Hongzhen Wang | HealthTech and Wearables | Women Researcher Award

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.

Ataollah Shirzadi | Natural Hazards | Best Researcher Award

Dr. Ataollah Shirzadi | Natural Hazards | Best Researcher Award

University of Kurdistan, Iran.

Dr. Ataollah Shirzadi is an Assistant Professor at the University of Kurdistan, Iran, specializing in Watershed Management Engineering. With over 90 peer-reviewed publications, he has become an internationally recognized figure in the field of natural hazard modeling. His expertise spans flash flood forecasting, shallow landslide prediction, soil erosion modeling, and geospatial risk assessment, all enhanced through artificial intelligence techniques. Notably, he was listed among the top 2% and 1% of global scientists in 2022 and 2023 by Stanford University and ISC. Dr. Shirzadi has contributed to several high-impact collaborative projects, including an international Iran-China initiative on flood susceptibility. He serves as a reviewer and editorial member for multiple international journals and actively mentors graduate researchers. His research bridges theoretical modeling and real-world disaster management, making significant strides in environmental resilience.

🔎Author Profile

🏅Awards and Honors

  1. Ranked among the Top 2% Scientists in the World

    • Source: Stanford University Ranking

    • Years: 2022 and 2023

  2. Ranked among the Top 1% Scientists in the Islamic World

    • Source: ISC (Islamic World Science Citation Center)

    • Year: 2023

  3. Elite and Talent Recognition Awards

    • By: Iran’s National Elite Foundation (for undergraduate and postgraduate academic excellence)

    • Context: Received for high GPA and top-ranking achievements during B.Sc. and M.Sc. studies

  4. National University Admission Ranks

    • Ranked 1st in M.Sc. entrance exam for the field of Mass Movements in Watershed Management

    • Ranked 3rd in B.Sc. entrance exam for Hydrology and Climatology

  5. Excellence Awards

    • University Honors: Recognized multiple times as top student in academic performance at both undergraduate and postgraduate levels

🏆Strengths for the Award

  1. High-Impact Research Contributions
    Dr. Shirzadi has co-authored over 90 international publications, with many articles appearing in top-tier journals such as Science of The Total Environment, Journal of Hydrology, Environmental Modelling & Software, Geomatics, Natural Hazards and Risk, and Remote Sensing. His research has garnered significant citations (e.g., 756 citations for a 2018 paper and 699 for a 2019 paper), reflecting the impact and relevance of his work in the scientific community.

  2. Pioneering Work in Natural Hazard Modeling
    His expertise lies in landslide, flash flood, and flood susceptibility modeling, with a strong emphasis on machine learning, deep learning, and hybrid AI techniques. Notably, he has developed several novel hybrid intelligence models integrating techniques like ANFIS, Genetic Algorithms, SVMs, Decision Trees, and ensemble approaches, widely cited and validated across diverse geographies.

  3. Recognition & Global Ranking
    He has been recognized among the top 2% and 1% of scientists globally by Stanford University and the Islamic World Science Citation Center (ISC) in 2022 and 2023 — a rare distinction that underlines his international academic standing.

  4. Editorial and Reviewer Excellence
    He holds editorial roles in journals such as Frontiers in Earth Science and has reviewed for more than 30 ISI-indexed journals, including Scientific Reports, Journal of Hydrology, CATENA, and Environmental Earth Sciences. This affirms his reputation as a trusted evaluator of cutting-edge scientific work.

  5. Academic Mentorship and Leadership
    Dr. Shirzadi has supervised over 17 M.Sc. and Ph.D. students, contributing actively to capacity building in environmental modeling and risk assessment. He has also been involved in international collaborations, including a recent Iran-China flood monitoring project, reflecting both leadership and global outreach.

🎓 Education 

Dr. Shirzadi earned his Ph.D. in Watershed Management Engineering (2014–2017) from Sari Agricultural Sciences and Natural Resources University, where his thesis focused on spatial prediction of shallow landslides using advanced data mining algorithms. He previously obtained an M.Sc. in Mass Movements (2007–2009) with a GPA of 19/20, also from Sari University, where he developed regional models for rockfall hazard mapping. His B.Sc. in Hydrology and Climatology (2003–2007) was awarded by the University of Agricultural Science and Natural Resources of Gorgan . Over the years, Dr. Shirzadi has cultivated robust expertise in geospatial modeling, sediment transport, and flood risk analysis. He also holds certifications in GIS, SPSS, AutoCAD, and multiple machine learning platforms, demonstrating both strong academic credentials and technical fluency.

🔬 Research Focus on Natural Hazards

Dr. Shirzadi’s research centers on the integration of AI, machine learning, and geospatial analysis to assess and mitigate natural hazards. His core focus includes:

  • Flood Susceptibility Mapping using hybrid machine learning models (e.g., ANFIS, rotation forests).

  • Shallow Landslide Prediction with ensemble algorithms combining decision trees, support vector machines, and neural networks.

  • Risk Zonation for erosion and sediment transport based on satellite data and multi-criteria analysis.

  • Uncertainty Quantification in hazard models to improve resilience strategies.

  • Remote Sensing Applications using Sentinel-1/2 and Landsat data for urban and rural hazard detection.

His goal is to create smart, scalable, and interpretable models that guide land use planning, policy-making, and emergency preparedness in climate-sensitive regions.

📘 Publication Top Notes

  1. A Comparative Assessment of Decision Trees Algorithms for Flash Flood Susceptibility Modeling
    Authors: Khosravi K., Pham B.T., Chapi K., Shirzadi A., et al.
    Journal: Science of The Total Environment (2018)
    Summary: Compared multiple decision tree algorithms to model flash flood susceptibility in Iran’s Haraz watershed, showing ensemble methods outperformed traditional single classifiers.

  2. Flood Susceptibility Modeling Using MCDM and Machine Learning
    Authors: Khosravi K., Shahabi H., Adamowski J., Shirzadi A., et al.
    Journal: Journal of Hydrology (2019)
    Summary: Evaluated and contrasted MCDM techniques and AI models; hybrid ML models proved superior in spatial flood risk analysis.

  3. A Novel Hybrid AI Approach for Flood Susceptibility
    Authors: Chapi K., Singh V.P., Shirzadi A., et al.
    Journal: Environmental Modelling & Software (2017)
    Summary: Developed a hybrid model combining several AI algorithms to predict flood risk, improving classification accuracy in complex terrains.

  4. Flood Susceptibility Using ANFIS-GA-DE Hybrid
    Authors: Hong H., Panahi M., Shirzadi A., et al.
    Journal: Science of the Total Environment (2018)
    Summary: Applied ANFIS enhanced with genetic and differential evolution algorithms for flood prediction in China’s Hengfeng area.

  1. Forecasting Floods Using ML & Statistical Models
    Authors: Shafizadeh-Moghadam H., Shahabi H., Shirzadi A., et al.
    Journal: Journal of Environmental Management (2018)
    Summary: Combined ML and traditional statistical techniques to forecast flood-prone zones, optimizing accuracy and runtime efficiency.

🚀Conclusion

Dr. Ataollah Shirzadi stands out as an exceptionally qualified candidate for the Best Researcher Award, with an impressive combination of scholarly output, innovative AI-based methodologies, global recognition, and academic service. His work not only advances theoretical models but also addresses urgent environmental and societal challenges. With continued growth in communication and cross-disciplinary application, he is poised to make even greater contributions to science and practice.