Zhaozhen Jiang | Computer Science | Best Research Article Award

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Dr. Zhaozhen Jiang | Computer Science | Best Research Article Award

Assistant Researcher | Naval Submarine Academy | China

Dr. Zhaozhen Jiang is a distinguished researcher at the Navy Submarine Academy in Qingdao, China, specializing in intelligent systems, maritime navigation, and dynamic target search. His research focuses on the development of advanced path-planning algorithms and neural network–based optimization techniques for complex maritime environments. He has published extensively and collaborated widely with researchers across multiple disciplines, reflecting a strong commitment to interdisciplinary innovation. His recent work on GBNN-based maritime dynamic target search demonstrates a focus on enhancing operational decision-making and situational awareness in challenging naval contexts. Through his research, he aims to advance autonomous maritime systems and contribute to safer, more efficient naval operations, while fostering technological progress with meaningful societal impact.

Citation Metrics (Scopus)

40
30
20
10
0

Citations

37

Documents

15

h-index

4

Citations

Documents

h-index

View Scopus Profile

Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

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Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Mona Almutairi | Artificial Intelligence | Best Researcher Award

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Ms. Mona Almutairi | Artificial Intelligence | Best Researcher Award

Shaqra University | Saudi Arabia

Ms. Mona Almutairi is a highly motivated computer science graduate with a strong academic foundation and practical experience in system engineering and data management. She completed her Bachelor’s degree in Computer Science from Shaqra University in 2019 with an impressive GPA of 4.19 out of 5, demonstrating consistent academic excellence. Her professional experience includes serving as a System Engineer at the Ministry of Economy and Planning, where she contributed to optimizing systems operations and enhancing digital workflows, as well as volunteering as a Data Entry Assistant at the Ministry of Health, where she efficiently managed and organized large datasets with accuracy and confidentiality. She further enriched her technical expertise through professional courses in Software Engineering from the Saudi Digital Academy and Web Development from the Ministry of Communications and Information Technology, equipping her with up-to-date industry knowledge and coding proficiency. Her research interests lie in software development, data analysis, and emerging technologies that integrate innovation with societal advancement. Ms. Almutairi’s research skills include proficiency in data analysis tools, problem-solving, and the ability to apply algorithmic thinking to real-world challenges. She is also adept at using Microsoft Office and has strong communication, teamwork, and adaptability skills, making her a collaborative and reliable professional. Her dedication to learning and excellence has been recognized through various academic and professional achievements, reflecting her commitment to continuous improvement. Overall, Ms. Almutairi is a forward-thinking computer scientist who combines technical knowledge, analytical capabilities, and professional experience to drive innovation in the field of information technology.

Profiles: Google Scholar | ORCID

Featured Publications

Almutairi, M., & Dardouri, S. (2025). Intelligent hybrid modeling for heart disease prediction. Information, 16(10), 869. Citations: 1

Mohammed Alenazi | Computer Engineering | Best Researcher Award

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Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

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Mr. Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

Biomedical Engineering at Pukyong National University, South Korea

Nhan T. Nguyen, a Master’s student at Pukyong National University, is a promising early-career researcher specializing in biomedical engineering, computer vision, and artificial intelligence. His research focuses on non-destructive testing, low-level vision, and automated inspection systems using advanced AI techniques such as GANs, transformers, and diffusion models. Nhan has contributed to multiple peer-reviewed publications in prestigious journals like IEEE Transactions and MDPI Applied Sciences, with additional manuscripts under review and in preparation. His work demonstrates strong practical relevance, with AI models deployed in industrial applications including semiconductor inspection, robotic automation, and smart city infrastructure. He has received several academic honors and awards, reflecting his dedication and innovation. Despite being at the master’s level, he serves as a peer reviewer for international journals and conferences, highlighting his scholarly maturity. With interdisciplinary expertise, a growing publication record, and impactful real-world applications, Nhan is highly suitable for the Best Researcher Award in the early-career category.

