Jinming yang | Remote sensing disaster detection | Best Researcher Award

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Assist. Prof. Dr. jinming yang | Remote sensing disaster detection | Best Researcher Award

Dr. JinMing Yang is an Assistant Researcher at the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. With a Ph.D. in Geography from Xinjiang University, his expertise lies in snow ecohydrology and disaster science in arid regions. His research integrates physical experiments, remote sensing technologies, and mathematical modeling to study snow and avalanche phenomena, focusing on precipitation-ice-snow runoff processes and snow disaster assessment. He has developed advanced remote sensing models for detecting avalanche debris and contributed significantly to understanding the dynamic evolution of mountain disasters. He has published influential articles in SCI-indexed journals, including innovations in automatic avalanche detection using C-band SAR data. He serves as a reviewer for Cold Regions Science and Technology and holds membership in the Xinjiang Natural Resources Society. His scientific contributions are helping to improve snow safety and disaster risk management, making him a prominent figure in his field.

Profile

🎓 Education

Dr. JinMing Yang earned his Ph.D. in 2017 from Xinjiang University, China, majoring in Geography with a specialization in Physical Geography. His doctoral studies provided a strong foundation in environmental and geospatial sciences, with a specific focus on the cryosphere in arid regions. He received intensive training in field-based observation, experimental research, remote sensing data analysis, and mathematical modeling techniques. Throughout his academic career, he has demonstrated a high level of scientific rigor and innovation, especially in integrating electromagnetic spectrum characteristics with snow and avalanche studies. His education combined theoretical depth with applied research, equipping him with the skills to investigate ecohydrological processes, snow accumulation dynamics, and avalanche risk modeling. This academic background not only enhanced his capacity for independent research but also prepared him for interdisciplinary collaborations aimed at understanding and mitigating natural disasters in vulnerable mountainous terrains.

🧪 Experience

Since completing his Ph.D., Dr. JinMing Yang has served as an Assistant Researcher at the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. He has accumulated significant experience in snow ecohydrology, particularly within arid and mountainous environments. His work centers on monitoring and modeling snow-related hazards using advanced remote sensing technologies, including SAR data, to improve avalanche detection accuracy. He led a National Natural Science Foundation of China project focused on spatiotemporal evolution of avalanches in the western Tianshan Mountains, employing multi-modal electromagnetic spectrum coupling. Dr. Yang has also contributed to disaster risk assessment frameworks and snow safety strategies, often working with cross-disciplinary teams. His methodological expertise in fusing remote sensing, geostatistics, and simulation models makes him an asset in both academic and practical contexts. Additionally, his editorial and peer-review contributions reflect his growing leadership in the cryosphere and natural hazards research community.

🏅 Awards and Honors

Dr. JinMing Yang has emerged as a notable young researcher in the field of snow disasters and mountain hazard assessment. While specific awards are not listed in the provided information, his nomination for the “Best Researcher Award” highlights his recognized excellence and impact in scientific research. His groundbreaking work on avalanche detection using SAR data and multi-source model integration has earned him reviewer responsibilities for prestigious journals like Cold Regions Science and Technology. His citation index of 29 indicates growing academic recognition, and his project funding from the National Natural Science Foundation of China signifies trust in his expertise and innovative capacity. His active role in professional societies like the Xinjiang Natural Resources Society further reflects his integration into influential academic networks. His contributions are instrumental in shaping practical policies for snow safety in arid mountain environments, positioning him as a strong candidate for national and international research accolades.

🔬 Research Focus

Dr. JinMing Yang’s research is focused on snow ecohydrology and disaster risk modeling in arid and mountainous regions, with snow as the central element. He explores the precipitation-ice-snow runoff process and studies the mechanisms driving avalanche formation and development. A major aspect of his work involves remote sensing-based detection and assessment of snow disasters, particularly the use of SAR data to automate avalanche debris identification. His innovative research has led to the development of independently constructed models combining multi-source variables to quantify avalanche risk spatially and temporally. He also investigates the seasonal dynamics and causal variable influences on avalanche behavior, contributing to mountain safety science. These models offer insights into hazard evolution, aiding in effective disaster prevention and response. His methodology relies heavily on geospatial analysis, electromagnetic spectrum modeling, and simulation techniques, bridging theoretical understanding with real-world applications for environmental safety and planning.

✅ Conclusion

Dr. JinMing Yang stands out as an accomplished researcher whose innovative contributions to snow ecohydrology and remote sensing-based disaster assessment have advanced scientific understanding and practical management of snow-related hazards in arid mountain regions.

Publications

  • Dynamic spatiotemporal quantification of avalanches in the Central Tianshan Mountains by integrating air–space–ground collaborative sensing and snow field–terrain filters

    International Journal of Digital Earth
    2024-12-31 | Journal article
    DOI: 10.1080/17538947.2024.2432525
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: JinMing Yang; LanHai Li; Yang Liu

  • Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

    International Journal of Digital Earth
    2024-12-31 | Journal article
    DOI: 10.1080/17538947.2023.2299208
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: Yang Liu; Jinming Yang; Xi Chen; Junqiang Yao; Lanhai Li; Yubao Qiu

  • Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions

    CATENA
    2023-05 | Journal article
    DOI: 10.1016/j.catena.2023.106997
    Part ofISSN: 0341-8162
    CONTRIBUTORS: Yang Liu; Xi Chen; Jinming Yang; Lanhai Li; Tingting Wang

  • Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China

    Remote Sensing
    2022-03-10 | Journal article
    DOI: 10.3390/rs14061340
    Part ofISSN: 2072-4292
    CONTRIBUTORS: Jinming Yang; Qing He; Yang Liu

  • Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices

    CATENA
    2021-11 | Journal article
    DOI: 10.1016/j.catena.2021.105559
    Part ofISSN: 0341-8162
    CONTRIBUTORS: Yang Liu; Xi Chen; Yubao Qiu; Jiansheng Hao; Jinming Yang; Lanhai Li

  • Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

    Arabian Journal of Geosciences
    2021-03 | Journal article
    DOI: 10.1007/s12517-021-06699-y
    Part of ISSN: 1866-7511
    Part of ISSN: 1866-7538
    CONTRIBUTORS: Jinming Yang; Chengzhi Li

Zhiwei Fan | Geology | Best Researcher Award

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Dr. Zhiwei Fan | Geology | Best Researcher Award

Zhi-Wei Fan is a Ph.D. candidate at Central South University, China, specializing in geochemistry and petrology with a focus on magmatic-hydrothermal systems and rare metal mineralization. He integrates micro-analytical techniques such as LA-ICP-MS, EMPA, and XRD in mineralogical research, with key interests in beryl, muscovite, tourmaline, and pegmatite-hosted critical elements. Known for academic excellence and research contributions, he has received multiple scholarships and awards for innovation, leadership, and volunteer service. His scholarly works are published in reputed journals like Ore Geology Reviews and GSA Bulletin, contributing to the understanding of crystal chemistry and ore-forming processes in pegmatites. He actively participates in national ore deposit conferences and demonstrates strong communication skills in English. With a foundation built on excellence from undergraduate through Ph.D. studies at Central South University, Fan is positioned to advance research in mineral systems and geochemical modeling.

Profile

Education 🎓

Zhi-Wei Fan completed his Bachelor’s degree in Geosciences from Central South University in 2021, where he began cultivating his academic focus on mineralogy and geochemistry. He continued at the same institution for his Master’s degree from 2021 to 2023, further refining his expertise in petrology and rare-metal-bearing pegmatites. His postgraduate education emphasized advanced geochemical tools and mineralogical techniques, which laid the groundwork for his transition into a Ph.D. program in 2023 at Central South University. Throughout his academic journey, Fan has received multiple honors for academic excellence and research performance, including high-ranking scholarships and recognition for his thesis. His comprehensive education equips him with strong theoretical knowledge and practical laboratory skills in micro-analysis, mineral crystal chemistry, and isotopic geochemistry. His education has supported active research participation in key national academic forums and contributions to high-impact geological publications, marking him as a rising scholar in ore deposit studies.

Experience 👨‍🏫

Zhi-Wei Fan has cultivated significant research and academic experience at Central South University across his undergraduate, master’s, and Ph.D. programs. His fieldwork and lab experience include mineralogical investigations in the Baishawo Be-Li-Nb-Ta pegmatite deposit, application of Rayleigh fractionation models, and characterization of critical elements using advanced techniques like LA-ICP-MS and XRD. He has presented at national conferences, including the 16th and 17th China Ore Deposits Conferences, showcasing his insights on magmatic-hydrothermal systems. Fan has also demonstrated leadership as an Outstanding Student Cadre and made contributions to public service as a volunteer during epidemic control efforts. His experience extends to interdisciplinary collaboration, analytical modeling of mineral evolution, and academic publishing. He has earned several scholarships, reflecting his consistent academic performance and research impact. As an active scholar and field researcher, he combines strong scientific methodology with critical analytical skills, making meaningful contributions to the field of economic geology.

Awards & Recognitions 🏅

Zhi-Wei Fan has been recognized with numerous academic and service honors at Central South University. He earned the Third Prize Scholarship for three consecutive years (2018, 2019, 2020), Second Prize in 2022, and the prestigious First Prize Scholarship in 2023. His leadership was honored with the “Outstanding Student Cadre” and “Outstanding Member” awards in 2020. His social contributions were acknowledged with the “Outstanding Volunteer for Epidemic Prevention and Control” award. He received the “Outstanding Thesis” award in 2021 and the “Favorite On-Site Display Project Award” at the 2021 Innovation and Entrepreneurship Conference. These awards reflect his strong academic standing, leadership capabilities, community service, and research innovation. Fan has consistently demonstrated excellence across academics, extracurricular activities, and public service, positioning him as a well-rounded and impactful scholar in the geoscience community.

Research Interests 🔬

Zhi-Wei Fan’s research focuses on the geochemistry and petrology of ore-forming systems, especially magmatic-hydrothermal environments associated with rare metal mineralization. He specializes in characterizing mineral chemistry and zonation in pegmatites to understand enrichment mechanisms of critical elements such as Li, Be, Nb, and Ta. His work employs micro-analytical tools like LA-ICP-MS, EMPA, and SC-XRD to investigate minerals like beryl, tourmaline, and garnet. Fan develops Rayleigh fractionation models to decipher the internal and regional zonation of pegmatites. He is particularly interested in crystal chemistry and fluid-rock interaction processes that govern rare metal concentration and transport. His recent publications address crystal fractionation in beryl and the genesis of granite-pegmatite systems in South China. His research contributes to decoding mineral evolution patterns and advancing resource exploration strategies for rare metal deposits, making him a promising researcher in economic geology and mineral exploration.

Publications
  • Zhiwei Fan, Yiqu Xiong. 2024. Crystallographic insights and crystal fractionation
    simulations of alkali-and water-bearing beryl: Implications for magmatic–hydrothermal
    evolution and Be enrichment mechanisms. Ore Geology Reviews, 106278

 

  • Yiqu Xiong, Zhiwei Fan. 2024. Genetic linkage between parent granite and zoned rare
    metal pegmatite in the Renli-Chuanziyuan granite-pegmatite system, South China.
    Geological Society of America Bulletin.

