Seho Lee | Data Science | Best Researcher Award

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Prof. Seho Lee | Data Science | Best Researcher Award

Assistant Professor at University of Ulsan, South Korea

Dr. Seho Lee, Assistant Professor in the Department of AI Convergence, is a distinguished researcher specializing in neuroengineering, brain–computer interfaces (BCI), and clinical artificial intelligence. He earned his Ph.D. in Brain and Cognitive Engineering from Korea University, following advanced degrees in biomedical engineering and electrophysics. His research focuses on advanced neurophysiological signal analysis, AI-based clinical decision support, and predictive modeling for neurological disorders. Dr. Lee has played key roles in multiple international, grant-funded projects and has published extensively in high-impact journals, including IEEE and Frontiers in Neuroscience. Holding several patents in BCI and biomedical AI, he is also an active member of professional bodies like IEEE. With a strong commitment to innovation, interdisciplinary collaboration, and mentorship, Dr. Lee continues to advance technologies that bridge engineering and clinical applications for global impact.

Professional Profile

Google Scholar | Scopus Profile | ORCID Profile

Education

Dr. Seho Lee holds a Ph.D. in Brain and Cognitive Engineering from Korea University, where he specialized in advanced neurophysiological signal processing and AI applications in neuroscience. He earned his Master’s degree in Biomedical Engineering from Hanyang University, focusing on biomedical signal analysis and neural interface technologies. His academic journey began with a Bachelor’s degree in Electrophysics from Kwangwoon University, where he developed a strong foundation in physics and electronics. This diverse educational background has enabled Dr. Lee to integrate engineering, computer science, and neuroscience in his research. His training at leading Korean institutions provided both theoretical depth and practical expertise, positioning him to contribute meaningfully to the rapidly evolving fields of neuroengineering, artificial intelligence, and clinical technology innovation at both national and global levels.

Experience

Dr. Lee currently serves as an Assistant Professor in the Department of AI Convergence, where he leads research at the intersection of neuroscience, artificial intelligence, and clinical applications. He has been a key contributor to multiple multi-year, grant-funded projects, collaborating with hospitals, research institutes, and industry partners. His work spans developing brain–computer interface (BCI) systems, AI-powered clinical decision support tools, and predictive models for neurological disorder progression. Dr. Lee’s experience includes managing large datasets, integrating wearable and invasive neural monitoring technologies, and designing patient-centric solutions. His portfolio also features active involvement in interdisciplinary teams, student supervision, and community-driven scientific initiatives. Through his extensive project work and academic contributions, he has developed a global research perspective while ensuring his innovations address practical, real-world needs in healthcare and rehabilitation technologies.

Research Interest

Dr. Lee’s research interests focus on neuroengineering, brain–computer interfaces (BCI), clinical artificial intelligence, and neuroscience applications for patient rehabilitation. He specializes in advanced analysis of continuously measured neurophysiological signals, quantitative brain network modeling, and AI-driven diagnostic tools. His work aims to create practical solutions for individuals with neurological impairments by enhancing brain–machine communication and predictive clinical analytics. Key topics include the use of AI for clinical data interpretation, development of real-time decision support systems, and non-invasive neural stimulation methods. Dr. Lee is also deeply engaged in studying pathological progression in neurological disorders, with the goal of early intervention. His multidisciplinary approach combines engineering, cognitive science, and medicine, contributing to breakthroughs that directly improve patient quality of life and expand the capabilities of modern neurotechnology.

Award and Honor

While building his academic career, Dr. Lee has earned recognition through successful participation in competitive, nationally funded research projects and prestigious institutional appointments. His innovations in brain–computer interface technology and AI-based biomedical systems have resulted in multiple patents in Korea and the United States, marking him as a thought leader in his domain. He has been invited to present at leading IEEE conferences, demonstrating peer acknowledgment of his contributions. His publications in high-impact journals further attest to his research excellence. The combination of competitive grant awards, impactful publications, and translational research outcomes underscores Dr. Lee’s professional standing. These achievements not only highlight his expertise but also his commitment to advancing science with tangible applications, making him a strong candidate for distinguished research recognitions and honors.

