Cihat Emre Ustun

Affiliation: Centre for Intelligent Transport - Queen Mary University of London

I'm currently working as a postdoctoral researcher at Queen Mary University of London, where I'm developing data-driven liquid ammonia spray models.

My research focuses on machine learning for fuels and combustion. I'm particularly interested in exploring bayesian ML/UQ and their applications in fuel and combustion science. I also have expertise in computational modelling, applied to various multiphysics and multiscale problems.

I completed my PhD in Mechanical Engineering from Queen Mary University of London in 2025, with my dissertation on "Accelerating Zero-Carbon Fuel Combustion Modelling with Machine Learning".

In my personal life, I enjoy running, working out, and spending time with my other half.

Cihat Emre Ustun

News

Jul 6, 2024
My new paper on Multi-objective Aerodynamic Design Optimization of a New Engine Intake Electromagnetic Wave Blocker has been accepted for publication in Physics of Fluids!
Jul 6, 2024
My new paper on Machine Learning Applications for Predicting Fuel Ignition and Flame Properties: Current Status and Future Perspectives has been accepted for publication in Energy & Fuels!
July 6, 2025
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Blog

Coming soon! I'll be sharing insights about my research, academic life, and thoughts on developments in the field.

Publications

COL
Optimized CFD modelling and validation of radiation section of an industrial top-fired steam methane reforming furnace
Mustafa Tutar, Cihat Emre Üstün, Jose Miguel Campillo-Robles, Raquel Fuente, Silvia Cibrián, Ignacio Arzua, Arturo Fernández, Gabriel A. López
Computers & Chemical Engineering - Dec 2021
URL
PHD
Applying machine learning techniques to predict laminar burning velocity for ammonia/hydrogen/air mixtures
Cihat Emre Üstün, Muhammed Reza Herfathmanesh, Agustin Valera Medina, and Amin Paykani
Energy and AI - May 2023
URL
PHD
Data-driven prediction of laminar burning velocity for ternary ammonia/hydrogen/methane/air premixed flames
Cihat Emre Üstün, Sven Eckart, Agustin Valera Medina, and Amin Paykani
Fuel - March 2024
URL
PHD
Probabilistic machine learning framework for chemical source term integration with Gaussian Processes: H2/air auto-ignition case
Cihat Emre Üstün and Amin Paykani
International Journal of Hydrogen Energy - June 2024
URL
COL
A thermodynamic computational model analysis of a newly designed Tri-Rotor (TR) volume air compressor
Mustafa Tutar, Cihat Emre Üstün, Huseyin Mutlu
Thermal Science and Engineering Progress- July 2024
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COL
Perspectives on NOX Emissions and Impacts from Ammonia Combustion Processes
Syed Mashruk, Hao Shi, Luca Mazzotta, Cihat Emre Üstün, B. Aravind, Roberto Meloni, Ali Alnasif, Elena Boulet, Radoslaw Jankowski, Chunkan Yu, Mohammad Alnajideen, Amin Paykani, Ulrich Maas, Rafal Slefarski, Domenico Borello, Agustin Valera-Medina
Energy & Fuels- October 2024
URL
COL
Machine Learning Applications for Predicting Fuel Ignition and Flame Properties: Current Status and Future Perspectives
Cihat Emre Üstün, Rodolfo Da Silva Machado De Freitas, Ekenechukwu Chijioke Okafor, Mahdi Shahbakhti, Xi Jiang, Amin Paykani
Energy & Fuels- July 2025
URL

Teaching

Information about my teaching experience and courses.

Current Teaching

Level 4-7 DS/ML/AI subjects: Teaching on demand as a faculty member at CambridgeSpark.

Past Teaching

Level 7 - Advanced engines and power systems: MSc Automotive Engineering.

Level 6 - High performance engine design: BEng Automotive Engineering.

Contact

Get in Touch

I'm always interested in discussing research collaborations, sharing ideas, or connecting with fellow researchers.

Email: prw287@qmul.ac.uk

Institution: SEMS, Queen Mary University of London

Office: Mile End Campus, SEMS Building, 147

Academic & Professional Profiles:

LinkedIn | Google Scholar | Scopus | University Profile

ORCID | ResearchGate | GitHub