Mohammad Nadimi

Dr. Mohammad Nadimi received his PhD in Electrical Engineering from the University of Manitoba in October 2018, specializing in the development of novel laser systems with biological applications. Following his graduation, he served as a Sessional Instructor in Statistics at the University, where he shared his expertise in quantitative methods.

Dr. Nadimi then pursued postdoctoral research in the Department of Biosystems Engineering, focusing on quality monitoring of grains, oilseeds, legumes, and their derivatives using advanced optical techniques.

In September 2023, Dr. Nadimi expanded his scope to include big data analytics as a Senior Data Analyst at Wawanesa Insurance, where he applied his analytical skills to large-scale datasets to minimize risks and losses.

In April 2025, Dr. Nadimi returned to the Department of Biosystems Engineering at the University of Manitoba as an Assistant Professor. His current research integrates photonics techniques and large-scale data analytics into grain storage systems to improve crop quality assurance and storage.

An active contributor to the academic community, Dr. Nadimi has published over 40 peer-reviewed articles and more than 20 conference papers. He also serves as a Senior Editor for the Elsevier journal 'Measurement: Food', collaborating with the editorial team to advance knowledge in innovative measurement techniques for agri-food quality and safety.

Area

• Application of electromagnetic imaging and spectroscopy techniques for real-time quality monitoring of agri-food products.
• Development and application of advanced data analytics techniques, including machine learning and artificial intelligence, to optimize computational time and power in big data analysis within the agri-food domain.
• Microstructural analysis of raw and processed agri-foods to enhance understanding of food quality and safety.
• Applications of physical treatments, such as laser biostimulation, to improve the viability of crops.
• Applications of smart electronic sensing technologies for real-time crop quality monitoring.

Expertise

• Grain quality monitoring
• Photonics and spectroscopy
• Advanced imaging techniques
• Microstructural analysis
• Electronics and sensor technologies
• Advanced statistical data analysis
• Machine learning and deep learning

Research description

To develop industry-aligned, photonics-based technologies integrated with advanced
data analytics that enhance agri-food production and storage efficiency, elevate food
quality, reduce loss, and minimize environmental impacts.

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