Research Skill

Dr. Lee possesses a comprehensive set of research skills spanning AI model development, neurophysiological data acquisition and analysis, brain network modeling, and clinical signal interpretation. He is adept in programming, machine learning, and deep learning techniques applied to healthcare data, including EEG, EMG, and imaging modalities. His expertise extends to designing and implementing both hardware and software components for brain–computer interfaces, ensuring reliability and usability in clinical environments. Dr. Lee is skilled in statistical modeling, biomedical signal preprocessing, and data visualization for translational research. He has extensive experience collaborating across disciplines, managing international research collaborations, and guiding graduate students in advanced projects. Combined with his patent-proven innovation capabilities, these skills enable him to transform theoretical concepts into impactful neuroengineering solutions that address pressing healthcare challenges.

Publication Top Notes

  • Title: Improved prediction of bimanual movements by a two-staged (effector-then-trajectory) decoder with epidural ECoG in nonhuman primates
    Authors: H Choi, J Lee, J Park, S Lee, K Ahn, IY Kim, KM Lee, DP Jang
    Year: 2018
    Citations: 26

  • Title: Physicochemical factors that affect electroporation of lung cancer and normal cell lines
    Authors: HB Kim, S Lee, Y Shen, PD Ryu, Y Lee, JH Chung, CK Sung, KY Baik
    Year: 2019
    Citations: 24

  • Title: Effects of actin cytoskeleton disruption on electroporation in vitro
    Authors: HB Kim, S Lee, JH Chung, SN Kim, CK Sung, KY Baik
    Year: 2020
    Citations: 19

  • Title: Importance of reliable EEG data in motor imagery classification: Attention level-based approach
    Authors: S Lee, YT Kim, SO Hwang, H Kim, DJ Kim
    Year: 2020
    Citations: 7

  • Title: Long-term evaluation and feasibility study of the insulated screw electrode for ECoG recording
    Authors: H Choi, S Lee, J Lee, K Min, S Lim, J Park, K Ahn, IY Kim, KM Lee
    Year: 2018
    Citations: 6

  • Title: Decoding saccadic directions using epidural ECoG in non-human primates
    Authors: J Lee, H Choi, S Lee, BH Cho, K Ahn, IY Kim, KM Lee, DP Jang
    Year: 2017
    Citations: 6

  • Title: Reduced burden of individual calibration process in brain–computer interface by clustering the subjects based on brain activation
    Authors: YT Kim, S Lee, H Kim, SB Lee, SW Lee, DJ Kim
    Year: 2019
    Citations: 4

  • Title: Right hemisphere lateralization in neural connectivity within fronto-parietal networks in non-human primates during a visual reaching task
    Authors: J Lee, H Choi, K Min, S Lee, KH Ahn, HJ Jo, IY Kim, DP Jang, KM Lee
    Year: 2018
    Citations: 3

  • Title: Investigating the effect of mindfulness training for stress management in military training: the relationship between the autonomic nervous system and emotional regulation
    Authors: S Lee, JH Kim, H Kim, SH Kim, SS Park, CW Hong, KT Kwon, SH Lee
    Year: 2025
    Citations: 2

  • Title: Effects of altered functional connectivity on motor imagery brain–computer interfaces based on the laterality of paralysis in hemiplegia patients
    Authors: S Lee, H Kim, JB Kim, DJ Kim
    Year: 2023
    Citations: 2

  • Title: Classification of the motion artifacts in near-infrared spectroscopy based on wavelet statistical feature
    Authors: SB Lee, H Kim, S Lee, HJ Kim, SW Lee, DJ Kim
    Year: 2019
    Citations: 2

  • Title: Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy
    Authors: S Lee, H Kim, JH Kim, M So, JB Kim, DJ Kim
    Year: 2024

Conclusion

Dr. Seho Lee represents the next generation of innovators at the convergence of neuroscience, artificial intelligence, and clinical technology. His unique blend of academic excellence, research experience, and practical innovation has positioned him to make transformative contributions to healthcare and rehabilitation sciences. With numerous high-impact publications, patents, and international collaborations, he has demonstrated the ability to move ideas from concept to clinical reality. His work directly improves the lives of individuals with neurological disorders while advancing the scientific understanding of brain–machine interaction. As both a leader and mentor, Dr. Lee continues to inspire new research directions in neuroengineering. His commitment to interdisciplinary collaboration, societal impact, and global engagement makes him an exemplary figure deserving of recognition as a top researcher in his field.