  • Textural and chemical characteristics of beryl from the Baishawo Be-Li-Nb-Ta pegmatite deposit, Jiangnan Orogen: Implication for rare metal pegmatite genesis

    Ore Geology Reviews
    2022-09-06 | Journal article
    DOI: 10.1016/j.oregeorev.2022.105094
    Part of ISSN: 0169-1368
    CONTRIBUTORS: Zhi-Wei Fan; Xiong Yiqu; Yong-Jun Shao; Chun-Hua Wen

Behzad Zamani | Earth sciences | Best Researcher Award

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Dr. Behzad Zamani | Earth sciences | Best Researcher Award

Behzad Zamani G. is a seasoned geoscientist 🌍 with expertise in tectonics, geohazards, morphotectonics, fault studies & hydrogeology 💧, combining academic excellence with over two decades of research, teaching, and applied geophysical exploration 🔎 in Iran 🇮🇷 and France 🇫🇷, contributing deeply to tectonic stress modeling and geological problem-solving 🧠.

Profile

Education 🎓

Behzad earned a Ph.D. in Geology (Tectonics) from University of Shiraz & Pierre et Marie Curie 🇫🇷 (2008) 🎯, an M.Sc. in Geology (Tectonics) from University of Tabriz 🇮🇷 (2000) 📘 focusing on seismo-tectonics & GIS 🌐, and a B.Sc. in Geology from University of Tabriz (1997) 📚, shaping a solid academic foundation in Earth sciences 🌋.

Experience 👨‍🏫

Behzad boasts a stellar academic & industry journey 🌍, serving as Associate Professor at University of Tabriz since 2008 🎓, Invited Professor at University of Strasbourg 🇫🇷 (2023-24) 📢, Research Assistant at GeoAzur Lab 🇫🇷 (2005-08) 🔬, plus over a decade in hydrogeology, geophysics & tectonic consultancy roles across Iran’s major infrastructure projects 🚧.

Awards & Recognitions 🏅

Behzad earned the highest Ph.D. dissertation grade 🥇 (2009), was recognized for a “Hot Paper” in Geodynamics 📜 (2007), secured Iran’s national Ph.D. research fund 💡 (2003), ranked 2nd in the national Ph.D. entrance exam 🧠 (2002), 1st in M.Sc. at Tabriz 🎖️ (2001), and topped Shiraz University’s Ph.D. test 📚 (2001).

Research Interests 🔬

Behzad’s research deciphers Earth’s stress fields 🌍, fault systems ⚡, morphotectonics 🏞️, hydrogeology 💧, and geohazards 🧯 using geophysical data modeling 🖥️, seismo-tectonic analysis 📊, remote sensing 🛰️ & GIS, offering vital solutions to engineering-geological challenges 🏗️ and water exploration in hard rock terrains ⛏️.

Publications

  • Present-day crustal deformation of the Caucasus and Northern Iran constrained by InSAR time series  

    2025-01-20 | Preprint
    DOI: 10.5194/egusphere-egu24-13308
    CONTRIBUTORS: Zaur Bayramov; Renier Viltres; Cecile Doubre; Alessia Maggi; Frederic Masson; Behzad Zamani; Marie-Pierre Doin

  • Geodynamics and tectonic stress model for the Zagros fold–thrust belt and classification of tectonic stress regimes

    Marine and Petroleum Geology
    2023-09 | Journal article
    DOI: 10.1016/j.marpetgeo.2023.106340
    CONTRIBUTORS: Behzad Zamani G.

zeenat khadim | Remote Sensing | Best Research Article Award

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Dr. zeenat khadim | Remote Sensing | Best Research Article Award

Zeenat Khadim Hussain is a passionate PhD Researcher 🧠 at Wuhan University, China 🇨🇳 specializing in Photogrammetry and Remote Sensing 🌍. With strong analytical skills and innovative thinking 💡, she explores the fusion of Deep Learning 🤖 with geospatial technologies for solving real-world problems 🌱. Her research has made contributions to urban sustainability 🌇, building detection 🏢, and solar energy potential mapping ☀️. Zeenat’s scholarly journey is marked by impactful publications 📚 in reputed journals, advancing cutting-edge solutions for environmental monitoring 🌐. She actively engages in international research collaborations 🤝 and industry-driven projects like Pakistan’s Flood Emergency Reconstruction 🌊. Zeenat remains committed to promoting sustainable development goals (SDGs) through scientific research 📖 and practical applications. Her work reflects dedication to empowering urban resilience 🏙️ and advancing geospatial science 📡 for a smarter and greener future 🌿.

Profile

Education 🎓

Zeenat Khadim Hussain holds an exceptional academic background 🏅. Currently pursuing a PhD in Photogrammetry and Remote Sensing 🛰️ at Wuhan University, China 🇨🇳, her education combines theoretical excellence 📚 and real-world applications 🌍. Prior to this, she achieved commendable academic success in her undergraduate and postgraduate studies 🎓, where she cultivated her passion for geospatial technologies 🔭 and machine learning 💻. Through rigorous training at Wuhan University’s prestigious School of Remote Sensing 🏫, she honed her research methodology, scientific writing ✍️, and computational modeling skills 🧠. Her strong educational foundation underpins her ability to bridge the gap between advanced technology 💡 and environmental sustainability 🌱, positioning her as a forward-thinking researcher 🔬 with global perspectives 🌐. Zeenat’s academic record reflects her commitment to lifelong learning 📖 and excellence in scientific inquiry 🏆.

