Alireza Hasheminasab
Lead Computational and AI/ML Research Scientist
Dr Seyed Alireza Hasheminasab is a Lead Computational and AI/ML Research Scientist at the Oxford Translational Myeloma Centre (OTMC), University of Oxford. His research focuses on developing artificial intelligence and machine-learning methods to analyse complex biomedical data and translate computational insights into clinically meaningful applications.
His work centres on integrating multi-omics and clinical data, including genomics, transcriptomics, population-scale proteomics, and immune profiling, to support early disease detection and biomarker discovery in multiple myeloma and related conditions such as systemic amyloidosis. He contributes to the development of computational frameworks for translational research and precision medicine within OTMC.
He has experience working with large-scale healthcare datasets, including UK Biobank, CPRD, and the NHS England Secure Data Environment (SDE), as well as institutionally curated clinical datasets. His work emphasises reproducibility, scalability, and alignment with UK data governance standards to support translation into real-world clinical applications.
He collaborates with clinicians, experimental researchers, and industry partners, and has contributed to international projects applying AI methods in real-world healthcare settings.
Prior to his current role, he was a Postdoctoral Data Scientist at the Centre for Statistics in Medicine (University of Oxford), working on machine-learning approaches to epidemiology and healthcare data.
Recent publications
Evaluating large language models for clinical note processing: local fine-tuning and internal-external validation using electronic health records from South Asia.
Journal article
Hasheminasab SA. et al, (2026), BMC Med Inform Decis Mak, 26
AI-based analysis reveals novel blood protein biomarkers for predicting cardiac amyloidosis approximately a decade early: a UK biobank study
Conference paper
Hasheminasab A. et al, (2025), Blood, 146
nti-Adhesion Properties of KTX-1001, a Selective NSD2/MMSET Inhibitor, Enhance Carfilzomib Sensitivity in Multiple Myeloma
Journal article
Nandana D. et al, (2025), Clinical Lymphoma Myeloma and Leukemia, 25, S213 - S214
Ensuring Equity in AI: Addressing Data-Driven Biases in Health-specific Large Language Models for Global Use
Other
Khalid S. et al, (2025)
nti-Adhesion Properties of KTX-1001, a Selective NSD2/MMSET Inhibitor, Enhance Carfilzomib Sensitivity in Multiple Myeloma
Conference paper
Nandana D. et al, (2025), CLINICAL LYMPHOMA MYELOMA & LEUKEMIA, 25