Nan Zhang | Data Science | Best Researcher Award

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Assist. Prof. Dr. Nan Zhang | Data Science | Best Researcher Award

Department Head at Wuxi Institute of Technology, China

Dr. Nan Zhang 🎓, currently serving as the Head of Department at Wuxi Institute of Technology 🏫, is an accomplished researcher with a Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology 🧠. Her expertise spans across pattern recognition, computer vision, and machine learning 🤖. With 9 high-impact journal publications 📚, 18 patents 🔬, and two published books 📘, she has made notable contributions to both theoretical research and applied innovation. Dr. Zhang’s work includes advanced AI applications in medical thermal imaging and exercise prescription optimization for Type 2 diabetes patients 💡💊. Her involvement in 8 research projects and 3 industry collaborations reflects strong academic-industry synergy 🤝. Dr. Zhang exemplifies innovation, leadership, and a commitment to real-world impact, making her a valuable asset to the global research community 🌏.

Professional Profile 

ORCID Profile

🎓 Education

Dr. Nan Zhang earned her Ph.D. in Pattern Recognition and Intelligence System from the esteemed Nanjing University of Science and Technology 🏛️ in 2013. Her academic journey reflects a strong foundation in mathematics, computing, and system intelligence 📐💻. With a passion for innovation and analytical precision, she focused her doctoral research on advanced feature extraction and intelligent algorithm design 🧠. Her educational background has provided her with a solid base to explore diverse AI applications, especially in computer vision and medical technology 💊. This scholarly pursuit laid the groundwork for her prolific career as a researcher, educator, and innovator, enabling her to bridge the gap between theory and real-world applications 🌐.

👩‍🏫 Professional Experience

Currently the Department Head and Associate Professor at the School of Control Engineering, Wuxi Institute of Technology 🏫, Dr. Nan Zhang brings over a decade of academic excellence and research leadership. She has successfully led 8 major research projects and collaborated on 3 industry consultancy initiatives 🧪🤝. Her dual expertise in academic rigor and industrial relevance enables her to train students and researchers to tackle real-world challenges. Under her leadership, the department has expanded its focus on AI integration and technological innovation 🌟. She also contributes actively as a mentor, curriculum designer, and academic reviewer, emphasizing both theory and its practical implementation 💼📘.

🔍 Research Interest

Dr. Zhang’s primary research interests revolve around Pattern Recognition, Computer Vision, and Machine Learning 🤖. Her work focuses on developing algorithms that enable intelligent systems to perceive, interpret, and act on complex data, especially in dynamic environments. Recently, she has explored deep learning applications in medical thermal imaging for diagnostic improvements 🩻 and adaptive exercise prescriptions for diabetic patients 🏃‍♀️💉. Her research bridges the gap between AI theory and practical healthcare technology, aiming to make intelligent systems more precise and accessible. She is passionate about advancing human-centric AI that not only predicts but enhances decision-making across health and industrial sectors 🧬🧑‍⚕️.

🏅 Awards and Honors

With a track record of excellence, Dr. Nan Zhang has been recognized through multiple patents (18 total) 🧾 and journal publications (9 indexed in SCI/Scopus) 📚. Her books (ISBN: 978-7-121-45435-6, 978-620-2-30759-8) showcase her thought leadership in emerging technologies. While not explicitly listed, her portfolio and credentials reflect eligibility for prestigious honors like the Best Researcher Award 🌟. Her contributions in AI-enhanced diagnostics and intelligent system modeling highlight her impact in both academic and applied domains 🧠🔬. These recognitions affirm her continued commitment to innovative, responsible, and impactful science on both national and global platforms 🌍🏆.