Experience 👨‍🏫

Zeenat Khadim Hussain has rich research and project experience 🏗️, including 3+ major research projects on urban analysis 🏙️, solar irradiance assessment ☀️, and building footprint extraction 🧱 using deep learning algorithms 🤖 and Sentinel-2 satellite imagery 🛰️. She has also contributed to Pakistan’s Flood Emergency Reconstruction and Resilience Project 🌊, applying her expertise to real-world disaster management 🌐. Zeenat has authored 6 publications in high-impact journals (SCI, Scopus) 📰 and is continuously expanding her research collaborations 🤝. Her commitment to developing AI-powered geospatial solutions 💡 for urban sustainability and disaster resilience 🚧 has made her a rising star 🌟 in remote sensing. From conceptualizing research frameworks 📋 to deploying machine learning pipelines ⚙️, Zeenat’s experience spans both academic and applied research environments 🧪, establishing her as a dynamic and results-oriented researcher 🔬.

Awards & Recognitions 🏅

Zeenat Khadim Hussain’s academic journey is distinguished by multiple honors 🎖️ and global recognition 🌐. Her groundbreaking work on building detection and rooftop solar assessment 🏢☀️ has earned commendation in academic conferences and journal publications 📚. She has been nominated for competitive research awards 🥇 such as the Best Research Scholar Award 🧠 and Excellence in Research 🌟, highlighting her commitment to scientific innovation 🔬. Her practical impact through Pakistan’s Flood Reconstruction Project 🌊 and interdisciplinary collaborations 🤝 showcases her leadership potential and societal contribution 💪. Zeenat’s consistent publication record in reputed platforms 📰 and successful completion of funded research projects 💸 emphasize her growing academic influence 🚀. Her career reflects resilience, intellectual curiosity 🧠, and a passion for using technology to solve environmental and urban challenges 🌍.

Research Interests 🔬

Zeenat Khadim Hussain’s research focuses on the fusion of Deep Learning 🤖, Photogrammetry 📏, and Remote Sensing 🛰️ to tackle urban, environmental, and energy-related challenges 🌍. She specializes in building footprint detection 🏢, rooftop solar potential mapping ☀️, and disaster risk management 🌊 using geospatial data and AI algorithms ⚙️. Her work bridges the gap between theoretical modeling 📐 and real-world applications in sustainable urban planning 🌱 and renewable energy assessment ⚡. She is especially passionate about translating satellite data into actionable insights 🔎 for climate adaptation, resilience, and urban growth 📊. Zeenat’s latest research introduces deep learning architectures 🧠 that enhance rooftop extraction accuracy, as published in Remote Sensing Applications: Society and Environment 🌐. Her aim is to push the frontiers of automated geospatial analysis 🚀, enabling smarter and greener cities 🏙️ for future generations 🌿.

Publications 

  • Spatiotemporal optimization for communication-navigation-sensing collaborated emergency monitoring

    International Journal of Digital Earth
    2025-12-31 | Journal article
    DOI: 10.1080/17538947.2025.2475517
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: xicheng tan; Bocai Liu; Chaopeng Li; Zeenat Khadim Hussain; Kaiqi Wang; Kai Wang; Mengyan Ye; Danyang Yang; Zhiyuan Mei

  • A Novel Architecture for Building Rooftop Extraction Using Remote Sensing and Deep Learning

    Remote Sensing Applications: Society and Environment
    2025-04 | Journal article | Author
    DOI: 10.1016/j.rsase.2025.101551
    Part ofISSN: 2352-9385
    CONTRIBUTORS: zeenat khadim; Jiang Congshi; Muhammad Adrees; Hamza Chaudhary; Rafia Shafqa

  • 3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization

    Geo-spatial Information Science
    2025-03-11 | Journal article
    DOI: 10.1080/10095020.2025.2472006
    Part of ISSN: 1009-5020
    Part of ISSN: 1993-5153
    CONTRIBUTORS: Yumin Chen; xicheng tan; Jinguang Jiang; Xiaoliang Meng; Zeenat Khadim Hussain; Jianguang Tu; Huaming Wang; You Wan; Zongyao Sha

Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

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Prof. Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

Mingshun Jiang is a professor at the School of Control Science and Engineering, Shandong University 🎓. He is a doctoral supervisor and a young expert of Mount Taishan Scholars 🌟. He serves as the director of the Shandong Engineering Research Center for Intelligent Sensor and Detection Technology 🔬 and deputy director of the Institute of Intelligent Perception 🏛️. His research primarily focuses on intelligent sensors and detection technologies, with over 20 funded projects, including the National Natural Science Foundation and the National Key R&D Program 🏆. He has authored 60+ high-level academic papers in renowned journals 📑. His innovative contributions aim at monitoring complex structural states in high-end equipment 🚀. With extensive industry collaborations, his work has applications in aerospace, rail transit, and military technology 🛰️🚆.

Profile

Education 🎓

Mingshun Jiang earned his doctoral degree in Control Science and Engineering from Shandong University 🎓. His academic journey focused on developing intelligent sensor systems and detection methodologies 📡. His research expertise was cultivated through interdisciplinary learning, integrating control science, artificial intelligence, and structural health monitoring 🤖. His doctoral research emphasized advanced ultrasonic-guided wave detection and probabilistic diagnostic imaging techniques 🏗️. Jiang’s educational background provided him with expertise in designing smart sensor networks, optimizing detection mechanisms, and enhancing structural health monitoring systems ⚙️. With strong mathematical and engineering foundations, he developed novel algorithms for real-time damage localization and predictive maintenance 📊. His continuous learning and research efforts have been instrumental in bridging technological gaps in aerospace, rail transit, and high-end industrial applications 🚆✈️.

