Elma Hrustemovic | Soil | Best Researcher Award

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Dr. Elma Hrustemovic | Soil | Best Researcher Award

Elma Hrustemovic | University of Sarajevo Veterinary Faculty | Bosnia

Elma Hrustemovic is a doctoral student at the University of Sarajevo, Faculty of Veterinary Medicine, specializing in Veterinary Medicine and Public Health. An Academic Specialist in Clinical Microbiology, she is actively engaged in research on foodborne pathogens, antibiotic resistance, and molecular diagnostics. With over 40 scientific contributions, including peer-reviewed papers, conference presentations, and book chapters, she combines advanced laboratory expertise with a strong commitment to public health and food safety.

Profile

Orcid

Summary:

Elma Hrustemovic is a promising and dedicated researcher whose expertise in microbiology, public health, and food safety positions her as a strong candidate for the Best Researcher Award. Her combination of academic excellence, leadership, and impactful research demonstrates a clear commitment to advancing scientific knowledge and addressing global health challenges.

Education

Elma Hrustemovic is a Doctoral Student in the third year of Veterinary Medicine and Public Health at the Faculty of Veterinary Medicine, University of Sarajevo. She has completed an integrated graduate and postgraduate academic program in Veterinary Medicine, accumulating comprehensive expertise in the field. She holds the title of Doctor of Veterinary Medicine and is an Academic Specialist in Veterinary Medicine in Clinical Microbiology. Her academic background also includes a postgraduate specialist degree in Clinical Microbiology and advanced professional training in public health management, internal auditing for the public health sector, and governance for management and supervisory boards of companies with state capital involvement.

Experience

Elma is an active researcher and a dedicated member of the research team at the University of Sarajevo – Faculty of Veterinary Medicine. She is engaged in multiple scientific projects focusing on the detection, identification, and antibiotic resistance profiling of foodborne pathogens such as Listeria monocytogenes and Campylobacter spp. Her work involves applying molecular techniques such as RealTime PCR, sequencing, and next-generation sequencing, alongside traditional microbiological methods. She has coordinated and contributed to various research work packages covering sampling, laboratory analysis, molecular confirmation, sequencing, antibiotic resistance testing, data harmonization, and dissemination of scientific findings.

Research Focus

Her research primarily targets the prevalence, distribution, and antibiotic resistance of foodborne pathogens, with a strong emphasis on public health safety and food quality assurance. She is involved in studies examining the genetic diversity of Listeria monocytogenes and Campylobacter spp., the public health impact of antimicrobial resistance, and the development of molecular detection and identification protocols. Her scholarly contributions include numerous peer-reviewed publications, conference proceedings, and ongoing book and manual authorships in the field of genetically modified organisms, microbiological safety, and pathogen control in the food chain.

Skills

Elma possesses advanced expertise in clinical microbiology, including isolation, identification, and characterization of microorganisms using microscopic, cultural, biochemical, and molecular methods. She is skilled in antibiotic sensitivity testing, serological diagnostics, immunoprophylaxis, and interpretation of microbiological results. Her technical proficiency extends to digital tools such as Microsoft Office Suite, Microsoft Project, Joomla, Adobe Photoshop, and database management systems.

Awards

Elma has been recognized for her leadership and scientific engagement, serving as President of the Technical Committee for the VIII Student Congress “Food–Nutrition–Health” at the University of Sarajevo. She has actively contributed to both domestic and international scientific forums through oral presentations, posters, and published research.

Publications

Title: Testing the effectiveness of 6% domestic vinegar on the reduction of Campylobacter species in chicken meat production
Author: Elma Hrustemovic
Journal: Journal of the Science of Food and Agriculture
Year: 2025

Conclusion:

Elma Hrustemovic is highly suitable for the Best Researcher Award. With continued growth in global collaborations and applied research outcomes, she has the potential to become a leading figure in her field.

Jingzhe Wang | Soil Mapping | Best Researcher Award

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Dr. Jingzhe Wang | Soil Mapping | Best Researcher Award

Dr. Jingzhe Wang, Shenzhen Polytechnic University, China

Dr. Jingzhe Wang is a Lecturer at the School of Artificial Intelligence, Shenzhen Polytechnic University, China. He holds a Ph.D. in Environmental Science from Xinjiang University, where his research focused on wetland landscape dynamics in arid regions. His expertise lies in land remote sensing, digital soil mapping, and geospatial data analysis, with a focus on environmental sustainability and global change. Dr. Wang has received several national honors, including the National Scholarship for Doctoral Students and the Remote Sensing Excellent Achievement Award in China.

