Niels Hellings

Master

Kharazmi University

Industrial Engineering, Socio-Economic Systems Engineering; Supervisors: Prof. HamidReza Izadbakhsh, Prof. Ammar Jalalimanesh

Fully Funded PhD Scholarship

KU Leuven

Fully Funded PhD Scholarship

Universiteit Hasselt