🛠️ Research Skills

Dr. Zhang excels in AI algorithm design, feature extraction, deep learning, and image analysis 🔧🧠. She is proficient in various programming and analytical tools used in computer vision and pattern recognition, such as Python, MATLAB, and TensorFlow 💻📊. Her strength lies in bridging theoretical models with functional systems, particularly in biomedical imaging and intelligent diagnostics 🔬🖥️. In addition, she brings expertise in scientific writing, patent drafting, and industry collaboration, enabling her to work effectively across multidisciplinary teams. Her ability to lead and innovate in both solo and collaborative research settings reflects her technical depth and strategic foresight 🔍🌐.

Publications Top Note 📝

  • Title: Medical image inpainting with edge and structure priors
    Authors: Qianna Wang, Yi Chen, Nan Zhang, Yanhui Gu
    Year: 2021
    DOI / Source: 10.1016/j.measurement.2021.110027
    Published in: Measurement (Elsevier)
    Citation Source: Crossref

  • Title: Robust H∞ filtering for Markovian jumping static neural networks with time-varying delays
    Authors: Aodong Zhao, Nan Zhang, Maolong Xi, Jun Sun, Meiyan Dong
    Year: 2020
    DOI / Source: 10.1177/1748302620931340
    Published in: Journal of Algorithms & Computational Technology (SAGE)
    Citation Source: Crossref

  • Title: Feature extraction based on Low-rank affinity matrix for biological recognition
    Authors: Nan Zhang, Yi Chen, Maolong Xi, Fangqin Wang, Yanwen Qu
    Year: 2018
    DOI / Source: 10.1016/j.jocs.2018.06.001
    Published in: Journal of Computational Science (Elsevier)
    Citation Source: Crossref

  • Title: Low-rank representation based discriminative projection for robust feature extraction
    Authors: Nan Zhang, Jian Yang
    Year: 2013
    DOI / Source: 10.1016/j.neucom.2012.12.012
    Published in: Neurocomputing (Elsevier)
    Citation Source: Crossref

📝 Conclusion

Dr. Nan Zhang represents a rare blend of academic brilliance, research depth, and societal relevance 🌟. Her contributions in AI-driven medical imaging, pattern recognition, and intelligent diagnostics highlight a career committed to innovation with impact 🚀. With a clear vision and versatile skillset, she continues to advance next-generation technologies that bridge health and machine intelligence 🧬🤖. As a mentor, department leader, and researcher, Dr. Zhang has cultivated a culture of excellence and curiosity, inspiring the next wave of innovators 🌱. Her dedication, accomplishments, and forward-thinking make her an ideal nominee for prestigious recognitions like the Best Researcher Award 🏅🎓.

Fang-Rong Hsu | Data Science | Best Researcher Award

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Prof. Fang-Rong Hsu | Data Science | Best Researcher Award

Professor at Department of Information Engineering and Computer Science/Feng Chia University, Taiwan

Dr. Fang-Rong Hsu 🎓, a distinguished expert in bioinformatics, AI, and medical image processing 🧠🖼️, has made remarkable contributions to interdisciplinary research at the intersection of computer science and healthcare 💻❤️. With a Ph.D. in algorithm design from National Chiao-Tung University and decades of academic leadership 🏫, he has published extensively in top-tier SCIE journals and conferences 🌐📚. His cutting-edge work spans AI-driven diagnostics, vision transformers, and smart health technologies 🤖🧬. As a professor and former director at Feng Chia University, Dr. Hsu has influenced both research and education profoundly 📈👨‍🏫. Known for impactful real-world applications—from cancer detection to IoT-based safety systems—his research continues to shape the future of intelligent healthcare and data science 🚑📊. Dr. Hsu is a leading force in innovation and scientific excellence 🏅.

Professional Profile 

Scopus Profile
ORCID Profile

🎓 Education

Dr. Fang-Rong Hsu earned his Ph.D. in Algorithm Design and Analysis from the Department of Computer Science & Information Engineering at National Chiao-Tung University, Taiwan (1992) 🧠💡. He previously completed his B.S. in Computer Science at the same institution in 1986. His solid academic foundation in theoretical computing laid the groundwork for a multifaceted research career bridging algorithms, AI, and biomedical engineering 📘🖥️. With a deep interest in computational science and problem-solving, his educational journey reflects a strong commitment to both innovation and academic excellence 🎯📚.

