Muhammad Asif Munir | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Hawazin Elani | Machine Learning | Best Researcher Award

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Dr. Hawazin Elani | Machine Learning | Best Researcher Award

Harvard University | United States

Dr. Hawazin W. Elani, Ph.D., is an accomplished scholar and academic leader whose research integrates dentistry, epidemiology, and health policy to advance oral health equity through data-driven, interdisciplinary approaches. She serves as an Associate Professor in the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health and in the Department of Oral Health Policy and Epidemiology at the Harvard School of Dental Medicine, with additional affiliations at the Harvard Data Science Initiative and the Kempner Institute for the Study of Natural and Artificial Intelligence. Dr. Elani earned her Ph.D. in Dental Sciences with a concentration in Epidemiology and Population Health and an M.Sc. from McGill University, as well as an MMSc in Oral Biology and a Clinical Certificate in Prosthodontics from Harvard. Her research explores health disparities, oral health policy, and the application of artificial intelligence and machine learning in predicting oral health outcomes. She has authored over 30 peer-reviewed publications in high-impact journals such as Health Services Research, JAMA Network Open, and Journal of Dental Research, with her work cited widely for shaping discussions on healthcare access and reform. As principal investigator on multiple NIH and foundation-funded projects, including R01 and K-series grants, she has led innovative studies assessing the effects of Medicaid expansion and socioeconomic factors on dental care utilization. Recognized with Harvard’s Young Mentor Award and Distinguished Junior Faculty Award in 2024, Dr. Elani also contributes to national and international committees, including the NIH, the National Academies of Sciences, and the Medicaid Policy Research Advisory Group. Through her leadership, global collaborations, and dedication to mentoring, she continues to advance the intersection of artificial intelligence, population health, and oral health policy, driving forward equitable and sustainable improvements in healthcare delivery worldwide.

Profiles: Scopus | ORCID
Featured Publication

lani, H. W., Kawachi, I., & Sommers, B. D. (2020). Changes in emergency department dental visits after Medicaid expansion. Health Services Research, 55(1), 76–84.

Elani, H. W., Simon, L., Ticku, S., Bain, P. A., Barrow, J., & Riedy, C. A. (2018). Does providing dental services reduce overall health care costs? A systematic review of the literature. Journal of the American Dental Association (1939), 149(6), 430–438.e10.

Elani, H. W., Starr, J. R., Da Silva, J. D., & Gallucci, G. O. (2018). Trends in dental implant use in the U.S., 1999–2016, and projections to 2026. Journal of Dental Research, 97(13), 1424–1430.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., Cleber, S., & Bittner, N. (2019). Comparison of the color appearance of peri-implant soft tissue with natural gingiva using anodized pink-neck implants and pink abutments: A prospective clinical trial. The International Journal of Oral & Maxillofacial Implants, 34(1), 168–175.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., & Bittner, N. (2017). A prospective clinical trial to assess the optical efficacy of pink neck implants and pink abutments on soft tissue esthetics. Journal of Esthetic and Restorative Dentistry, 29(3), 213–219.

Ye Tao | Machine Learning | Best Researcher Award

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Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.