Morteza Najibi

Morteza Najibi

Researcher in statistical modelling and AI for health

Lund University

Biography

I am a Researcher at the Department of Clinical Sciences, Lund University, Sweden. I develop probabilistic models that bring together computational, experimental and clinical evidence to support decision-making in medical research. My long-term goal is to advance precision medicine through robust patient-level prediction, with careful attention to uncertainty and how well models perform across populations and settings.

My research connects statistical machine learning, modelling of real-world health data and AI in health. I take a transdisciplinary approach: bringing methods, data and scientific perspectives together to understand complex biological and clinical systems. Reliability, reproducibility and meaningful interpretation guide this work.

My experience spans academic research, scientific software development and research computing infrastructure. Working with biologists, clinicians, epidemiologists, physicists and computer scientists has shaped how I connect methodological questions with practical research needs, from molecular and imaging studies to clinical cohorts and registries.

Across my career, I have worked on:

  • Protein structure modelling
  • Genetic variation and disease-related biological pathways
  • Neuroimaging and functional MRI
  • Biomedical registries and clinical cohort data
  • Longitudinal modelling, survival analysis and patient outcome prediction

Here you can explore my research papers and reports, teaching materials, external activities and blog posts. I welcome opportunities for collaboration and scientific discussion.

Download my CV

Interests
  • Statistical machine learning and patient-level prediction
  • Bayesian and non-parametric modelling
  • Longitudinal, survival and functional data
  • Directional statistics
  • Real-world evidence and biomedical data integration
  • Trustworthy AI for health
Education

  • PhD in Statistics, 2015

    Shahid Beheshti University

  • MSc in Mathematical Statistics, 2010

    Tarbiat Modares University

  • BSc in Statistics, 2008

    Persian Gulf University

Research expertise & skills

Connecting methods, data and disciplines for robust patient-level prediction.

Statistical methods Probabilistic modelling, prediction and uncertainty. R Python C++ Methods & selected workShow less

How I use this expertise

I develop and apply Bayesian, longitudinal, survival and functional-data methods to biomedical questions. The emphasis is on interpreting variation and building robust patient-level predictions.

Selected work

Biomedical data Connecting clinical, molecular and imaging evidence. SQL OMOP / OHDSI MRI / fMRI Methods & selected workShow less

How I use this expertise

My work spans clinical registries, protein structure, genetic variation, proteomics and MRI/fMRI. Database organisation and OMOP harmonisation support interpretable, reusable analyses across these research settings.

Selected work

Scientific software & systems From statistical packages to Linux and MRI infrastructure. Shiny Linux Docker Git HPC Methods & selected workShow less

How I use this expertise

My experience includes R package contributions, Shiny applications, Linux server administration, MRI data workflows and HPC administration. This connects hands-on technical work with research coordination and leadership of SDAT.

Selected work

Research AI Local language models, retrieval and evidence-grounded research tools. Local modelsRAGEvaluation Explore the planned workflowShow less

Research direction

Building on my statistical and computing background, I am developing workflows that connect local language models with research documents and structured data. Retrieval, source attribution and systematic evaluation are central to this direction.

Proposed technology stack

Candidate tools for this direction; the integrated system is planned.

Data & backend

Structured records, document embeddings and analytical queries.

Retrieval & context

Document indexing, semantic retrieval and reranking before generation.

Local model serving

Local prototyping with Ollama; vLLM as an alternative for a GPU serving environment.

Evaluation & sensitivity

RAG quality assessment alongside separate analyses of how model inputs affect outputs.

Select an area to explore its methods, applications and tools.

Experience

Research, teaching & scientific leadershipSelect a role to read more
Statistician and Data ScientistRheumatology Research Group, Lund University May 2022to present

Clinical and registry research in rheumatology; longitudinal, survival and machine-learning models for patient outcomes.

Responsibilities include:

  • Conducting epidemiological and clinical research in rheumatology
  • Developing survival and longitudinal models for disease progression and outcomes
  • Designing and analyzing case-control and cohort studies using registry data
  • Applying machine learning methods for prediction and feature selection
  • Managing and harmonizing large-scale clinical datasets

Lund, Sweden · Organisation website

Associate Senior ResearcherProtein Bioinformatics Group, Lund University Oct 2021to Apr 2022

Protein bioinformatics and genetic variation, connecting statistical methods with structural and biological data.

Responsibilities include:

  • Conducting research in protein bioinformatics and structural modeling
  • Investigating genetic variation and disease-related biological pathways
  • Applying statistical and computational methods to biological data

Lund, Sweden · Organisation website

Postdoctoral Research FellowAndré Lab, Lund University Jun 2019to Oct 2021

Probabilistic protein-structure prediction and statistical machine learning across molecular science and computational biology.

Responsibilities include:

  • Developing probabilistic models for protein structure modeling and prediction
  • Conducting research in statistical machine learning and computational biology
  • Collaborating across molecular science and data-driven modeling projects

Lund, Sweden · Organisation website

Assistant Professor of StatisticsDepartment of Statistics, Shiraz University Sep 2017to Jul 2019

Teaching R, C++, statistical modelling and simulation; research in machine learning and directional statistics.

Responsibilities include:

  • Taught Advanced R and C++ Programming, Statistical Modeling and Simulation Studies, Probability, and Linear Algebra courses (70%)
  • Researched statistical machine learning and directional statistics (30%)
  • Member of entrepreneur and innovation committee

Shiraz, Iran · Organisation website

CEOScientific Data Analysis Team Oct 2016to Dec 2021

Leadership of scientific data analysis, modelling and deployment; strategic planning and project management.

Responsibilities include:

  • Analysing
  • Modelling
  • Deploying
  • Strategic Planning
  • Project Management

Tehran, Iran · Organisation website

Assistant Professor of StatisticsDepartment of Statistics, Persian Gulf University Sep 2015to Sep 2017

Teaching Bayesian modelling and statistical methods; research and advisory work in statistics and strategic planning.

Responsibilities include:

  • Taught Bayesian statistical modeling, simulation studies, probability, statistical methods, and linear algebra courses (70%)
  • Researched statistical machine learning and directional statistics (30%)
  • President’s Management Advisory Board member in statistics and strategic planning

Bushehr, Iran · Organisation website

Full academic CV

Research notes

Transdisciplinary research: connecting knowledge, planning and improvement
Connecting knowledge and continual improvement with cooperation, goodwill and shared prosperity, drawing on transdisciplinary research and W. Edwards Deming.
Morteza Najibi
Sep 6, 2026 11 min read
Transdisciplinary research: connecting knowledge, planning and improvement

Contact

For research collaboration, academic enquiries or a conversation about shared interests, please get in touch.