Professional Profile 

Education🎓

Nhan T. Nguyen has built a strong educational foundation in engineering and artificial intelligence across reputable institutions in Vietnam and South Korea. He earned his Bachelor of Science degree in Information Technology Engineering from Ho Chi Minh University of Technology, where he was actively involved in undergraduate research and received multiple academic awards and scholarships. During his undergraduate years, he developed projects integrating AI with OCR and chatbot systems. Currently, he is pursuing a Master’s degree in the Industry 4.0 Convergence Bionics Engineering program at Pukyong National University in South Korea under the supervision of Professor Junghwan Oh. His graduate research focuses on non-destructive testing, specifically in scanning acoustic microscopy systems, and applying AI to industrial inspection tasks. Through this academic journey, Nhan has gained in-depth knowledge and hands-on experience in computer vision, machine learning, and robotics, forming a strong educational background that supports his innovative contributions to research and industry applications.

Professional Experience📝

Nhan T. Nguyen has gained diverse professional experience in the fields of artificial intelligence, computer vision, and industrial automation. He served as an AI Engineer at the Artificial Intelligence Center of FPT Software in Vietnam, where he worked on optimizing dehumidification processes for the Chicago Art Museum and enhancing defect detection in steel production using machine learning algorithms. His role involved data analysis, predictive modeling, and AI deployment in real-world environments. He also contributed to a deep learning-based search engine enhancement project for a pharmaceutical retail company. In addition, at FPT Information System’s Smart City Department, he developed camera-based systems for sidewalk encroachment detection, which were integrated into Ho Chi Minh City’s traffic management system. Currently, as a Graduate Research Assistant at Pukyong National University, he is involved in automating weld inspection systems and developing AI models for defect detection in scanning acoustic microscopy. His experience bridges academic research and practical industrial implementation.

Research Interest🔎

Nhan T. Nguyen’s research interests lie at the intersection of artificial intelligence, computer vision, and industrial automation, with a particular focus on low-level vision tasks and non-destructive testing. He is passionate about developing advanced AI models such as Generative Adversarial Networks (GANs), transformers, and diffusion models for applications in image restoration, super-resolution, and defect detection. His work emphasizes enhancing the performance and reliability of automated inspection systems used in semiconductor manufacturing, steel production, and other industrial settings. Nhan is also interested in integrating AI with robotic systems, using tools like 3D scanners, lasers, and cameras to automate surface inspection processes. Additionally, he explores exploratory data analysis across multiple domains, including medical, environmental, and industrial datasets. His goal is to bridge the gap between theoretical research and practical implementation, contributing to more intelligent, accurate, and efficient inspection and monitoring systems in smart manufacturing and biomedical engineering environments.

Award and Honor🏆

Nhan T. Nguyen has received numerous awards and honors in recognition of his academic excellence, innovative research, and technical achievements. He was awarded a scholarship by Pukyong National University in 2023 for his outstanding performance as a graduate student. During his undergraduate studies at Ho Chi Minh University of Technology, he received the prestigious KMS Technology Scholarship in 2022, as well as the City Now Company Scholarship and the Impressive Award in the HUTECT Start-up Wing competition in 2021. He also earned a Consolation Prize in the university’s AI Hackathon in 2020 and was recognized for his undergraduate research contributions. Nhan consistently demonstrated academic excellence, earning the Outstanding Undergraduate Student Scholarship in 2018. These honors reflect his dedication to research, creativity in problem-solving, and strong commitment to applying AI technologies to real-world challenges. His consistent recognition throughout his academic career underscores his potential as a leading researcher in his field.

Research Skill🔬

Nhan T. Nguyen possesses a robust set of research skills that span artificial intelligence, computer vision, and industrial automation. He is highly proficient in data processing, exploratory data analysis, and model development using Python and advanced machine learning frameworks. His expertise includes designing and implementing deep learning models, particularly using Generative Adversarial Networks (GANs), transformers, and diffusion models for image super-resolution, denoising, and defect detection. Nhan is skilled in integrating AI models with hardware systems such as robotic arms, 3D scanners, lasers, and industrial cameras to build intelligent inspection systems. He has hands-on experience with non-destructive testing methods, particularly scanning acoustic microscopy, and is adept at handling real-world industrial datasets. Additionally, Nhan is capable of deploying AI solutions into operational environments, enhancing automation processes in sectors like semiconductor manufacturing, smart cities, and healthcare. His ability to bridge theoretical models with practical applications showcases his strong technical and problem-solving capabilities as a researcher.

