Hongzhen Wang | HealthTech and Wearables | Women Researcher Award

Published on by Technology Scientists

Dr. Hongzhen Wang | HealthTech and Wearables | Women Researcher Award

Associate Professor at Zhejiang A&F University, China

Wang Hongzhen is an accomplished Associate Professor and Master’s Supervisor specializing in plant biochemistry and medicinal plant research. With over two decades of academic and research experience, she has focused on advancing the authenticity, classification, and cultivation of Anoectochilus roxburghii, a highly valued medicinal orchid. Her academic journey spans Shanxi University, Guizhou University, and a Ph.D. from Linnaeus University in Sweden, equipping her with a global research perspective. Currently serving at Zhejiang A&F University, she integrates traditional plant sciences with modern biotechnological tools, including hyperspectral imaging and machine learning, to address challenges in medicinal plant authenticity and health applications. Having authored more than 30 high-impact papers, led numerous provincial and national projects, and earned awards for her contributions, Wang’s research significantly contributes to the advancement of health-related technologies and the sustainable development of medicinal plant resources.

Professional Profile

Scopus

Education

Wang Hongzhen’s education reflects a solid foundation in biological sciences and plant biochemistry. She began her academic training at the College of Life Sciences, Shanxi University, where she acquired essential knowledge of genetics and plant physiology. She then pursued postgraduate studies at the Institute of Genetic Engineering and Molecular Biology, Guizhou University, focusing on genetic regulation and biochemical pathways in plants. To advance her expertise, she completed her doctoral studies at Linnaeus University, Sweden, where she conducted extensive research in plant biochemistry, molecular biology, and the physiological mechanisms underlying medicinal plants. Her education uniquely combines traditional Chinese medicine plant studies with modern molecular tools and international scientific methodologies. This broad educational background prepared her to address critical questions in plant-based healthcare and medicinal resource development. Through this journey, she gained the capacity to integrate advanced research technologies, including hyperspectral imaging and bioinformatics, into her research on medicinal plant authentication.

Experience

Wang Hongzhen has built a rich academic and research career that bridges plant biochemistry, medicinal plant cultivation, and health-related applications. She began her professional journey as a teacher at Zhejiang Forestry College, where she contributed to developing courses in biotechnology and plant sciences. After completing her Ph.D. in Sweden, she joined Zhejiang A&F University in, where she continues to serve in the Discipline of Chinese Medicine. Over her career, she has presided over or contributed to more than 14 national and provincial projects, including studies funded by the National Natural Science Foundation of China. Her project leadership includes topics such as germplasm quality evaluation, resistance mechanisms, and cultivation innovations for Anoectochilus roxburghii. Beyond academic teaching, she has actively collaborated in advancing agricultural biotechnology and integrating medicinal plant research with modern imaging and computational analysis. Her career illustrates a continuous progression toward interdisciplinary, impactful scientific contributions in HealthTech and plant sciences.

Research Focus

Wang Hongzhen’s research focuses on the intersection of plant biochemistry, computational imaging, and medicinal resource sustainability. Her primary work centers on Anoectochilus roxburghii, a rare and valuable medicinal orchid widely used in traditional medicine. She investigates quality evaluation of germplasm resources, development of high-yield and disease-resistant varieties, and protocorm-like body formation mechanisms for scalable cultivation. Recently, she has integrated hyperspectral imaging and machine learning to achieve small-sample authenticity identification and variety classification, bridging biotechnology with cutting-edge computational methods. This research ensures authenticity, prevents adulteration, and enhances traceability of medicinal plants in healthcare applications. Additionally, she has explored molecular mechanisms such as polyamine regulation, enzyme gene function, and stress resistance in medicinal species. Her work is not only fundamental for improving the pharmacological reliability of herbal resources but also future-oriented in connecting plant sciences with HealthTech innovations, including wearable biosensing and AI-based diagnostic tools.

Publication Top Note

Title: Small-Sample Authenticity Identification and Variety Classification of Anoectochilus roxburghii (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning
Authors: Wang Hongzhen.
Summary: The study combines hyperspectral imaging with machine learning to authenticate and classify A. roxburghii from small samples, offering a fast and reliable method to prevent adulteration in medicinal plants.

Conclusion

Wang Hongzhen’s research demonstrates a rare combination of depth in plant biochemistry and breadth in applying advanced computational tools such as hyperspectral imaging and machine learning to address real-world problems in medicinal plant science. Her contributions in germplasm evaluation, cultivation, and molecular regulation of Anoectochilus roxburghii are significant, impactful, and forward-looking. With further emphasis on interdisciplinary international collaboration and AI-driven translational outputs, she is highly suitable for the Women Researcher Award.

