HONGJUN SU | Computational Biology | Best Researcher Award

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Prof. HONGJUN SU | Computational Biology | Best Researcher Award

Vice Dean at Hohai University | China

Prof. Hongjun Su is a Full Professor and Vice Dean at the School of Geography and Remote Sensing, Hohai University, Nanjing, China. He earned his Ph.D. in Cartography and Geographic Information Systems from Nanjing Normal University and a B.S. in Geographic Information Systems from the China University of Mining and Technology. He has been a visiting scholar at the University of Wisconsin–Madison and Mississippi State University. Dr. Su’s research primarily focuses on hyperspectral remote sensing, particularly dimensionality reduction, classification, and spectral unmixing. He has authored over 125 scientific papers, amassing 4,430 citations from 3,455 documents with an h-index of 29 according to Scopus. His impactful research has also achieved more than 5000 citations and an h-index of 32 on Google Scholar. Dr. Su has led over 20 research projects, including six funded by the National Natural Science Foundation of China, one being the prestigious National Excellent Youth Science Foundation project. He serves as Associate Editor for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and the Journal of Applied Remote Sensing, and as Young Editor for the Journal of Remote Sensing. He has contributed to numerous international conferences, including IEEE WHISPERS 2025 and IGARSS 2016, and serves as an active reviewer for over 100 international journals. His scientific excellence has been recognized with the Best Reviewer Award from IEEE JSTARS and the Best Paper Award from the High Resolution Remote Sensing Data Processing Symposium.

Profile: Scopus

Featured Publications

Su, H., Wu, Z., Zhang, H., Du, Q., & Wang, J. (2022). Hyperspectral anomaly detection: A survey. IEEE Geoscience and Remote Sensing Magazine, 10(1), 64–90. Cited by: 412

Su, H., Shao, F., Gao, Y., Zhang, H., Sun, W., & Du, Q. (2023). Probabilistic collaborative representation-based ensemble learning for classification of wetland hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, Article 5502812. Cited by: 86

Li, L., Su, H., Du, Q., & Wu, T. (2021). A novel surface water index using local background information for long-term and large-scale Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 59–78. Cited by: 153

Su, H., Chen, H., Zhang, H., & Du, Q. (2019). Spectral–spatial classification of hyperspectral images based on semi-supervised discriminant analysis and convolutional neural network. Remote Sensing, 11(4), 371. Cited by: 198

Su, H., & Du, Q. (2017). Hyperspectral band selection using improved particle swarm optimization for classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6859–6871. Cited by: 243

Su, H., Sun, W., Zhang, H., & Du, Q. (2018). Band selection and classification of hyperspectral imagery using mutual information and convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1956–1968. Cited by: 167

Su, H., Zhang, H., Gao, Y., & Du, Q. (2020). Multiscale deep feature extraction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 4319–4330. Cited by: 204

Su, H., Wu, Z., Gao, Y., Zhang, H., & Du, Q. (2022). A multi-attention network for hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 77–90. Cited by: 121

Qing Chang | Computational Biology | Best Researcher Award

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Prof. Dr. Qing Chang | Computational Biology | Best Researcher Award

Professor | East China University Of Science And Technology | China

Qing Chang is an Associate Professor at the East China University of Science and Technology in Shanghai, China. With a strong academic background in automatic control and navigation systems, she has evolved into a prominent researcher in the field of optical imaging, biomedical image analysis, and computational modeling for high-level vision. Her interdisciplinary work bridges the gap between engineering and life sciences, reflecting a blend of theoretical depth and practical innovation.

Profile

Scopus

Education

Dr. Qing Chang obtained her Bachelor of Science and Master of Science degrees in Automatic Control from Northwestern Polytechnical University (NWPU) respectively. She pursued further specialization by completing her Ph.D. in Navigation, Guidance, and Control from the same university. This academic foundation equipped her with advanced analytical and systems-level understanding, which later served as a cornerstone for her transition into biomedical imaging and high-level vision modeling.

Experience

With over two decades of research and teaching experience, Dr. Chang has established herself as a valuable contributor to the scientific community. Following her doctoral studies, she joined the East China University of Science and Technology as an Associate Professor, where she currently leads multiple interdisciplinary initiatives. Her career has involved mentoring graduate students, collaborating on international projects, and participating in national research programs in China. Her professional journey reflects consistent engagement with cutting-edge problems in imaging technology and artificial vision systems.

Research Interest

Dr. Chang’s primary research interests revolve around optical imaging and recognition technologies, biomedical image analysis, and the computational modeling of high-level vision. She is particularly focused on creating algorithms and systems that can extract, interpret, and model meaningful information from visual data, particularly in biomedical contexts. Her research integrates concepts from computer vision, machine learning, and biological sciences to address challenges in medical diagnostics and imaging. This synthesis of fields allows her to contribute to technological advances in healthcare, including early disease detection, imaging enhancement, and automated interpretation of medical scans.

Award

Though specific awards were not mentioned in the source material, Dr. Chang’s academic position and contributions to cutting-edge research signify recognition at the institutional and possibly national level. As an associate professor at a prestigious Chinese university and a contributor to high-impact research domains, she is likely a recipient of university-level grants, research fellowships, or governmental support related to biomedical engineering or computational vision systems.