Conclusion💡

Nhan T. Nguyen demonstrates exceptional promise and proven capability in applied AI and biomedical inspection research, with practical impact, strong publications, and academic service. For a master’s-level researcher, this profile is outstanding.

Publications Top Noted✍️

📄 1. GAN-Based Super-Resolution in Linear R-SAM Imaging for Enhanced Non-Destructive Semiconductor Measurement

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Le Hai Tran, Vu Hoang Minh Doan, Van Bang Nguyen, Wonjo Lee, Sudip Mondal, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.3390/app15126780

  • Source: Applied Sciences, Published on June 17, 2025

📄 2. Transformer-Based Super-Resolution Scanning Acoustic Imaging for Industrial Inspection

  • Authors: Trong Nhan Nguyen, Vu Hoang Minh Doan, Tan Hung Vo, Jaeyeop Choi, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.1109/icit63637.2025.10965207

  • Source: 2025 IEEE International Conference on Industrial Technology (ICIT), Published on March 26, 2025

📄 3. Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Sudip Mondal, Junghwan Oh

  • Year: 2024

  • Citation (DOI): 10.1109/TSM.2024.3444850

  • Source: IEEE Transactions on Semiconductor Manufacturing, Published in November 2024

Afeez Soladoye | Machine learning | Young Scientist Award

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Mr. AfeezSoladoye | Machine learning | Young Scientist Award

Lecturer at Federal university Oye-Ekiti, Nigeria

Soladoye Afeez Adekunle is a promising young scholar in Computer Engineering, currently pursuing his Ph.D. at the Federal University Oye-Ekiti. With a Master’s degree earned with distinction, he has demonstrated strong academic and research capabilities. His work spans machine learning, artificial intelligence, and applied computing, including the development of medical prediction systems and fake news detection using deep learning. In addition to his teaching responsibilities at undergraduate and postgraduate levels, he actively contributes as a peer reviewer for reputable journals such as BMJ Open and serves as a technical editor. His involvement in academic committees and university-level projects reflects his leadership and dedication to institutional development. While his practical projects are impactful, the inclusion of more peer-reviewed publications and measurable research outcomes would further enhance his profile. Overall, his commitment to innovation, education, and research makes him a suitable and competitive candidate for the Young Scientist Award.

Professional Profile

Education🎓

Soladoye Afeez Adekunle has a solid educational background in Computer Engineering, reflecting his dedication to academic excellence and continuous professional development. He is currently pursuing a Ph.D. in Computer Engineering at the Federal University Oye-Ekiti, Nigeria, with a research focus on advanced computing and intelligent systems. He previously earned a Master of Engineering (M.Eng) in Computer Engineering from the same university, graduating with distinction in 2023. His undergraduate studies were completed at Ladoke Akintola University of Technology, Ogbomosho, where he obtained a Bachelor of Technology (B.Tech) degree in Computer Engineering in 2016. His foundational education includes a Senior School Leaving Certificate from Foundation Model College, Ikirun, in 2009, and a Primary School Leaving Certificate from Al-hilal Nursery and Primary School, Ikirun, in 2003. His academic journey reflects a consistent commitment to learning, skill acquisition, and growth in the field of computer science and engineering, preparing him for a successful career in research and education.

