Dr. Fatemeh Shakeri
Post Doctoral Fellow
Start date: May 2024
Target completion date: June 2026
Research focus
My research is centred on the application of data-driven methodologies, machine learning and artificial intelligence within the domains of engineering, specifically targeting advancements in energy storage and conversion systems.
Awards and recognitions
– Mitacs Accelerate Global Excellence Award, 2022
– Chemical and Petroleum Engineering Graduate Excellence Award 2018-2022
Previous studies
– B.Sc. in Chemical Engineering, Iran University of Science and Technology, Tehran, Iran
– M.Sc. in Chemical Engineering, Sharif University of Technology, Tehran, Iran
– Ph.D. in Chemical Engineering, University of Calgary, Alberta, Canada
Publications
1. ShakeriHosseinabad, Fatemeh, et al. “Influence of flow field design on zinc deposition and performance in a zinc-iodide flow battery.” ACS applied materials & interfaces 13.35 (2021): 41563-41572.
2. ShakeriHosseinabad, Fatemeh, et al. “Electrode materials for enhancing the performance and cycling stability of zinc iodide flow batteries at high current densities.” ACS Applied Materials & Interfaces 15.29 (2023): 34711-34725.
3. Shakerihosseinabad, Fatemeh, et al. “The role of hydrophobicity and active layer thickness on degradation and durability of the air cathode in alkaline fuel cells.” Journal of Power Sources 578 (2023): 233209.
4. Vykhodtsev, Anton V., Shakeri Hosseinabad Fatemeh., et al. “Artificial intelligence-assisted physics-based model of lithium-ion battery for power systems operation research.” Journal of Energy Storage 151 (2026): 120563.
5. Shakerihosseinabad, Fatemeh, Behrouz Far, and Hamidreza Zareipour. “Digital Twin-AI Framework for a Battery Management System.” Authorea Preprints (2026).
6. Shakerihosseinabad, Fatemeh, et al , Next-Generation Battery Management System:Integrating Digital Twins, Physics-Informed AI, and Reinforcement Learning in a Modular Multilayer Framework- A Perspective, Preprints (2026).
Industry experience
– Engaged in research collaboration as a Ph.D. visiting student with the Electrochemical Innovation Lab at the University College London
– Participated in a research collaboration and internship with Zinc8 Energy Solutions Inc. and the Natural Sciences and Engineering Research Council of Canada (NSERC)
– Collaborated on research initiatives with Alberta Innovates
Other
My primary interest lies in the application of data science, machine learning, and artificial intelligence within the field of renewable electrical energy systems, particularly focusing on batteries. I am passionate about leveraging computational techniques to optimize energy storage and conversion solutions and enhance the efficiency and sustainability of renewable energy technologies. I am also committed to contributing to innovative solutions that address global health and energy challenges.