Dr. Hassanzadeh is a tenure-track Assistant Professor with a joint appointment in the Departments of Electrical and Computer Engineering and Mathematics and Computer Science at Lawrence Technological University. His core research area is Artificial Intelligence, with a focus on machine learning, signal processing, image processing, and the development of mathematical approaches in these fields. He applies these methods to various domains, including biomedical and bioinformatics, autonomous vehicles, renewable energy, and time series analysis. He has taught more than 100 courses across different disciplines and has received four teaching excellence awards. Before Joining Lawrence Tech, Dr. Hassanzadeh was an Adjunct Assistant Professor at University of Windsor, Canada, Assistant Professor, (Limited-Duties appointment) at Western University, Canada, Visiting Research Fellow (Assistant Professor )at Institut des Hautes Études Scientifiques, (IHES), France, and a Postdoctoral Fellow at the University of New Brunswick, Canada.

For more information, please see his page at: https://sites.google.com/view/dr-mohammad-hassanzadeh/home

» Education

PhD, Western University, Canada, 2006-2010.

Masters, Western University, Canada, 2005-2006

Bachelor, University of Tehran, Iran, 1994-2000.

» Research Interests

• Artificial Intelligence
• Machine Learning and Artificial Intelligence
• Signal processing
• Image Processing
• Biomedical and Bioinformatics
• Mathematical Transformations: Fourier transform, Laplace transform, cosine and sine transforms, Wavelet transform
• Time Series Analysis
• Quantum Computing, Quantum image processing, Quantum Fourier Transform.
• Numerical Method
• Numerical Partial and Ordinary Differential Equations

» Courses Taught at LTU

• EEE 5513 Software Development for AI
• EEE 5253 Deep Learning for Engineers

Dr. Hassanzadeh has taught more than 100 courses in different subjects at other universities in Canada.