Professional Experience📝

Soladoye Afeez Adekunle has amassed valuable professional experience across academia, research, and industry. He currently serves as a Lecturer II in the Department of Computer Engineering at the Federal University Oye-Ekiti, where he teaches both undergraduate and postgraduate courses, supervises student projects, and mentors young researchers. In addition to his teaching role, he is the Assistant Examination Officer and Level Advisor, playing a vital role in exam coordination and academic advising. He also contributes as a Technical Editor for the FUOYE Journal of Engineering and Technology and reviews scholarly articles for esteemed journals like BMJ Open and the Nigerian Journal of Technological Development. As a freelance Machine Learning Engineer, he has developed predictive systems for medical diagnosis and fake news detection, showcasing his ability to apply research in practical contexts. His previous roles include network engineering trainee and peer tutor, reflecting a versatile and well-rounded professional path in computer science and engineering.

Research Interest🔎

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Award and Honor🏆

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Research Skill🔬

Soladoye Afeez Adekunle possesses a diverse and practical set of research skills that align with cutting-edge developments in computer engineering and artificial intelligence. His expertise includes data analysis, machine learning model development, deep learning, and natural language processing. He has applied these skills in various impactful projects such as medical prediction systems for cancer and stroke, fake news detection, and object measurement using computer vision techniques. Adept at data preprocessing, model training, performance evaluation, and algorithm optimization, he ensures high-quality and accurate research outcomes. He is also skilled in using tools and frameworks such as Python, TensorFlow, Keras, and MATLAB for simulation and modeling. His experience in peer reviewing academic journals and formatting manuscripts further demonstrates his understanding of scientific writing and research ethics. Soladoye’s ability to merge academic research with practical application, along with his commitment to innovation, positions him as a capable and forward-thinking researcher in the technology domain.

Conclusion💡

Soladoye, Afeez Adekunle presents a strong case for the Young Scientist Award, especially in the areas of emerging technologies, machine learning, and applied computing. His academic excellence, teaching versatility, peer-review contributions, and practical ML project development demonstrate his passion and potential.

Publications Top Noted✍️

  • Title: IMPACT OF SOCIAL MEDIA ON POLICE BRUTALITY AWARENESS IN NIGERIA

    • Authors: OJOA, SOLADOYE Afeez A.

    • Year: 2020

    • Citations: 24

  • Title: Detection of Cervical Cancer Using Deep Transfer Learning

    • Authors: B.A. Omodunbi, A.A. Soladoye, A.O. Esan, N.S. Okomba, T.G.O.O.M. Ojelabi

    • Year: 2024

    • Citations: 4*

  • Title: Optimizing Stroke Prediction Using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

    • Authors: A.A. Soladoye, D.B. Olawade, I.A. Adeyanju, O.M. Akpa, N. Aderinto, et al.

    • Year: 2025

    • Citations: 2

  • Title: E-learning: Significance on Federal Unity Schools Students’ in Nigeria Amidst COVID-19 Lockdown

    • Authors: A.A. Soladoye

    • Year: 2020

    • Citations: 2

  • Title: Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

    • Authors: B.A. Omodunbi, A.A. Soladoye, N.S. Okomba, M.O. Ayinla, C.S. Odeyemi

    • Year: [Year not specified]

    • Citations: 2*

  • Title: Smart Hospitality: Leveraging Technological Advances to Enhance Customer Satisfaction

    • Authors: O.O. Osadare, O.N. Akande, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Internet of Things (IoT) Based Remote Surveillance Camera for Supervision of Examinations

    • Authors: C. Segun Odeyemi, B.A. Omodunbi, O.M. Olaniyan, A.A. Soladoye

    • Year: 2024

    • Citations: 1

  • Title: Prediction of Customer Satisfaction in Airline Hospitality Services for Improved Service Delivery Using Support Vector Machine

    • Authors: A.A. Sobowale, O.O. Osadare, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Development of an Interactive Android-Based Ayo-Olopon Game

    • Authors: E.Y. Bolaji Abigail Omodunbi, Afeez Adekunle Soladoye, Opeyemi Asaolu

    • Year: 2023

    • Citations: 1

Hafiz Khan | Machine Learning | Best Researcher Award

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Prof. Dr. Hafiz Khan | Machine Learning | Best Researcher Award