Axel Ransinangue | Computer Vision Systems | Best Academic Researcher Award

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Mr. Axel Ransinangue | Computer Vision Systems | Best Academic Researcher Award

PhD Candidate at University of Bordeaux in France.

Axel Ransinangue is a Ph.D. candidate at Bordeaux University, conducting research at the intersection of artificial intelligence and geosciences. Specializing in deep learning for carbonate reservoir characterization, his work integrates advanced image processing, computer vision, and geological interpretation. Axel’s expertise spans Python, MATLAB, TensorFlow, PyTorch, and geospatial tools such as QGIS and ArcGIS, enabling him to develop innovative solutions for analyzing thin section images, petrophysical properties, and hyperspectral datasets. Collaborating closely with TotalEnergies, he has designed semi-supervised classification systems, synthetic data generation pipelines, and multiscale segmentation techniques that bridge synthetic and real-world geological imagery. His contributions extend beyond research, actively engaging in scientific communication through conferences and leading discussions in the computer vision community. Driven by a passion for data-driven science, Axel’s work demonstrates both academic rigor and industrial relevance, making him a promising leader in AI-driven geoscience innovation.

Professional Profile

Google Scholar

Education

Axel holds a Bachelor’s degree in Earth Sciences and Environment with honors from Pau University, where he developed strong foundations in porous media analysis and image processing. He pursued a Master’s degree in Engineering from Bordeaux INP – ENSEGID, graduating with honors, and participated in an international exchange at Kyushu University, Japan, expanding his technical and cultural perspectives. Currently, Axel is a Ph.D. candidate in Artificial Intelligence, Sciences, and Environment at Bordeaux University, working in collaboration with TotalEnergies. His doctoral research integrates AI methodologies with carbonate petrography to enhance reservoir characterization. Under the supervision of experts in geology and computer science, he specializes in representation learning, domain adaptation, and synthetic data conditioning for geological imagery. This interdisciplinary education has equipped him with a unique blend of computational, analytical, and field-specific skills, positioning him at the forefront of AI applications in earth sciences.

Experience 

Axel’s professional experience blends academic research with industrial applications. At TotalEnergies, as a Geologist Intern, he analyzed carbonate thin sections, interpreting petrographic features for reservoir evaluation. Later, as a Data Scientist at AGEOS, he applied hyperspectral imaging to mineralogical quantification, developing regression models and calibrating point cloud acquisitions. His current role as a Ph.D. researcher involves designing deep learning systems for carbonate reservoir characterization, focusing on semi-supervised classification, conditional synthetic dataset generation, and multiscale image segmentation. He has also explored model explainability, ensuring AI decisions are interpretable for geological experts. Additionally, Axel has worked on integrating bi-modal classification models combining imagery with petrophysical data, as well as anomaly detection frameworks. His cross-domain expertise enables the translation of AI methodologies into practical tools for geoscience, creating value both in research and industrial operations.

Research Focus 

Axel’s research lies at the convergence of computer vision, deep learning, and carbonate petrography. His primary objective is to enhance geological image analysis through advanced AI-driven methodologies. Key areas include representation learning with invariance to interpretation biases, synthetic dataset generation conditioned on geological parameters, and domain adaptation to bridge synthetic and real-world imagery. He specializes in texture synthesis, pixel harmonization, and object packing strategies for creating high-quality training data when labeled datasets are scarce. His work also involves developing heuristics-based regularization techniques for improved segmentation accuracy and integrating statistical analysis to link image descriptors with petrophysical properties. By leveraging semi-supervised and multi-modal approaches, Axel aims to create robust and generalizable AI models that address challenges in reservoir characterization. This research not only advances geological sciences but also contributes to broader AI applications in image-based data interpretation across environmental and industrial domains.

Publication Top Notes

Title: SynSection: Sedimentology-driven data generation for deep learning applications in carbonate petrography
Authors: A. Ransinangue, R. Labourdette, E. Houzay, S. Guillon, R. Bourillot, et al.
Journal: Marine and Petroleum Geology, Article ID 107490.
Summary: The study presents SynSection, a framework for generating synthetic carbonate thin section images based on sedimentological parameters. Combining texture synthesis, object packing, and pixel harmonization, it produces realistic datasets to train deep learning models when labeled geological data is scarce. Demonstrated improvements in image classification and segmentation highlight its potential for reservoir characterization and data-driven petrography.

Conclusion

Axel Ransinangue presents a compelling case for recognition as a Best Academic Researcher. The work combines cutting-edge AI methodologies with domain-specific geological expertise, producing research that is both academically valuable and industrially applicable. With ongoing expansion of publication output and interdisciplinary collaborations, the candidate has strong potential to emerge as a leading figure in AI-driven geoscience research. Their contributions already reflect a blend of innovation, rigor, and practical relevance that aligns well with the award’s intent.