معرفی
Dr. Saeed Samet is a Professor in the School of Computer Science at the University of Windsor. His research focuses on cybersecurity, privacy-preserving data mining, federated learning, blockchain technology, and healthcare informatics. He has led projects such as the privacy-preserving personal health record system (P3HR) and the decentralized electronic health records (DEHR) model leveraging blockchain. Samet actively engages in interdisciplinary collaborations, including developing frameworks like SEERa for community prediction and RNBFT for scalable Byzantine consensus. He advises students like Saghi Khani on topics such as social isolation detection algorithms. Notably, his work addresses adversarial attacks in machine learning and federated learning security. Samet has contributed to initiatives like the International Collegiate Programming Contest and the WE-Spark Health Institute grants.
Education
While formal academic credentials are not explicitly detailed in the text, Samet holds a professorship, indicating advanced qualifications in computer science or related fields. His extensive publications suggest doctoral-level expertise.
Research Interests
Samet’s research spans cybersecurity mechanisms, privacy-preserving methodologies (e.g., homomorphic encryption, federated learning), blockchain applications in healthcare, and machine learning robustness. Recent work emphasizes scalable consensus algorithms (RNBFT), adversarial defense in federated learning, and frameworks for decentralized data evaluation. His projects often integrate real-world challenges, such as combating fake news via generative AI and enhancing pandemic response through blockchain-based patient tracking.
Awards & Grants
Recipient of a WE-Spark Health Institute grant for innovative research in Windsor-Essex (2021). His work on social isolation algorithms (2020) and privacy-preserving statistical analysis (2019) underscores sustained funding support for impactful projects.
Advising & Grants
Advises Saghi Khani on machine learning algorithms for social isolation detection. Collaborates on grants involving interdisciplinary teams, such as the $287,000 WE-Spark Health Institute award. His research often bridges academia and industry, addressing practical challenges in healthcare and cybersecurity.
Labs & Teams
Leads initiatives in the School of Computer Science’s research groups, focusing on privacy-preserving systems and distributed learning. Collaborates with organizations like the WE-Spark Health Institute and the University of Windsor’s interdisciplinary teams on projects such as the DEHR blockchain model and federated learning frameworks.