Professor at Texas Tech University Health Sciences Center, United States

Dr. Hafiz M. R. Khan is a Full Professor of Biostatistics at Texas Tech University Health Sciences Center, with an extensive academic and research background. He holds a Ph.D. in Statistics from the University of Western Ontario and has postdoctoral training in Bioinformatics. His career spans multiple institutions, including Florida International University and the University of Medicine & Dentistry of New Jersey. Dr. Khan has held leadership roles such as Associate Chair and Director of Outcome Measures, contributing significantly to academic committees and research initiatives. He has published extensively in peer-reviewed journals, focusing on biostatistics, public health, and cognitive impairment research. His strengths for the Best Researcher Award include a strong publication record, leadership in academia, and interdisciplinary collaboration. Areas for improvement may include further engagement in international research projects. Overall, his contributions to biostatistics and public health research make him a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Dr. Hafiz M. R. Khan has a strong educational background in statistics and biostatistics. He earned his Ph.D. in Statistics from the University of Western Ontario, Canada, where he specialized in statistical methodologies and their applications in health sciences. To further enhance his expertise, he completed postdoctoral training in Bioinformatics, gaining advanced knowledge in computational biology and data analysis. His academic journey also includes a Master’s and Bachelor’s degree in Statistics, which provided him with a solid foundation in quantitative analysis and research methods. Throughout his education, Dr. Khan focused on interdisciplinary applications of statistics, particularly in public health, epidemiology, and biomedical sciences. His strong academic credentials have enabled him to contribute significantly to research, teaching, and mentoring students in biostatistics and public health. His education has played a pivotal role in shaping his career, allowing him to bridge the gap between statistical theory and real-world health applications.

Professional Experience

Dr. Hafiz M. R. Khan has an extensive professional background in statistics, biostatistics, and public health research. He has held various academic and research positions, contributing significantly to statistical methodologies in biomedical and epidemiological studies. As a professor and researcher, he has taught biostatistics, data analysis, and public health courses at reputable institutions, mentoring numerous students and professionals. His expertise extends to consulting for healthcare organizations and research institutions, where he applies statistical models to solve complex health-related problems. Dr. Khan has also collaborated on interdisciplinary projects involving bioinformatics, machine learning, and predictive analytics in healthcare. His professional journey includes publishing high-impact research papers, serving as a peer reviewer for scientific journals, and participating in international conferences. His work has been instrumental in advancing statistical applications in medicine and public health, bridging the gap between theoretical research and practical implementation in real-world health challenges.

Research Interest

Dr. Hafiz M. R. Khan’s research interests lie at the intersection of biostatistics, epidemiology, and public health, with a strong focus on statistical modeling, predictive analytics, and machine learning applications in healthcare. He is particularly interested in developing advanced statistical methodologies to analyze complex biomedical data, improve disease prediction models, and enhance public health decision-making. His work explores the integration of statistical techniques with bioinformatics to study genetic influences on diseases and health outcomes. Additionally, he investigates the application of artificial intelligence in medical research, aiming to optimize diagnostic accuracy and treatment effectiveness. Dr. Khan is also passionate about global health issues, including infectious disease surveillance, health disparities, and aging populations. Through interdisciplinary collaborations, he strives to bridge the gap between statistical theory and real-world healthcare applications, contributing to innovative solutions that enhance patient care, policy-making, and public health interventions worldwide.

Award and Honor

Dr. Hafiz M. R. Khan has received numerous awards and honors in recognition of his outstanding contributions to biostatistics, public health, and epidemiology. He has been honored with prestigious research grants and fellowships from esteemed institutions, highlighting his excellence in statistical modeling and healthcare analytics. His groundbreaking work has earned him accolades such as the Best Researcher Award and Excellence in Public Health Research recognition. Dr. Khan has been invited as a keynote speaker at international conferences and has received distinguished scholar awards for his impactful publications. His dedication to academic excellence has also been acknowledged through teaching awards, mentoring recognitions, and leadership roles in professional organizations. Additionally, he has been recognized for his contributions to global health initiatives, demonstrating his commitment to improving healthcare outcomes. These awards and honors underscore his influence in the field and his continuous efforts to advance research, education, and policy in health sciences.