Experience 👨‍🏫

Mingshun Jiang has extensive research and academic experience, currently serving as a professor at Shandong University 🏛️. He has led over 20 major research projects, including the National Natural Science Foundation and National Key R&D Program 🌍. As the director of the Shandong Engineering Research Center, he focuses on intelligent sensor development and detection technologies 🔍. His research has been successfully applied in aerospace, rail transit, and high-end industrial monitoring 🚀🚆. He has supervised numerous doctoral students and collaborated with various enterprises on engineering solutions 🏗️. Jiang has also played a key role in technical verification and real-world applications of his research findings 📡. His leadership in academia and industry-driven research has established him as a leading expert in intelligent perception and structural health monitoring 🏆.

Awards & Recognitions 🏅

Mingshun Jiang has received multiple prestigious recognitions, including being a young expert of Mount Taishan Scholars in Shandong Province 🌟. His work has been supported by national and provincial funding agencies, highlighting his contributions to intelligent sensor technology 🏆. He has been awarded numerous grants under the National Natural Science Foundation and National Key R&D Program 🎖️. Jiang’s research achievements have been recognized through invited talks at leading academic conferences and industry collaborations 🤝. He has served as an executive director of the China Inspection and Testing Society, further solidifying his reputation in the field 🔬. His high-impact publications in top-tier journals have earned him accolades for innovation and research excellence 📑. Jiang continues to receive recognition for his contributions to the monitoring of complex structural states in high-end equipment 🚀.

Research Interests 🔬

Mingshun Jiang’s research focuses on intelligent sensors, structural health monitoring, and detection technology 📡. His work integrates artificial intelligence, probabilistic diagnostic imaging, and ultrasonic-guided wave techniques for real-time damage localization and predictive maintenance 🏗️. Jiang has developed innovative methodologies for monitoring key structural indicators such as boundary loads, damage detection, and component failures 🚆. His research aims to bridge the gap between technological innovation and application in aerospace, rail transit, and industrial monitoring 🛰️. His team has successfully engineered high-end monitoring systems that have undergone technical validation and real-world implementation 🔍. Jiang’s expertise extends to developing smart sensing layers for structural health monitoring, contributing to safer and more efficient industrial systems ⚙️. Through his interdisciplinary research, he continues to advance intelligent perception systems for next-generation monitoring applications 🚀.

Publications 
  • Ruijie Song, Lingyu Sun, Yumeng Gao, Juntao Wei, Chang Peng, Longqing Fan andMingshun Jiang*. Unsupervised temperature-compensated damage localization method based on damage to baseline autoencoder and delay-based probabilistic imaging. Mechanical Systems and Signal Processing, 230: 112649, 2025.
  • Hong Zhang ,Feiyu Teng , Juntao Wei , Shanshan Lv , Lei Zhang , Faye Zhang  and Mingshun Jiang*. Damage Location Method of Pipeline Structure by Ultrasonic Guided Wave Based on Probability Fusion.  IEEE Transactions on Instrumentation and Measurement, 73, 9504914, 2024.
  • . LingyuSun , Juntao Wei , Chang Peng , Wei Hao , Feiyu Teng , Longqing Fan , Lei Zhang , Qingmei Sui  and Mingshun Jiang. Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures.  Mechanical Systems and Signal Processing, 223, 111858, 2024.
  • XiaoshuQin , Shanshan Lv , Changhang Xu , Jing Xie , Lei Jia , Qingmei Sui  and Mingshun Jiang*. Implications of liquid impurities filled in breaking cracks on nonlinear acoustic modulation response: Mechanisms, phenomena and potential applications.  Mechanical Systems and Signal Processing, 200, 110550, 2023.
  • Shanshan Lv , Juntao Wei  and Mingshun Jiang*. Damage localization method for plate-like composite structure based on valid path optimization and search point matching.  Mechanical Systems and Signal Processing, 182, 109562, 2023.

Wanli Xie | ​Geological Engineering | Best Researcher Award

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Dr. Wanli Xie | ​Geological Engineering | Best Researcher Award

📌 Wanli Xie, Ph.D. is a Full Professor and Doctoral Supervisor at Northwest University, China. He directs the Key Laboratory of Loess Dynamic Disaster Prevention and Low-Carbon Remediation and the Research Center for Loess Dynamic Disasters and Ecological Restoration Technologies. A visiting scholar at the University of Colorado Boulder, USA, his expertise spans bulk solid waste treatment, mine environmental restoration, green carbon sequestration, and loess collapsibility. His research has led to national engineering standards, disaster prediction models, and eco-friendly geotechnical innovations. With over 70 publications, 14 patents, and multiple national awards, Dr. Xie plays a key role in geotechnical engineering, geosynthetics, and ecological restoration. He is an active member of international geotechnical societies, an advisor to Shaanxi’s geological disaster prevention, and a leader in geotechnical education and research. 🌍🏗

Profile

Education 🎓

🎓 Ph.D. in Geological Engineering (2004) – Northwest University, China 🏛
🎓 M.S. in Hydraulic Engineering (2001) – Kunming University of Science and Technology 💧
🎓 B.S. in Hydrogeology & Engineering Geology (1997) – Xi’an Engineering University (now Chang’an University) 🌍

Dr. Xie’s education laid the foundation for his expertise in geotechnical engineering, geological hazard prevention, and soil mechanics. His doctoral research focused on loess collapsibility mechanisms and deformation modeling, which later contributed to national railway subgrade standards. His master’s studies in hydraulic engineering provided insights into water-soil interactions, while his undergraduate work in hydrogeology strengthened his understanding of geotechnical risk assessment. His multidisciplinary background supports his contributions to sustainable land restoration, reinforced soil mechanics, and disaster mitigation. 📚🔬

