Ibtasam Wajid | Medicine | Best Researcher Award Published on 13/10/202513/10/2025 by Best Achievements Mr. Ibtasam Wajid | Medicine | Best Researcher Award Shenzhen University China | Pakistan Ibtasam Wajid is a dedicated researcher currently pursuing a PhD in Biomedical Engineering at Shenzhen University, China, with a strong academic foundation in Health Informatics (MS, COMSATS University Islamabad) and Biochemistry (BS, Islamia University of Bahawalpur). Working under the supervision of Prof. Dr. Nadeem Javaid at Comsens Lab, COMSATS University, Islamabad, Ibtasam’s research focuses on the integration of machine learning and deep learning techniques in healthcare, particularly for cardiovascular disease prediction and medical data analysis. His expertise encompasses data science, biomedical signal processing, and artificial intelligence applications in clinical decision-making. He has contributed to publications and collaborative projects, with an h-index of 2, 4 published documents, and over 35 citations on Google Scholar, reflecting his growing influence in computational health research. His technical proficiency includes Python, R-Studio, LaTeX, SPSS, and QGIS, along with hands-on laboratory skills in molecular biology and medical imaging. Beyond research, he has organized workshops and mentored undergraduate students in innovative data-driven healthcare projects. His commitment to scientific advancement and multidisciplinary collaboration positions him as a promising researcher contributing to the future of intelligent healthcare systems. Profile: Scopus | Google Scholar Featured Publications shaq, S., Afzal, S., Usmani, S. A., Wajid, I., Aziz, A., Tahir, R., Ahmad, A., & Ullah, A. (2025). Inflammatory cell death and leukemia treatment. Critical Reviews in Oncology/Hematology, 104909.
Ibtasam Wajid is a dedicated researcher currently pursuing a PhD in Biomedical Engineering at Shenzhen University, China, with a strong academic foundation in Health Informatics (MS, COMSATS University Islamabad) and Biochemistry (BS, Islamia University of Bahawalpur). Working under the supervision of Prof. Dr. Nadeem Javaid at Comsens Lab, COMSATS University, Islamabad, Ibtasam’s research focuses on the integration of machine learning and deep learning techniques in healthcare, particularly for cardiovascular disease prediction and medical data analysis. His expertise encompasses data science, biomedical signal processing, and artificial intelligence applications in clinical decision-making. He has contributed to publications and collaborative projects, with an h-index of 2, 4 published documents, and over 35 citations on Google Scholar, reflecting his growing influence in computational health research. His technical proficiency includes Python, R-Studio, LaTeX, SPSS, and QGIS, along with hands-on laboratory skills in molecular biology and medical imaging. Beyond research, he has organized workshops and mentored undergraduate students in innovative data-driven healthcare projects. His commitment to scientific advancement and multidisciplinary collaboration positions him as a promising researcher contributing to the future of intelligent healthcare systems.
Ibtasam Wajid is a dedicated researcher currently pursuing a PhD in Biomedical Engineering at Shenzhen University, China, with a strong academic foundation in Health Informatics (MS, COMSATS University Islamabad) and Biochemistry (BS, Islamia University of Bahawalpur). Working under the supervision of Prof. Dr. Nadeem Javaid at Comsens Lab, COMSATS University, Islamabad, Ibtasam’s research focuses on the integration of machine learning and deep learning techniques in healthcare, particularly for cardiovascular disease prediction and medical data analysis. His expertise encompasses data science, biomedical signal processing, and artificial intelligence applications in clinical decision-making. He has contributed to publications and collaborative projects, with an h-index of 2, 4 published documents, and over 35 citations on Google Scholar, reflecting his growing influence in computational health research. His technical proficiency includes Python, R-Studio, LaTeX, SPSS, and QGIS, along with hands-on laboratory skills in molecular biology and medical imaging. Beyond research, he has organized workshops and mentored undergraduate students in innovative data-driven healthcare projects. His commitment to scientific advancement and multidisciplinary collaboration positions him as a promising researcher contributing to the future of intelligent healthcare systems.