Research Skill

Dr. Hafiz M. R. Khan possesses exceptional research skills in biostatistics, public health, and epidemiology, enabling him to conduct advanced statistical analyses and develop innovative models for healthcare studies. His expertise includes data analysis, predictive modeling, machine learning applications in health research, and designing population-based studies. He has a strong command of statistical software such as R, SPSS, SAS, and STATA, which he utilizes to interpret complex datasets effectively. Dr. Khan excels in systematic reviews, meta-analysis, and quantitative research methodologies, ensuring rigorous scientific inquiry and evidence-based conclusions. His ability to synthesize large datasets and extract meaningful insights has contributed significantly to policy recommendations and healthcare improvements. Additionally, his collaborative approach to interdisciplinary research allows him to work seamlessly with experts from diverse fields. His critical thinking, problem-solving abilities, and meticulous research design skills make him a valuable contributor to advancing public health, epidemiology, and statistical sciences.

Conclusion

Dr. Hafiz M. R. Khan is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to academia, research, and public health. His leadership roles, mentoring, and commitment to advancing Biostatistics make him a strong contender. However, enhancing visibility of research impact, citations, international collaborations, and applied innovations could further strengthen his application.

Publications Top Noted

  • Title: Metabolic syndrome in aboriginal Canadians: prevalence and genetic associations
    Authors: RL Pollex, AJG Hanley, B Zinman, SB Harris, HMR Khan, RA Hegele
    Year: 2006
    Citations: 145

  • Title: Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions
    Authors: K Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, …
    Year: 2005
    Citations: 142

  • Title: Genetic Variation in PPARG Encoding Peroxisome Proliferator-Activated Receptor γ Associated With Carotid Atherosclerosis
    Authors: KZ Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, B Zinman, …
    Year: 2004
    Citations: 123

  • Title: Guillain–Barré syndrome after Gardasil vaccination: data from vaccine adverse event reporting system 2006–2009
    Authors: N Souayah, PA Michas-Martin, A Nasar, N Krivitskaya, HA Yacoub, …
    Year: 2011
    Citations: 120

  • Title: Type 2 diabetes and its correlates among adults in Bangladesh: a population-based study
    Authors: MAB Chowdhury, MJ Uddin, HMR Khan, MR Haque
    Year: 2015
    Citations: 110

  • Title: Physical therapists’ attitudes, knowledge, and practice approaches regarding people who are obese
    Authors: S Sack, DR Radler, KK Mairella, R Touger-Decker, H Khan
    Year: 2009
    Citations: 78

  • Title: Trends in outcomes and hospitalization costs for traumatic brain injury in adult patients in the United States
    Authors: K Farhad, HMR Khan, AB Ji, HA Yacoub, AI Qureshi, N Souayah
    Year: 2013
    Citations: 56

  • Title: Predictive inference from a two-parameter Rayleigh life model given a doubly censored sample
    Authors: HMR Khan, SB Provost, A Singh
    Year: 2010
    Citations: 49

  • Title: Optimizing RNA extraction yield from whole blood for microarray gene expression analysis
    Authors: J Wang, JF Robinson, HMR Khan, DE Carter, J McKinney, BA Miskie, …
    Year: 2004
    Citations: 48

  • Title: Secondhand smoke exposure reduction intervention in Chinese households of young children: a randomized controlled trial
    Authors: AS Abdullah, F Hua, H Khan, X Xia, Q Bing, K Tarang, JP Winickoff
    Year: 2015
    Citations: 45

  • Title: Statistical machine learning approaches to liver disease prediction
    Authors: F Mostafa, E Hasan, M Williamson, H Khan
    Year: 2021
    Citations: 40

  • Title: The safety profile of home infusion of intravenous immunoglobulin in patients with neuroimmunologic disorders
    Authors: N Souayah, A Hasan, HMR Khan, HA Yacoub, M Jafri
    Year: 2011
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    Year: 2020
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  • Title: Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening?
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