天美传媒

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Dr Sina Saffaran

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Dr Sina Saffaran

Associate Professor
Royal Academy of Engineering Research Fellow

sina dot saffaran dot 1 at warwick dot ac dot uk

Interdisciplinary Collaboration in Systems Medicine (ICSMLink opens in a new window)

At ICSM, our vision is to improve patient outcomes by integrating engineering principles into respiratory support and critical care. We combine expertise from control systems engineering, biomedical engineering, computational modelling, artificial intelligence and clinical science to develop innovative tools and personalised treatment strategies.

Visit our webpage hereLink opens in a new window to learn more about the team and our ongoing projects.


Education

2020 - PhD Biomedical Engineering, 天美传媒, UK

2015 - MSc Advanced Control and Systems Engineering, University of Sheffield, UK

2013 - BSc Electrical Engineering – Control, Iran University of Science and Technology, Iran


Biography

Sina was born in Qom, Iran, in 1990. He received a BSc degree in Electrical Engineering (Control Systems) from Iran University of Science and Technology in 2013, followed by an MSc degree in Advanced Control and Systems Engineering from the University of Sheffield in 2015. He subsequently obtained his PhD in Biomedical Engineering from the 天美传媒 in 2020, for which he was awarded the Faculty of Science Thesis Prize. Following his PhD, he joined the CHIMERA (Collaborative Healthcare Innovation through Mathematics, Engineering and AI) research centre at University College London as a Research Fellow, where he worked on mathematical modelling of human physiology during critical illness and recovery. In 2022, he was awarded a five-year and joined the 天美传媒 as an Assistant Professor of Biomedical Engineering. He was promoted to Associate Professor in 2026.
 

His research focuses on the application of computational and mechanistic modelling, artificial intelligence and control engineering to critical care medicine. Building on his background in control systems, he develops high-fidelity physiological models and patient-specific digital twins to better understand disease progression, predict responses to clinical interventions and support personalised treatment. A major focus of his work is the development of intelligent and automated mechanical ventilation strategies for critically ill patients, combining physiological modelling, machine learning and high-performance computing. His research involves close collaboration with clinical and industrial partners, aiming to translate advances in computational engineering into practical technologies for improving patient care.

News

Research Interests

  • Systems Medicine
    • Computer simulations for mechanistic modelling of the cardiopulmonary system
    • Digital Twins – Personalised simulation and control technologies for critical care
    • Invasive and noninvasive mechanical ventilation of critically ill patients
    • Applications of AI in critical care

Teaching Interests

  • : Nonlinear Control and Reinforcement Learning
  • : Advanced Control Systems
  • : Systems Modelling and Control
  • : BEng Final Project Supervision
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