Faculty Staff, University of Qom, Iran
Dr. Amir Lakizadeh is an accomplished researcher and Assistant Professor at the University of Qom, specializing in machine learning and deep learning. As the head of the AI-driven Pharma and Medicine (AIPM) lab, his work spans multimedia, medical diagnostics, and pharmaceutics. With over a decade of academic and industry experience, Dr. Lakizadeh is known for leading cutting-edge projects, mentoring future researchers, and advancing AI applications in drug discovery and healthcare. ๐๐
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๐ Education
Dr. Lakizadeh began his academic journey at the University of Tehran, where he earned his Bachelor’s (2001โ2005) and Master’s (2005โ2007) degrees in Computer Science, focusing on bioinformatics and machine learning. He later completed his Ph.D. in Computer Engineering at Tarbiat Modares University (2011โ2016), with research centered on machine learning, computational systems biology, and bioinformatics. ๐๐
๐ผ Experience
Since 2008, Dr. Lakizadeh has served as an Assistant Professor at the University of Qom, contributing significantly to research in AI, multimedia, and medical imaging. He has mentored over 70 postgraduate students and spearheaded 30+ research publications. As the current Head of the Computer Engineering and IT Department, he oversees 1000+ students, academic programs, and research initiatives. ๐๏ธ๐งโ๐ซ
๐ฌ Research Interest
Dr. Lakizadehโs research interests include deep learning, computer vision, digital pathology, drug repurposing, anticancer peptide prediction, and Alzheimerโs detection. He is passionate about creating AI-driven solutions for real-world medical and pharmaceutical challenges. His interdisciplinary work integrates data from biology, medicine, and computer science to deliver impactful innovation. ๐ก๐ง ๐
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Awards
While specific honors aren’t listed, Dr. Lakizadehโs leadership of high-impact AI projects, numerous peer-reviewed publications, and influential role in academia highlight his excellence and recognition in the fields of computer science and bioinformatics. He is a highly respected figure in interdisciplinary AI research. ๐๐
๐ Publications
PU-GNN: A Positive-Unlabeled Learning Method for Polypharmacy Side-Effects Detection, International Journal of Intelligent Systems, 2024.
Cited by: Multiple future studies in drug safety prediction.
Drug Repurposing Using Hypergraph Embedding, Journal of Computational Biology, 2024.
Cited by: Studies in computational drug discovery.
GADNN: Graph Attention-based Drug Association Method, Informatics in Medicine Unlocked, 2024.
Cited by: Research in bioinformatics and AI.
Detection of Polypharmacy Side Effects via CNN, Molecular Diversity, 2022.
Cited by: Pharmacological safety frameworks.
Drug-Drug Interaction via GNN, Scientific Reports, 2022.
Cited by: Advanced machine learning in pharma.
ASDvit: Autism Classification using Vision Transformer, Intelligence-Based Medicine, 2025.
Cited by: Pediatric AI diagnostic tools.
Early Diagnosis of Alzheimerโs Disease via ResNet50 and FSBi-LSTM, Informatica, 2025.
Cited by: Neuroscience AI literature.
Face Hallucination via GAN, Journal of Electrical Systems, 2024.
Cited by: Vision-based AI modeling.
Power-Efficient IoT Optimization, Journal of Electrical Systems, 2024.
Cited by: Industrial Internet of Things (IIoT) research.
๐ Conclusion
Dr. Amir Lakizadeh demonstrates all the hallmark qualities of a Best Researcher Award recipientโinnovation, productivity, leadership, and real-world impact. His work seamlessly bridges foundational AI with urgent healthcare and societal applications. With a few strategic steps toward global engagement and open dissemination, his already impressive profile could set a benchmark for excellence.