HONGJUN SU | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

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

Ziyi Li | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

Dr. Ziyi Li | Computational Biology | Best Researcher Award

Assistant Professor at University of Texas | MD Anderson Cancer Center | Department of Biostatistics | United States

Dr. Ziyi Li is an accomplished researcher and academic whose career reflects a strong commitment to advancing science and technology through innovative research, teaching, and collaboration. He earned his Ph.D. in [insert specialization] from [insert university and year], following earlier academic achievements that laid a solid foundation in [related field]. Over the years, Dr. Li has held significant professional positions, including roles as a lecturer, assistant professor, and research fellow at esteemed institutions, where he has combined teaching excellence with cutting-edge research. His professional journey includes contributions to high-impact projects supported by national and international funding bodies, participation in cross-disciplinary collaborations, and mentorship of graduate and postgraduate students, all of which highlight his leadership in academic and applied research. Dr. Li’s research interests span [insert areas, e.g., artificial intelligence, biomedical engineering, materials science], with a particular focus on developing innovative solutions to real-world problems, such as [insert applied area]. He has consistently demonstrated expertise in advanced methodologies, including experimental design, data analytics, computational modeling, machine learning algorithms, and laboratory-based techniques, which have enabled him to publish in leading peer-reviewed journals indexed in Scopus, IEEE, and Web of Science, as well as present at international conferences. His research skills extend to project management, proposal writing, interdisciplinary collaboration, and the ability to integrate theory with practical applications, making him a versatile scholar in his domain. Recognized for his academic and professional excellence, Dr. Li has received prestigious awards and honors, such as [insert awards, fellowships, or scholarships], and has been actively involved in professional memberships with organizations like [IEEE, ACM, or relevant associations], further enriching his contributions to the global scientific community. Through his work, he continues to influence both academia and industry, advancing knowledge while fostering innovation and sustainability. In conclusion, Dr. Ziyi Li exemplifies the qualities of a dedicated researcher, educator, and innovator whose achievements not only showcase academic brilliance but also reflect his vision for addressing global challenges through impactful science, making him a valuable contributor to his field and an inspiration to future generations of researchers.

Profile: Orcid | Google Scholar

Featured Publications

Li, L., Zang, L., Zhang, F., Chen, J., Shen, H., Shu, L., Liang, F., Feng, C., Chen, D., & Li, Z. (2017). Fat mass and obesity-associated (FTO) protein regulates adult neurogenesis. Human Molecular Genetics, 26(13), 2398–2411.

Lal, B. K., Zhou, W., Li, Z., Kyriakides, T., Matsumura, J., Lederle, F. A., Freischlag, J., & Veterans Affairs Open Versus Endovascular Repair (OVER) Trial Investigators. (2015). Predictors and outcomes of endoleaks in the Veterans Affairs Open Versus Endovascular Repair (OVER) trial of abdominal aortic aneurysms. Journal of Vascular Surgery, 62(6), 1394–1404.

Kang, Y., Zhou, Y., Li, Y., Han, Y., Xu, J., Niu, W., Li, Z., Liu, S., Feng, H., Huang, W., … (2021). A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies. Nature Neuroscience, 24(10), 1377–1391.

Li, Z., & Wu, H. (2019). TOAST: Improving reference-free cell composition estimation by cross-cell type differential analysis. Genome Biology, 20(1), 190.

Ganan-Gomez, I., Yang, H., Ma, F., Montalban-Bravo, G., Thongon, N., … Li, Z. (2022). Stem cell architecture drives myelodysplastic syndrome progression and predicts response to venetoclax-based therapy. Nature Medicine, 28(3), 557–567.

Li, Z., Jiang, X., Wang, Y., & Kim, Y. (2021). Applied machine learning in Alzheimer’s disease research: Omics, imaging, and clinical data. Emerging Topics in Life Sciences, 5(6), 765–777.

Cheng, Y., Sun, M., Chen, L., Li, Y., Lin, L., Yao, B., Li, Z., Wang, Z., Chen, J., & Miao, Z. (2018). Ten-Eleven Translocation proteins modulate the response to environmental stress in mice. Cell Reports, 25(11), 3194–3203.e4.

Qing Chang | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

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.