Experience 👨‍🏫

👨‍🏫 Full Professor & Doctoral Supervisor – Northwest University (Present)
🔬 Director, Key Laboratory of Loess Dynamic Disaster Prevention
🌿 Director, Research Center for Loess Dynamic Disasters & Ecological Restoration Technologies
🌎 Visiting Scholar – University of Colorado Boulder, USA
🏗 Expert, Shaanxi Provincial Geological Disaster Prevention Group
🌍 Advisor, Qinling Ecological Conservation Expert Committee

Dr. Xie has pioneered multi-scale analysis of loess collapsibility, developed eco-flexible geosynthetic walls, and optimized loess stabilization techniques. His work has improved railway infrastructure, reduced disaster risks, and promoted sustainable restoration. He has also led international accreditation for NWU’s Geological Engineering Program and contributed to national geotechnical standards. His mentorship has produced award-winning students, shaping the next generation of engineers. 🚆🏔

Awards & Recognitions 🏅

🏅 Leading Talent in Sci-Tech Innovation – Shaanxi Special Support Program (2021)
🏆 Shaanxi Provincial Teaching Master (2023)
🔬 Young & Middle-Aged Leading Sci-Tech Talent – Shaanxi (2019)
🥇 National Second Prize for Science and Technology Progress (2020, Rank 4)
🥈 Shaanxi Provincial First Prize for Technological Invention (2019, Rank 2)

Dr. Xie’s pioneering research in loess collapsibility, geotechnical stability, and ecological restoration has been recognized at the national and provincial levels. His contributions to railway subgrade stability and green remediation earned him top scientific and engineering awards. As a renowned educator, he has also been honored for his excellence in teaching and mentorship. 🏅📚🔬

Research Interests 🔬

🧪 Bulk Solid Waste Treatment & Mine Environmental Restoration
🌱 Green Carbon Sequestration & Ecological Remediation
🏗 Reinforced/Improved Soils & Loess Collapsibility
🔬 Multi-Scale Mechanisms of Loess Mechanical Behavior
⚠️ Geological Hazard Prevention & Disaster Prediction

Dr. Xie’s research integrates geotechnical engineering, environmental restoration, and sustainable development. He has developed predictive models for loess deformation, innovated eco-friendly geotechnical materials, and pioneered smart monitoring systems for disaster prevention. His work directly supports climate resilience, infrastructure stability, and sustainable land management. 🌏📡🚧

Publications 

  • Publications(Selected, 70+ Total)
    1. Gao, X., Xie, W.* et al. (2025).Soil& Tillage Research 251:106548. (Top Journal)
    2. Xie, W., Li, P. et al. (2018).CATENA 173:276–288. (SCI Q1)
    3. Zhu, Y., Xie, W.* et al. (2023).Gondwana Research 166:162–180. (SCI Q1)
    4. Xie, W., Guo, Q. et al. (2021).Advances in Civil Engineering 8819015.
    5. Xie, W., Wang, J. (2006).Reinforced Loess Stability Analysis. Shaanxi Science Press. (Monograph)

    Patents: 14 authorized inventions, including:

    • In-situ loess strength testing device (ZL202411473412.7, 2024)
    • Eco-flexible geosynthetic retaining wall system (ZL202410289406.X, 2024)

Jingjun Lin | Laser-Induced Breakdown Spectroscopy (LIBS) | Best Researcher Award

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Dr. Jingjun Lin | Laser-Induced Breakdown Spectroscopy (LIBS) | Best Researcher Award

Dr. Jingjun Lin is a Lecturer at Changchun University of Technology, specializing in laser-induced breakdown spectroscopy (LIBS) and advanced spectral analysis. He has made significant contributions to the field of spectroscopy, materials science, and machine learning-based classification techniques. As an active researcher, he has published extensively in high-impact journals, advancing applications in metal analysis, additive manufacturing, and biomedical diagnostics. With experience as a visiting scholar at Tokushima University, Japan, Dr. Lin continuously explores innovative methodologies to improve spectral detection accuracy. His interdisciplinary expertise bridges spectroscopy, physics, and artificial intelligence. ✨🔬📊

Profile

Education 🎓

  • 🏛 Ph.D. Changchun University of Technology (2015-2018), with research at Huazhong University of Science and Technology (2018)
  • 🎓 Master’s Degree Changchun University of Technology (2012-2015)
  • 🎓 Bachelor’s Degree Changchun University of Technology (2008-2012)
    Dr. Lin’s academic journey reflects a deep commitment to the study of spectroscopy, laser-induced breakdown analysis, and materials science. His research focuses on enhancing spectral analysis techniques and applying machine learning models to spectroscopy data. His Ph.D. research involved novel LIBS applications for material classification and defect detection, further refined during his studies at Huazhong University of Science and Technology. 📚🔍🎯

Experience 👨‍🏫

  • Visiting Scholar Tokushima University, Japan (2023-2024) 🌍
  • Lecturer Changchun University of Technology (2019-Present) 🏛
    Dr. Lin has been an academic professional dedicated to teaching and research in laser-induced breakdown spectroscopy (LIBS), spectroscopic data fusion, and materials analysis. His tenure as a lecturer at Changchun University of Technology involves mentoring students and leading research projects. As a visiting scholar at Tokushima University, he gained international exposure, refining his expertise in advanced laser spectroscopy and its industrial applications. 🧑‍🔬📖✨

