Who am I?
I am 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.
Personal Info
- Birthdate: 09/06/1996
- 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 Teamwork Desktop Programming
Latest Updates
Paper accepted for publication in the Journal of Power Sources
Our paper, “Swin Transformer and Generative Adversarial Networks for Accurate Battery Electrode Thickness Prediction in Manufacturing Using Ultrasound Sensing,” has been accepted for publication in the Journal of Power Sources. This work presents a real-time, non-contact method that combines ultrasound sensing with deep neural networks and cGANs to accurately predict electrode thickness during battery manufacturing. The approach is validated on pilot-line data and offers a cost-effective solution for scalable, quality-assured production.
Journal: Journal of Power Sources
Authors: Hamidreza Farhadi Tolie, Erdogan Guk, James Marco, and Mona Faraji Niri
Awarded UK Global Talent Visa
I'm honoured to have been awarded the UK Global Talent Visa, endorsed by UK Research and Innovation (UKRI). This recognition was granted following my appointment as a Research Fellow on the Nextrode project at University of Warwick, funded by the Faraday Institution, and reflects my ongoing contributions to research in artificial intelligence and computer vision. Sincere thanks to the Warwick Manufacturing Group at the University of Warwick, Faraday Institution, and the Nextrode team for their invaluable support!
Paper accepted for publication in the Journal of Power Sources
Our paper titled "Exploring the Influence of Calendering and Coating Line Conditions on the Microstructure of Cathode Electrode in Lithium-Ion Batteries: Ultrasonic Testing Insights" has been accepted for publication in Journal of Power Sources.
Journal: Journal of Power Sources
Authors: Erdogan Guk, Mona Faraji Niri, Hamidreza Farhadi Tolie, Matthew Capener, Philip Bellchambers, and James Marco
View Publication