About

Research Focus

I apply machine learning and deep learning to complex biomedical data, with multimodal neuroimaging as the central lens. My work centres on building end-to-end analytical pipelines that integrate brain imaging with other modalities — including EEG, movement, and speech — to accelerate diagnosis and therapeutic target identification in Alzheimer's disease, Parkinson's disease, autism, and depression. My proficiency spans Python, MATLAB, R, and Shell, together with the major deep learning frameworks, alongside scalable data engineering and high-performance computing.

Professional Experience

  • 2025 to date: Principal Scientist Neuroimaging, Boehringer Ingelheim (Germany).
  • 2018-2025: Chief Technology Officer, ClearSky Medical Diagnostics Ltd., York (UK).
    • Contributed to the development of machine learning (ML)-based medical devices for diagnosing and monitoring neurodegenerative conditions, including PD-Monitor, LID-Monitor, and MCI-Monitor.
    • Optimized ML for movement disorder analysis, enhancing diagnostic precision.
    • Collaborated with multidisciplinary teams, including clinicians and engineers, to integrate AI-driven solutions into clinical applications.
  • 2022-2025: Postdoctoral Researcher, The Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität München (LMU), University of Munich (Germany).
    • Developed ADPrep, an automated neuroimaging preprocessing pipeline (using Python, MATLAB, R, Shell, etc.) for multiple modalities (MRI, fMRI, PET, DTI), optimizing data quality and analysis. This pipeline's effectiveness is demonstrated by its use in over 25 peer-reviewed publications (2023-present) in neurodegenerative disease research, and it is going to be integrated into the GRIP platform, a Gates Ventures initiative.
    • Developed, validated, and deployed a deep learning model to infer full Alzheimer's disease A/T/N classification from single tau-PET scans, achieving high predictive accuracy for amyloid-PET (r=0.8) and MRI grey matter density (r=0.76).
    • Managed the High-Performance Computing (HPC) resources and supported research labs with their data analysis needs on the HPC, in addition to serving as an IT assistant for the LMU Hospital.
  • 2021-2022: Postdoctoral Research Associate, University of York (UK).
    • Applied white-box machine learning to resting-state fMRI data to differentiate depression from healthy controls.
  • 2021: Machine Learning and Image Processing Engineer, smartR.ai, Edinburgh (UK).
    • Created deep learning pipelines to normalize and match color profiles between FIBI and H&E histological images.
  • 2020-2021: Research Fellow, University of Aberdeen (UK).
    • Used neuroimaging to define a fatigue-related brain network in rheumatoid arthritis, exploring how therapies impact it for potential DBS or similar targeting.
  • 2019: Post-Doctoral Researcher in Neuroimaging, The University of Dublin (Ireland).
    • Linked speech patterns to brain volume changes in MCI/AD, exploring speech as an early marker of cognitive decline.
  • 2016-2017: Professional Engineer, My Therapy Tools Ltd.
    • Provided professional engineering support to a Horizon 2020 telerehabilitation platform development for brain injury patients.

Education

  • 2014-2018: PhD Electronic Engineering, University of York (UK).
    • Supervision: Professor Stephen Smith.
    • Thesis: Cartesian Genetic Programming Classification of Resting-State fMRI: Towards a Brain Imaging Biomarker for Parkinson's Disease.
  • 2013: MSc Digital Signal Processing, University of York (UK).
  • 2010: BSc Applied Science Electronics, University of Science and Arts of Yazd (Iran).

Collaborations

I have had the pleasure and honour of working with researchers such as Dr. Franzmeier, Professor Smith, Dr. Waiter, Professor Basu, and Professor Reilly on multiple projects. These collaborations have focused on developing advanced automated neuroimaging preprocessing pipelines, objective assessment of depression from rsfMRI brain scans, investigating the underlying mechanisms of rheumatoid arthritis-related fatigue in the brain, and analyzing speech and brain imaging features for the classification of Alzheimer's disease and mild cognitive impairment.

By its nature, this work is highly interdisciplinary, and I have been fortunate to collaborate closely with clinicians and clinical scientists as well as researchers across engineering, computer science, and the basic sciences — translating between these perspectives is one of the parts of the work I enjoy most.

Technical Skills

Neuroimaging data analysis, Machine Learning, Deep Learning, Git, High Performance Computing, MATLAB, C, R, Python, Shell, PHP, SQL, Docker, Linux, AI-based medical devices.