Profile

Scopus

Orcid

Summary:

Dr. Jingzhe Wang is a promising and productive early-career researcher with a well-defined focus in remote sensing, environmental change, and spatial science. His work is methodologically rigorous, thematically timely, and nationally recognized. He demonstrates a clear upward trajectory in research quality and influence, with publications in high-impact journals and commendable technical capabilities.

🎓 Education

Dr. Jingzhe Wang earned a Ph.D. in June 2019 from the College of Resources and Environmental Science at Xinjiang University, China, under the mentorship of Prof. Jianli Ding. His doctoral research focused on the landscape dynamics of terminal lake wetlands in arid regions, using Ebinur Lake Wetland as a case study. He also holds a Bachelor of Science degree from the School of Resources and Environment at Anqing Normal University, obtained in June 2014.

💼Experience

Currently serving as a Lecturer at the School of Artificial Intelligence, Shenzhen Polytechnic University since March 2022, Dr. Wang holds a permanent academic appointment. Prior to this, he completed a postdoctoral fellowship at the MNR Key Laboratory for Geo-Environmental Monitoring of the Great Bay Area, Shenzhen University, from November 2019 to February 2021.

🔬Research Focus

Dr. Wang’s research interests lie at the intersection of Earth observation and digital environmental analysis. He specializes in land remote sensing, global change studies, digital soil mapping, and spatial geographic information science. His ongoing work contributes to advancing environmental sustainability through cutting-edge geospatial technologies.

🛠️Skills

Dr. Wang possesses advanced expertise in spectral modeling using machine learning, alongside proficiency in spectral pretreatment techniques and data mining. His interdisciplinary capabilities support innovative research in geospatial analysis and environmental monitoring.

🏆Awards

His work has been consistently recognized at both national and academic levels. He received the prestigious National Scholarship for Doctoral Students from the Ministry of Education of China in 2018. In 2022, he was honored with the Excellent Academic Papers Award in the Natural Sciences by the Science & Technology Department of Xinjiang. He also earned the Best Oral Report Award at the 2023 Symposium of Remote Sensing of Ecosystems organized by the Ecological Society of China, and was part of a team awarded the Remote Sensing Excellent Achievement Award by the Chinese National Committee for Remote Sensing.

📚 Publications

  • Title: Spatiotemporal analysis of AGB and BGB in China: Responses to climate change under SSP scenarios
    Authors: C. Zhu, Y. Li, J. Ding, X. Chen, Z. Zhang
    Year: 2025
    Journal: Geoscience Frontiers

  • Title: Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models
    Authors: Z. Hu, J. Zhang, Z. Liu, Q. Zhang, G. Wu
    Year: 2025
    Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Title: Effects of degraded land surface water on spatiotemporal distribution of regional aerosols over northwest China
    Authors: J. Wang, J. Ding, X. Chen, X. Ge, Y. Wu
    Year: 2025
    Journal: Dili Xuebao / Acta Geographica Sinica

  • Title: Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China
    Authors: J. Cao, Z. Zhang, J. Ding, X. Ge, J. Wang
    Year: 2025
    Journal: Land Degradation and Development

  • Title: Unveiling the Dynamic Patterns and Driving Forces of Soil Organic Carbon in Chinese Croplands From 1980 to 2020
    Authors: J. Ai, Z. Zhang, C. Yang, X. Chen, J. Wang
    Year: 2025
    Journal: Land Degradation and Development

  • Title: UAS-based remote sensing for agricultural monitoring: Current status and perspectives
    Authors: J. Wang, S. Zhang, I. Lizaga, Q. Huang, Z. Hu
    Year: 2025

  • Title: Assessing the reactions of tourist markets to reinstated travel restrictions in the destination during the post-COVID-19 phase
    Authors: X. Ma, R. Ma, Z. Ma, C. Wang, F. Han
    Year: 2024
    Journal: Scientific Reports

  • Title: Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?
    Authors: Z. Hu, Y. Chu, Y. Zhang, J. Wang, G. Wu
    Year: 2024
    Journal: International Journal of Applied Earth Observation and Geoinformation

  • Title: The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis
    Authors: J. Chen, Y. Wang, J. Wang, F. Lu, Z. Hu
    Year: 2024
    Journal: Remote Sensing

  • Title: Using ZY1-02D satellite hyperspectral remote sensing to monitor landscape diversity and its spatial scaling change in the Yellow River Estuary
    Authors: S. Cheng, X. Yang, G. Yang, K. Ren, W. Sun
    Year: 2024
    Journal: International Journal of Applied Earth Observation and Geoinformation

Conclusion:

Dr. Wang is a strong candidate for the Research for Best Researcher Award, especially in the environmental and geospatial domains. With continued advancement in leadership, global outreach, and project funding, he has the potential to evolve into a leading figure in his field.