🏢 Professional Experience

Dr. Hsu has held multiple prestigious roles across Taiwanese universities 🏫. Currently a Professor in Information Engineering and Computer Science at Feng Chia University, he previously served as Director of the same department (2015–2018) and of the Bioinformatics Research Center (2004–2010) 🧬. His prior appointments include professorships and department chairs at Taichung Healthcare and Providence University, leading initiatives in IT, bioinformatics, and academic administration 🧑‍🏫📈. His career spans over three decades of leadership, education, and research guidance in computing and biomedical applications 🤝🔍.

🔬 Research Interest

Dr. Hsu’s research interests span bioinformatics, parallel processing, and biomedical image processing 🧬📊🖼️. His work focuses on integrating artificial intelligence into healthcare diagnostics, such as deep learning for cancer detection, medical image classification, and behavior analysis in zebrafish 🧠🧪. He is particularly passionate about explainable AI, computer vision, and AIoT applications in medical and public safety domains 🌐⚕️. His cross-disciplinary approach leverages computing power to address complex biological and clinical challenges, resulting in meaningful innovations that bridge academia and practical medicine 🔁💡.

🏅 Awards and Honors

Dr. Hsu is a respected researcher recognized through numerous SCIE-indexed publications and invited international collaborations 🌍📜. While specific awards are not listed, his consistent authorship in prestigious journals such as IEEE Access, Frontiers in Bioengineering, and Diagnostics underscores his high-impact research reputation 🥇📖. His appointment to leadership roles in multiple institutions also reflects strong peer recognition and institutional trust. His global conference presence and academic service contributions further reinforce his standing as an accomplished scholar and thought leader in computer science and bioinformatics 🌟🧑‍🔬.

🛠️ Research Skills

Dr. Hsu brings a powerful blend of skills including deep learning model development (CNN, ResNet, U-Net, ViT), biomedical image analysis, algorithm design, data mining, and AI-driven diagnostic systems 🤖🔬. He excels at integrating computer vision with health informatics and is experienced in parallel and embedded system implementation 🖥️⚙️. Skilled in interdisciplinary collaboration and academic writing, he leads high-impact research teams with strategic direction and technical precision. His technical expertise is matched by his ability to innovate across domains—from zebrafish modeling to smart city technologies 🚀📡.

Publications Top Note 📝

  • Title: Enhancing Safety with an AI-Empowered Assessment and Monitoring System for BSL-3 Facilities
    Authors: Yi-Ling Fan, Ching-Han Hsu, Fang-Rong Hsu, et al.
    Year: 2025
    Source: Heliyon

  • Title: Hybrid Top Features Extraction Model for Detecting X Rumor Events Using an Ensemble Method
    Authors: Taukir Alam, Wei Chung Shia, Fang-Rong Hsu, Taimoor Hassan, Pei-Chun Lin, Eric Odle, Junzo Watada
    Year: 2025
    Source: Journal of Web Engineering

  • Title: Coating Process Control in Lithium-Ion Battery Manufacturing Using Cumulative Sum Charts
    Authors: Min-Chang Liu, Fang-Rong Hsu, Chua-Huang Huang
    Year: 2024
    Citations: 1
    Source: Production Engineering

  • Title: Next-Generation Swimming Pool Drowning Prevention Strategy Integrating AI and IoT Technologies
    Authors: Wei-Chun Kao, Yi-Ling Fan, Fang-Rong Hsu, Chien-Yu Shen, Lun-De Liao
    Year: 2024
    Citations: 3
    Source: Heliyon

  • Title: Complex Event Recognition and Anomaly Detection with Event Behavior Model
    Authors: Min-Chang Liu, Fang-Rong Hsu, Chua-Huang Huang
    Year: 2024
    Citations: 1
    Source: Pattern Analysis and Applications

📌 Conclusion

Dr. Fang-Rong Hsu exemplifies the spirit of research excellence through his deep academic roots, broad interdisciplinary vision, and unwavering dedication to solving real-world challenges 🌐🧠. His contributions to artificial intelligence in healthcare, sustained publication record, and leadership roles make him a standout figure in the scientific community 🏅📚. As a scholar, mentor, and innovator, he continues to influence both present and future generations of researchers in computing, biomedicine, and beyond 🌱🔍. Dr. Hsu is an exemplary candidate for top-tier recognition in research leadership and innovation 🏆🎓.