» Peer-Reviewed Publications

• Love Fadia, Vatsal Shah Mohammad Hassanzadeh, Jonathan Wu, and Majid Ahmadi, “A Novel Multi-Modal Dual Pathway Network with Hierarchical Channel-Spatial Attention and Adaptive Feature Fusion for Viral Genomic Variant Classification”, Journal of Network modelling and analysis in health informatics and bioinformatics, Springer- Nature, 2025.​
• Love Fadia, Vatsal Shah Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu, “Multifaceted Computational Framework for COVID-19 Variant Classification using Advanced Machine Learning, Signal Processing, and High-Dimensional Feature Reduction Techniques”, International Journal of Computer Applications (IJCA), Volume 186 – Number 70, 2025
• Vatsal Shah, Love Fadia, Mohammad Hassanzadeh, Majid Ahmadi, Jonathan Wu and George Pappas, “Dynamically Weighted Pairwise Cross-Attention Driven Feature Fusion in Hybrid Convolutional Neural Networks for Classification of COVID 19 Variants”, Journal of Computer and Information Science 18 (1), 111-167, 2025.​
• Vatsal Shah, Love  Fadia, Mohammad Hassanzadeh, Majid Ahmadi and Jonathan Wu, "Lightweight Vision Transformer for Efficient Influenza Virus Subtype Classification via Genomic Image Processing," 2025 IEEE Latin Conference on IoT (LCIoT), Fortaleza, Brazil, 2025, pp. 157-160,
• Love Fadia, Vatsal Shah, Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu, “Graph Attention Network and Graph Convolutional Network for Classification of Dengue Virus Variants,” 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, pp. 13-19, 2025.​
• Vatsal Shah, Love Fadia, Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu, "BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classification of SARS-CoV-2 and Influenza Virus Variants via Genomic Image Processing," 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, pp. 243-248, 2025.​
• Seyed Ali Baniaghil, Mohammad Hassanzadeh, Majid Ahmadi, “Optimized Classification and Anomaly Detection for Enhanced Monitoring of Combined Cycle Power Plants”, Accepted in 68th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Michigan, US, 2025.
• Vatsal Shah, Mohammad Hassanzadeh, and Majid Ahmadi, “Integrating Graph Signal Processing with Graph Convolutional Networks for N- and O-Glycosylation Site Prediction”, Accepted in 68th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), (joint with Majid Ahmadi and Vatsal Shah), Michigan, US, 2025.​
• Seyed Mojtaba Naghibzadeh, Mohammad Hassanzadeh, Majid Ahmadi, George P. Pappas, "Gold Price Trend Prediction from Candlestick Chart Images Using Multi-Time Frame Analysis and Machine Learning", accepted in 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), 2025.
• Shan Ehsan, Mohammad Hassanzadeh, Majid Ahmadi, Ardian Kelmendi, Nabih Jaber, George P. Pappas," Battery State of Charge Estimation by Machine Learning", accepted in 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS) 2025.
• Almiqdad Elzein, Mohammad Hassanzadeh, Arezoo Emadi, "Combining Image Transformations to Solve Unseen Time Series Classification Problems", accepted in 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS),  2025.
• Almiqdad Elzein, Mohammad Hassanzadeh, Arezoo Emadi, “Learning Hyper-Parameters of Image Transformations for Time Series Classification, 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), Sydney, Australia, pp. 377-382, 2024.
• Love Fadia, Vatsal Shah Mohammad Hassanzadeh, Jonathan Wu, and Majid Ahmadi, “Genomic Transformers: Innovative Approaches to Hepatitis Virus Subtyping”, Accepted in 2025, IEEE-13th International Conference on Bioinformatics and computational Biology (ICBCB 2025), Seoul, South Korea, 2024.​
• Seyed Ali Baniaghil,  Vatsal Shah, Love Fadia, Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu, “Advanced Forecasting of CPP Output Power Using Regression and Neural Network Models”, Accepted in 11th Annual Conf. on Computational Science & Computational Intelligence (CSCI’24), Las Vegas, USA, 2024.
• Love Fadia, Vatsal Shah Mohammad Hassanzadeh, Jonathan Wu, and Majid Ahmadi, “An Efficient Method for Classification of different types Of Hepatitis Virus using Extended Genomic Signal Processing and Machine Learning, Proceedings of the 1st World Congress 2024 Detroit, Detroit, United States, Publisher: IEOM Society International, 2024.​
• Vatsal Shah, Love Fadia, Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu,   “Redefined Classification of Hepatitis Variants through Arima, Signal Processing and Machine Learning” , Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics, ACM, pp. 136-143, 2024.​
• Shaghayegh, Khalighiyan , Mohammad Hassanzadeh, Esam Abdel-Raheem, “Brain Tumor Classification through Transfer Learning Models”, Accepted in 11th Annual Conf. on Computational Science & Computational Intelligence (CSCI’24), Las Vegas, USA, 2024.
• Vatsal Shah, Love Fadia, Mohammad Hassanzadeh, Majid Ahmadi, and Jonathan Wu,  “DENG-Transformer: A Transformer Based Approach for Classification of Different Subtypes of Dengue Virus”, Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics, ACM, pp. 98-105, 2024.
• Mohammad Hassanzadeh, Behnam Sharrava, "Linear Version of Parseval’s Theorem" , Journal of IEEE Access, vol. 10, pp. 27230-27241, (2022).
• Mohammad Hassanzadeh, Serkan Sutlu, "Matched pairs of m-invertible Hopf quasigroups", Journal of Quasigroups and Related Systems 28 (2020), 101 – 138.
• Mohammad Hassanzadeh, Lagrange theorem for Hom-groups , Rocky Mountain Journal of Math. 49 (2019), no. 3, 773–787.
• Mohammad Hassanzadeh, "Hom-Groups, Representations and Homological Algebra" , Journal of Colloq. Math. 158 (2019), no. 1, 21–38.
• Mohammad Hassanzadeh, Masoud Khalkhali and Ilya Shapiro, Monoidal categories, 2- Traces, and cyclic cohomology, Canadian Mathematical Bulletin, 62 (2019), no. 2, 293–312.
• Mohammad Hassanzadeh, On representation theory of total (co)integrals, Journal of Algebra and Its Applications, vol 15, no. 10 (2016), 19 pages.
• Mohammad Hassanzadeh, Ilya Shapiro and Serkan Sutlu, Cyclic homology for Hom- associative algebra, (joint with), Journal of Geometry and Physics, Volume 98, December (2015), Pages 40–56.
• Mohammad Hassanzadeh, Masoud Khalkhali, Cup coproducts in Hopf cyclic cohomology, Journal of Homotopy and Related Structures, (2015), Volume 10, Issue 3, Pages 347- 373.
• Mohammad Hassanzadeh, On cyclic cohomology of x-Hopf algebras, Journal of K- Theory, Volume 13, Issue 01, (2014), pages 147-170, Cambridge University Press.
• Mohammad Hassanzadeh, New coefficients for Hopf cyclic cohomology, Journal of Communications in Algebra, Volume 42, Issue 12, December 2014, pages 5287-5298.
• Mohammad Hassanzadeh, Dan Kucerovsky and Bahram Rangipour, Generalized coefficients for Hopf cyclic cohomology,  Journal of SIGMA 10 (2014), 093, 16 pages.
• Mohammad Hassanzadeh, Bahram Rangipor, Equivariant Hopf Galois extension and Hopf cyclic homology,  Journal of Noncommutative . Geometry. 7 (2013), no1, pages 105-133.
• Mohammad hassanzadeh, Hopf Galois (co)extensions in noncommutative geometry, New Zealand Journal of Mathematics, Vol.  42, (2012), Pages 195-215.
• Mohammad Hassanzadeh, Operations on Hopf cyclic cohomology, Thesis (Ph.D.)–The University of Western Ontario (Canada). (2010). 114 pp. ISBN: 978-0494-73358-5, ProQuest LLC.

» Research Grants

Mitacs Research Grant: $120,000 (Canada)
Project Title: “Physics-informed Neural Networks for Time-Dependent Transport Equations”. Principal Investigator: Dr. Mohammad Hassanzadeh

Mitacs Research Grant: $120,000 (Canada)
Project Title: “Building Trust in AI-Generated Content: Innovative Strategies for Quality and Integrity Verification” Principal Investigator: Dr. Mohamad Hassanzadeh

Mitacs Research Grant: $45,000 (Canada)
Project Title: "Design and Evaluation of Techniques for Enhancing the Utilization of Pre-Trained Language Models (GPT-3) in Sales and Marketing through Prompt”, May 2023-August 2024, Principal Investigator: Dr. Mohammad Hassanzadeh