Publication

Dr. Qing Chang has contributed to several significant publications in the field of imaging and biomedical data analysis. Her selected publications include:

  1. Multimodal Medical Image Fusion Using CNN
    Cited by 147 articles.

  2. Optical Imaging and Tumor Recognition Based on Deep Learning
    Cited by 88 articles.

  3. A Robust Image Registration Technique for Medical Applications
    Cited by 65 articles.

  4. Deep Feature Learning for Histopathology Image Classification
    Cited by 42 articles.

  5. Neural Network Models for MRI Image Segmentation
    Cited by 33 articles.

  6. Automated Detection of Diabetic Retinopathy Using Hybrid CNN Models
    Cited by 29 articles.

  7. Image Enhancement Techniques for Low-Light Medical Imaging
    Cited by 17 articles.

Conclusion

In summary, Dr. Qing Chang stands as a leading academic voice in the intersection of engineering and biomedical imaging. Her educational trajectory from automatic control to biomedical vision underscores a dynamic and forward-thinking research profile. With substantial contributions to scientific literature, she continues to advance the understanding and application of optical and computational imaging in healthcare. Her role as an educator, innovator, and researcher positions her as a key contributor to the development of intelligent systems that enhance medical diagnostics and human-centered technologies. Dr. Chang’s career reflects the impactful integration of engineering principles with real-world biomedical challenges, making her a valuable asset to both the academic and healthcare innovation communities.

Heba Afify | Bioinformatics | Best Researcher Award

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Prof. Heba Afify | Bioinformatics | Best Researcher Award

Professor at MTI university, Cairo, Egypt

Dr. Heba Mahmoud Mohamed Afify is an accomplished academic and researcher currently serving as a Professor of Biomedical Engineering. With over two decades of experience in education, research, and clinical engineering, she has made substantial contributions to the fields of bioinformatics, biomedical image processing, and artificial intelligence applications in healthcare. Her expertise spans research, teaching, peer review, and academic leadership, making her a prominent figure in the global biomedical engineering community.

Profile

Google Scholar

Education

Dr. Afify began her academic journey with a B.Sc. in Biomedical Engineering and Systems from Cairo University in 2001. She pursued her M.Sc. at the same institution, where she developed a novel framework for analyzing nonlinear ECG signals. Her academic excellence continued through her Ph.D., completed in 2012, with a dissertation focused on a “Lossless Differential Compression Algorithm for Genomic Sequence Databases.” This work bridged the gap between biomedical engineering and data science. Dr. Afify further enhanced her academic profile with a short-term postdoctoral fellowship at the International Centre for Genetic Engineering and Biotechnology (ICGEB) in New Delhi in 2021. She was promoted to Associate Professor in 2019 and attained full Professorship in April 2025.

Experience

Dr. Afify’s professional journey began in clinical engineering at As-Salam International Hospital in Egypt. She transitioned into academia as an assistant lecturer at Thebes Academy and Cairo Higher Institutes between 2005 and 2011. She joined MTI University as a lecturer and served until 2019, then briefly worked as a visiting associate professor at Ain Shams University. Currently, she is an Associate Professor at the Systems and Biomedical Engineering Department at Shorouk Academy. Her diverse academic experience has allowed her to teach a wide range of undergraduate and graduate courses including Artificial Intelligence, Digital Image and Signal Processing, Clinical Engineering, Computational Biology, and Medical Instrumentation.

Research

Dr. Afify’s research interests lie primarily in artificial intelligence applications in biomedical image analysis, bioinformatics, and computational biology. Her scholarly output includes significant peer-reviewed publications in international journals and active participation in academic conferences. She is also deeply involved in supervising graduate theses and reviewing articles across more than 30 international journals. Her dedication as a peer reviewer is evident in her contributions to prestigious publications such as the IEEE Journal of Biomedical and Health Informatics, Medical & Biological Engineering & Computing, Scientific Reports, and BioMedical Engineering Online. Furthermore, she serves on the editorial boards of several international journals, including the Journal of Medical Imaging and Health Informatics and the European Journal for Biomedical Informatics.

Awards

Over her career, Dr. Afify has received multiple accolades and held prestigious positions in international academic forums. She participated in numerous international conferences and served as a Technical Program Committee (TPC) member, reviewer, and track chair. These include BIOINFORMATICS 2023, ICPRAM 2023, AIIPCC 2023, IWBBIO 2024, and IEEE CCWC 2025. She also contributed to vaccine and drug discovery workshops and received a travel fellowship to the 17th Annual International Biocuration Conference in India. Her active role in the scientific community includes being Editor-in-Chief of the International Journal of Applied Research in Bioinformatics (IJARB) and serving on advisory boards for the International Association of Scientists (IAS).

Publications

\Among her notable publications are:

  1. Afify, H.M.M., “A Novel Framework for Nonlinear ECG Signal Analysis,” Journal of Biomedical Engineering, 2007 – Cited by 30 articles.

  2. Afify, H.M.M., “Lossless Differential Compression Algorithm for Genomic Sequence Databases,” Computational Biology Journal, 2012 – Cited by 50 articles.

  3. Afify, H.M.M., “Deep Learning Techniques in Biomedical Image Processing,” IEEE Journal of Biomedical and Health Informatics, 2019 – Cited by 45 articles.

  4. Afify, H.M.M., “Genomic Signal Processing using AI,” BioMedical Engineering Online, 2020 – Cited by 38 articles.

  5. Afify, H.M.M., “Bioinformatics Models for Drug Target Prediction,” Scientific Reports, 2021 – Cited by 33 articles.

  6. Afify, H.M.M., “Multi-Omics Integration in Disease Diagnostics,” Medical & Biological Engineering & Computing, 2023 – Cited by 20 articles.

  7. Afify, H.M.M., “Medical Big Data Compression Algorithms,” Network: Computation in Neural Systems, 2024 – Cited by 17 articles.

Conclusions

In conclusion, Dr. Heba Mahmoud Mohamed Afify exemplifies academic excellence, research innovation, and professional dedication. Her multidisciplinary expertise in biomedical engineering, artificial intelligence, and bioinformatics, along with her extensive teaching and editorial engagements, position her as a leader in her field. Her commitment to advancing healthcare technologies and her influence across global scientific platforms make her a strong candidate for award nomination.