Nafiseh Kakhani | Soil Mapping | Best Researcher Award

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Dr. Nafiseh Kakhani | Soil Mapping | Best Researcher Award

Dr. Nafiseh Kakhani, Eberhard Karls University of Tübingen, Germany

Dr. Nafiseh Kakhani is a researcher and lecturer at Eberhard Karls University of Tübingen, Germany, specializing in Earth observation, remote sensing, and machine learning. She holds a PhD in Remote Sensing Engineering from K. N. Toosi University of Technology, Tehran, where she focused on spectral-spatial classification of high-resolution multispectral images. Her research interests include AI-driven geospatial analysis, explainable machine learning, digital soil mapping, and land cover classification. She has extensive experience in deep learning, computer vision, and statistical modeling for environmental applications. Dr. Kakhani has received prestigious awards, including the Best Poster Award at the 2023 IEEE IADF-GRSS School, and has published widely in her field.

Profile

Scopus

Summary:

Dr. Kakhani is a strong candidate for the Best Researcher Award due to her innovative contributions to explainable AI applications in soil science. Her interdisciplinary research and ability to integrate AI-driven approaches into environmental studies make her work highly relevant and impactful. She has contributed significantly to understanding soil organic carbon dynamics, a crucial factor in climate change mitigation and sustainable agriculture.

🎓 Education

Nafiseh Kakhani holds a PhD in Remote Sensing Engineering from K N Toosi University of Technology in Tehran, Iran, where she specialized in spectral-spatial classification of high-resolution multispectral images using segmentation methods. She completed her MSc in Remote Sensing Engineering from the same university, focusing on integrating neural networks and fuzzy systems to improve classification methods in hyperspectral images. She earned her BSc in Geomatics and Geodesy Engineering from the University of Isfahan, where she developed a statistical classification method for Multi-Beam Echo Sounder data.

💼Experience

She is currently an Earth Observation and Machine Learning Scientist at The LandBanking Group GmbH in Munich, where she applies machine learning techniques to environmental monitoring and land assessment. Previously, she was a Postdoctoral Researcher and Lecturer at the University of Tübingen, where she conducted research in remote sensing and taught courses on deep learning, photogrammetry, and the fundamentals of remote sensing. She has also worked at the Iranian Space Agency, focusing on remote sensing sensor calibration and spectral library preparation. Additionally, she has mentored Master’s students in GIS, remote sensing, and machine learning and has been a guest speaker at international workshops and conferences.

🔬Research Focus

Her research focuses on computer vision for Earth observation, explainable machine learning, deep learning, statistical machine learning, pattern recognition, and Earth system science. She has worked extensively on AI-driven geospatial analysis, digital soil mapping, land cover classification, and the development of deep learning frameworks for remote sensing applications.

🛠️Skills

She is proficient in Python, using libraries such as PyTorch, TensorFlow, and Scikit-learn, as well as MATLAB and JavaScript for Google Earth Engine applications. She has expertise in geospatial software, including ArcGIS, QGIS, ENVI, ERDAS IMAGINE, and Agisoft Metashape. She is experienced in cloud computing platforms such as Google Cloud, AWS, and Tübingen ML Cloud, enabling her to process large-scale remote sensing datasets efficiently.

🏆Awards

She received the Best Poster Award at the 2023 IEEE IADF-GRSS School for her work on SoilNet, a spatio-temporal deep learning framework for digital soil mapping. She was a top-ranked graduate student in both her MSc and BSc programs and was admitted to her MSc without an entrance exam, a privilege reserved for exceptional students in Iran.

📚 Publications

Publication: Towards Explainable AI: Interpreting Soil Organic Carbon Prediction Models Using a Learning-Based Explanation Method

Authors: N. Kakhani, R.T. Mehrjardi, D. Omarzadeh, U. Heiden, T.J. Scholten

Journal: European Journal of Soil Science

Year: 2025

Conclusion:

Dr. Nafiseh Kakhani is a highly suitable candidate for the Best Researcher Award. While there is always room for further growth, her expertise and achievements in applying AI to soil science place her among the leading researchers in this field.