Imperial College London

DrMatthieuKomorowski

Faculty of MedicineDepartment of Surgery & Cancer

Clinical Senior Lecturer
 
 
 
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Contact

 

m.komorowski14

 
 
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Location

 

5L15Lab BlockCharing Cross Campus

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Summary

 

Summary

Matthieu Komorowski MD PhD is a Clinical Senior Lecturer in the department of Surgery and Cancer at Imperial College London and an honorary consultant in intensive care and anaesthetics at Charing Cross Hospital. He holds full board certification in anaesthetics and intensive care in both France and the UK. He was previously a research fellow at the European Space Agency and holds additional qualifications in space, mountain, diving and hyperbaric medicine.

He joined Imperial College London in 2014 and completed a Masters of Research and a PhD in Medicine and Bioengineering, supervised by Profs Aldo Faisal and Anthony Gordon. In 2016/2017, he was a visiting scholar at the Laboratory of Computational Physiology at Harvard-MIT Division of Health Sciences and Technology (Profs Roger Mark and Leo Celi).

In his research, he applies machine learning techniques to build the next generation of decision support systems for critical care with a specific focus on sepsis.

Publications

Journals

Komorowski M, 2022, Anesthesia and Surgery in Space: Reply, Anesthesiology, Vol:136, ISSN:0003-3022, Pages:400-400

Komorowski M, Thierry S, Stark C, et al., 2021, On the challenges of anesthesia and surgery during interplanetary spaceflight., Anesthesiology, Vol:135, ISSN:0003-3022, Pages:155-163

Patel BV, Haar S, Handslip R, et al., 2021, Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom, Intensive Care Medicine, Vol:47, ISSN:0342-4642, Pages:549-565

Conference

Festor P, Habil I, Jia Y, et al., Levels of Autonomy & Safety Assurance forAI-based Clinical Decision Systems, WAISE 2021 : 4th International Workshop on Artificial Intelligence Safety Engineering

Festor P, Luise G, Komorowski M, et al., Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition, ICML2021 workshop on Interpretable Machine Learning in Healthcare

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