Awards & Recognitions 🏅

Dr. Lin has received multiple research grants and recognition for his contributions to spectroscopy and analytical chemistry. His papers have been published in high-impact journals such as Analytical Methods, Journal of Analytical Atomic Spectrometry, and Talanta. His innovative work on LIBS and Raman spectroscopy fusion for lung cancer diagnosis has been acknowledged for its potential clinical applications. 🏅📜🔬

Research Interests 🔬

Dr. Lin specializes in laser-induced breakdown spectroscopy (LIBS), machine learning-enhanced spectral analysis, and multi-modal spectroscopy fusion. His work includes:

  • Metal additive manufacturing defect detection using LIBS 🏭
  • Biomedical applications, including lung cancer classification with spectroscopy 🏥
  • Data fusion of LIBS and Raman spectroscopy for improved accuracy 🤖📊
  • Spectral enhancement techniques for more precise material identification 💡
    His interdisciplinary research aims to push the boundaries of LIBS applications in industry, healthcare, and environmental monitoring. 🚀🔍

Publications 

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Álvaro García Martín es Profesor Titular en la Universidad Autónoma de Madrid, especializado en visión por computadora y análisis de video. 🎓 Obtuvo su título de Ingeniero de Telecomunicación en 2007, su Máster en Ingeniería Informática y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad Autónoma de Madrid. 🏫 Ha trabajado en detección de personas, seguimiento de objetos y reconocimiento de eventos, con más de 22 artículos en revistas indexadas y 28 en congresos. 📝 Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su investigación ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, análisis de secuencias de video y procesamiento de señales multimedia. 📹 Ha sido reconocido con prestigiosos premios y ha participado en múltiples proyectos europeos de innovación tecnológica. 🚀

Profile

Education 🎓

🎓 Ingeniero de Telecomunicación por la Universidad Autónoma de Madrid (2007). 🎓 Máster en Ingeniería Informática y Telecomunicaciones con especialización en Tratamiento de Señales Multimedia en la Universidad Autónoma de Madrid (2009). 🎓 Doctor en Ingeniería Informática y Telecomunicación por la Universidad Autónoma de Madrid (2013). Su formación ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). 🌍 Durante su doctorado, recibió la beca FPI-UAM para la realización de su investigación. Su sólida formación académica le ha permitido contribuir significativamente al campo del análisis de video y visión por computadora, consolidándose como un experto en la detección, seguimiento y reconocimiento de eventos en secuencias de video. 📹

Experience 👨‍🏫

🔬 Se unió al grupo VPU-Lab en la Universidad Autónoma de Madrid en 2007. 📡 De 2008 a 2012, fue becario de investigación (FPI-UAM). 🎓 Entre 2012 y 2014, trabajó como Profesor Ayudante. 👨‍🏫 De 2014 a 2019, fue Profesor Ayudante Doctor. 📚 De 2019 a 2023, ocupó el cargo de Profesor Contratado Doctor. 🏛️ Desde septiembre de 2023, es Profesor Titular en la Universidad Autónoma de Madrid. 🏆 Ha participado en múltiples proyectos europeos sobre videovigilancia, transmisión de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. 🚀 Ha realizado estancias de investigación en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su experiencia docente abarca asignaturas en Ingeniería de Telecomunicaciones, Ingeniería Informática e Ingeniería Biomédica.

Research Interests 🔬

🎯 Su investigación se centra en la visión por computadora, el análisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. 📹 Especialista en detección de personas, seguimiento de objetos y reconocimiento de eventos en video. 🧠 Desarrolla algoritmos de aprendizaje profundo y visión artificial para mejorar la seguridad y automatización en ciudades inteligentes. 🏙️ Ha trabajado en proyectos sobre videovigilancia, transmisión multimedia y detección de anomalías en video. 🔬 Su investigación incluye procesamiento de imágenes, análisis semántico y redes neuronales profundas. 🚀 Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. 🌍 Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidándose como un referente en el campo de la visión por computadora. 📜

Awards & Recognitions 🏅

🥇 Medalla “Juan López de Peñalver” 2017, otorgada por la Real Academia de Ingeniería. 📜 Reconocimiento por su contribución a la ingeniería española en el campo de la visión por computadora y análisis de video. 🏛️ Ha recibido financiación para múltiples proyectos de investigación europeos y nacionales. 🔬 Ha participado en iniciativas de innovación en videovigilancia y análisis de video para seguridad. 🚀 Sus contribuciones han sido publicadas en las principales conferencias y revistas científicas del área. 📚 Su trabajo ha sido citado más de 4500 veces y cuenta con un índice h de 16 en Google Scholar. 📊

Publications 

1. Rafael Martín-Nieto, Álvaro García-Martín, Alexander G. Hauptmann, and Jose. M.
Martínez: “Automatic vacant parking places management system using multicamera
vehicle detection”. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martín-Nieto, Álvaro García-Martín, Jose. M. Martínez, and Juan C. SanMiguel:
“Enhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximization”. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Álvaro García-Martín, Juan C. SanMiguel and Jose. M. Martínez: “Coarse-to-fine adaptive
people detection for video sequences by maximizing mutual information”. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós and Álvaro GarcíaMartín: “Semantic-Aware Scene Recognition”. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Álvaro García-Martín, Marcos Escudero Viñolo, Jose M. Martinez, Jesus
Bescós, Jesus Peñuela, Juan Carlos Martinez, Gonzalo Alvis: “Towards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city maps”. Waste Management, June 2022.

6. Javier Montalvo, Álvaro García-Martín, Jesus Bescós: “Exploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Álvaro García-Martín, Jose M. Martinez, Jesus Bescós: “Enhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Information”. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo and Alvaro GarciaMartin: “On exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic data”. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos EscuderoViñolo: “Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensus”. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Álvaro García-Martín, José M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Juan C. SanMiguel:
“Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesis”. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira, Álvaro García-Martín:
“Improved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuning”. Applied Intelligence. Aug 2024.

