Nisar
Ahmed
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Biography
Nisar Ahmed is a Senior Researcher at the University of Turku, Finland. He holds a PhD in Artificial Intelligence and has over 15 years of experience in artificial intelligence, machine learning, computer vision, and biomedical data analysis across academia and industry. His research focuses on computational histopathology, multimodal AI, representation learning, and the integration of histopathology with spatial omics and clinical data for precision medicine.
His current work also explores quantum machine learning and quantum computing as emerging approaches for multimodal biomedical data integration. His research aims to develop robust, scalable, and interpretable computational methods for biomedical research and clinically relevant applications.
Teaching
My teaching experience includes data science, machine learning, deep learning, computer vision, information security, and cybersecurity at undergraduate and graduate levels.
My teaching activities include course design and delivery, student supervision, curriculum development, and project-based learning.
I am particularly interested in interdisciplinary teaching at the intersection of artificial intelligence, biomedical data science, medical imaging, and emerging computational methods, including quantum machine learning and quantum computing.
Research
My research focuses on artificial intelligence for computational histopathology and multimodal biomedical data analysis. I develop machine learning and deep learning methods for digital pathology, with particular interest in histopathological image representation learning, pathology foundation models, multimodal learning, and integration of histopathology with spatial omics, single-cell genomics, and clinical data.
My current research focuses on AI-driven approaches for cardiovascular precision medicine, including quantitative characterization of tissue remodeling, biomarker discovery, and prediction of clinically relevant outcomes. I am also exploring quantum machine learning and quantum computing as emerging computational paradigms for multimodal biomedical data integration and representation learning. I am particularly interested in robust, interpretable, and uncertainty-aware AI methods for heterogeneous biomedical data.