Who am I?
A Research Fellow at Warwick Manufacturing Group (WMG), University of Warwick, where I develop AI driven solutions to advance smart battery manufacturing. My work centers on building machine learning and computer vision systems for quality control of production lines through advanced data analysis.
My research background spans from underwater imaging focusing on quality assessment/enhancement, and anomaly detection to multisensor fusion for 3D reconstruction using sonar and stereo vision. More recently, I have shifted towards applications in battery manufacturing, applying deep learning and computer vision for predictive maintenance using image and time series data and optimization of manufacturing processes through AI powered analytics.
Contact Information
- Gmail: h.farhaditolie@gmail.com
- E-mail: hamidreza.farhadi-tolie@warwick.ac.uk (Academic)
- E-mail: h.farhadi-tolie@rgu.ac.uk (Academic)
My Expertise
Research
Image/Signal Processing, Deep Learning, Computer Vision, Data Science
Programming
Python, C#, C++, MATLAB
Modalities
Sonar, Optical, Hyperspectral, Acoustic, Stereo Vision
My Resume
Experience
Research Fellow
Warwick Manufacturing Group
2025-Present
Research Assistant
Warwick Manufacturing Group
2024-2025
Demonstrator
Robert Gordon University
2022-2024
Teaching Assistant
Institute for Advanced Studies in Basic Sciences
2016-2021
Education
PhD in Artificial Intelligence
2022 - 2025
Robert Gordon University, Aberdeen, United Kingdom.
MSc in Computer Science - Data Mining
2018 – 2021
Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
BSc in Information Technology Engineering
2014 – 2018
Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
Skills
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Python
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MATLAB
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C#
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C++
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HTML/CSS
Languages
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English
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Persian
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Turkish
Interests
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Music
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Programming
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Travel
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Sport
Other Skills
- Teaching
- Team Collaboration
- Scientific Communication
- Critical Thinking
- Problem Solving
- Time Management
- Independent Working
- Research Adaptability
Presentation Stats
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Total
8 -
Upcoming
0
Latest Updates
Talk: From Physics to Data – Explainable AI for Battery Dynamics
I have been invited to present my work titled “From Physics to Data: Explainable AI Linking Operando X-ray Diffraction to Electrochemical Cell Dynamics”. This talk will examine how explainable artificial intelligence can be used to analyse operando X-ray diffraction data alongside electrochemical measurements to investigate whether AI models inherently capture meaningful battery physics.
Invited Talk: AI and the Future of Lithium-Ion Battery Manufacturing at Keele University
In January 2026, I will be delivering an invited talk at the IOP West Midlands Keele Physics Centre on the role of AI in lithium-ion battery manufacturing.
Paper on ultrasonic sensing and deep transfer learning for large-format battery SoC estimation accepted in Energy & AI
In a joint collaboration with colleagues at Imperial College London, our paper titled “Large Format Battery SoC Estimation: An Ultrasonic Sensing and Deep Transfer Learning Predictions for Heterogeneity” has been accepted for publication in Energy & AI.
Journal: Energy & AI
Authors: Hamidreza Farhadi Tolie, Benjamin Reichmann, James Marco, Zahra Sharif Khodaei, Mona Faraji Niri