13. Javier Galán, Miguel González, Paula Moral, Álvaro García-Martín, Jose M. Martinez:
“Transforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identification”. Waste Management, Feb 2025.

Joshua Coste | Movement Ecology | Young Scientist Award

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Mr. Joshua Coste | Movement Ecology | Young Scientist Award

Joshua Coste is a marine biologist and ecologist specializing in movement ecology, navigation behavior, and population genetics. He completed his BEST-ALI master’s program at the University of La Réunion and has conducted research at the Environment and Sustainability Institute (University of Exeter). His work focuses on seabird homing navigation and population connectivity, integrating tracking and genetic techniques. He has been involved in international fieldwork, collaborating with the ENTROPIE lab, SEOR, and the UK Chagos Archipelago research team. His latest research, published in Animal Behaviour, highlights the adaptive navigation of red-footed boobies. He has presented at international conferences, including the 16th International Seabird Group Conference.

Profile

Education 🎓

Joshua earned his Master’s in Biodiversity, Ecology, and Evolution from the University of La Réunion, specializing in tropical, aquatic, coastal, and island ecosystems (2022–2024). His bachelor’s degree in Biology-Ecology was from Nantes University, France (2019–2022). He also holds a scientific high school diploma with honors, specializing in Engineering Sciences. His academic training includes practical work in marine biology, genetic analysis, and ecological modeling. His education has equipped him with expertise in spatial analysis, seabird tracking, and conservation genetics.

Experience 👨‍🏫

Joshua has completed multiple research internships worldwide. At the University of Exeter, he studied the homing navigation of red-footed boobies using GPS data. At the University of La Réunion, he analyzed the genetic structure of Barau’s Petrel colonies. His internship at the Federal University of Rio Grande do Norte, Brazil, involved studying coral competition. At IFREMER Bretagne, he worked on archaea cultures in extreme environments. He also supported students with disabilities at Handisup, Nantes. His voluntary experience includes seabird monitoring with ENTROPIE, coral reef assessments, and conservation work with BESTRUN.

Research Interests 🔬

Joshua specializes in movement ecology, behavioral ecology, and population genetics. His research explores seabird navigation, homing efficiency, and environmental adaptation. His study on red-footed boobies demonstrated how seabirds adjust flight paths based on daylight constraints. He has worked on connectivity between seabird colonies, philopatry’s influence on genetic diversity, and coral reef ecosystem dynamics. His interdisciplinary approach combines fieldwork, genetic analysis, and computational modeling.

Awards & Recognitions 🏅

Joshua was nominated for the Young Scientist Award by the International Cognitive Scientist Awards. His research on seabird navigation was recognized at the 16th International Seabird Group Conference. His Animal Behaviour publication has gained academic recognition. He actively contributes to international collaborations in marine biology and conservation.

Publications 📚

  • Homing navigation is optimized to diurnal constraints in a tropical seabird, the red-footed booby

    Animal Behaviour
    2025-04 | Journal article
    DOI: 10.1016/j.anbehav.2025.123116
    CONTRIBUTORS: Joshua Coste; Stephen C. Votier; Ruth E. Dunn; Robin Freeman; Malcolm A. Nicoll; Peter Carr; Hannah Wood; Alice M. Trevail

Raveendra Pilli | Image Processing | Best Researcher Award

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Mr. Raveendra Pilli | Image Processing | Best Researcher Award

He mentored B.Tech. projects focused on the early detection of Alzheimer’s Disease. One project involved utilizing multi-modality neuroimaging techniques, where MRI and PET images were collected from the OASIS database, preprocessed, and robust features were extracted for classification. MATLAB and the SPM-12 toolbox were used for this task. Another project focused on the early detection of Alzheimer’s Disease using deep learning networks, where an MRI dataset from the ADNI database was collected, preprocessed, and the performance was compared with baseline algorithms. For this project, he used MATLAB and Python.

NIT-Silchar, India

Profile

Education

A dedicated research scholar with a Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar (Thesis Submitted, CGPA 9.0), specializing in brain age prediction and early detection of neurological disorders using neuroimaging modalities. With extensive teaching experience, a strong passion for research, and a proven ability to develop engaging curricula, deliver effective lectures, and guide students toward academic success, I am committed to contributing to the field through research, publications, and presentations. My academic journey includes an M.Tech. from JNTU Kakinada (76.00%, 2011) and a B.Tech. from JNTU Hyderabad (65.00%, 2007), along with a strong foundational background in science, having completed 10+2 (MPC) with 89.00% in 2003 and SSC with 78.00% in 2001.

Work experience

He worked as a Junior Research Fellow at the National Institute of Technology, Silchar, Assam, from July 2021 to June 2023, where he assisted professors with course delivery for Basic Electronics, conducted laboratory sessions, graded assignments, and provided office hours for student support. From July 2023 to December 2024, he served as a Senior Research Fellow at the same institute, taking on additional responsibilities, including mentoring B.Tech. projects and assisting with Digital Signal Processing laboratory duties. Prior to his research roles, he was an Assistant Professor at SRK College of Engineering and Technology, Vijayawada, Andhra Pradesh, where he taught courses such as Networks Theory, Digital Signal Processing, RVSP, SS, and LICA. He utilized innovative teaching methods, including active learning techniques, to enhance student engagement and learning outcomes. He also mentored undergraduate research projects in image processing and received positive student evaluations for his teaching effectiveness.

Publication