Shaohuai Feng | Data Science | Best Researcher Award | 2350

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Dr. Shaohuai Feng | Data Science | Best Researcher Award

Student at Feng CGSS, China

Feng Shaohuai is a dedicated researcher focused on sustainable development 🌍 and carbon neutrality 🌱. His work explores how industrial systems, environmental policies, and technological innovation can drive the low-carbon transition, particularly in emerging economies. Through interdisciplinary approaches, Feng bridges academic research with practical solutions to address global climate challenges. His projects, such as sustainable industrial transformation and data-driven environmental policy models, offer actionable frameworks for achieving long-term sustainability goals 🌿. With a commitment to policy implementation, his research informs industry and government decision-making. Feng’s recent publications in top journals reflect his expertise in environmental governance and low-carbon industrial policies. Despite his impressive academic contributions, there is room for expansion in industry partnerships, patents, and editorial roles 📚. Feng is a rising star in his field, bringing impactful, real-world solutions to pressing global issues and advancing the sustainability agenda 🌟.

Professional Profile

Scopus Profile

Education 🎓

Feng Shaohuai’s academic journey is rooted in the study of sustainability and carbon neutrality 🌍. He holds degrees specializing in environmental governance and low-carbon technologies 🌱, which have shaped his expertise in addressing global climate issues. His education provides a deep understanding of sustainable development challenges, particularly in the context of developing economies 🌏. Feng’s studies have focused on green innovation, energy efficiency, and policy frameworks that support the transition to a low-carbon future 🔋. This educational background enables him to merge theory with practice, crafting real-world solutions that align with global climate goals 🌐. Through his learning, Feng has honed interdisciplinary skills to create strategies that foster sustainability across various sectors, positioning him as a key figure in the field of environmental research 🔬.

Professional Experience 💼

Feng Shaohuai currently serves as a researcher at Universiti Sains Malaysia, where he focuses on sustainable industrial transformation and energy efficiency ⚙️. His professional work bridges the gap between policy, data, and technology, creating practical solutions for sustainable development 🌍. Feng leads projects that develop low-carbon industrial policies and green innovation strategies, aiming to transform industries and promote sustainability in emerging economies 🌱. His role enables him to work with governments, academia, and industry leaders to drive global sustainability agendas. Through his interdisciplinary approach, Feng has made valuable contributions to carbon neutrality and energy efficiency, ensuring that his research delivers real-world impact in the fight against climate change 🌡️.

Research Interest 🔬

Feng Shaohuai’s research is dedicated to carbon neutrality, low-carbon industrial policies, and sustainable development 🌱. He explores the intersection of technological innovation, environmental governance, and market mechanisms to develop solutions that address global sustainability challenges 🌍. His focus is on how data-driven environmental policies can enhance decision-making and improve energy efficiency 🔋. Feng also investigates strategies for sustainable industrial transformation, particularly in developing economies, to foster green innovation and reduce carbon footprints 🌳. His interdisciplinary research combines policy, technology, and industry, aiming to create actionable solutions for global climate goals 🌏. Through this work, Feng seeks to accelerate the transition to a low-carbon future and promote environmental sustainability across sectors 🌐.

Awards and Honors 🏆

While Feng Shaohuai has yet to receive major formal awards, his research has earned recognition in the field of sustainability and carbon neutrality 🌿. His articles in respected journals like Resources Policy and Energy Strategy Reviews showcase his growing influence in the academic world 📚. Feng’s work on low-carbon industrial transformation and environmental policies has contributed to advancing sustainable development goals 🌏. Though not yet awarded, his published research and interdisciplinary approach are a testament to his expertise and potential for future recognition 🌟. As his work continues to influence the fields of green innovation and sustainable development, Feng is well-positioned to receive accolades for his contributions to the low-carbon transition and global climate solutions 🌱.

Conclusion 🌟

Feng Shaohuai is an emerging leader in sustainability research, focusing on carbon neutrality, green innovation, and low-carbon industrial policies 🌿. His academic background and professional expertise enable him to bridge the gap between policy, data, and technology, driving impactful solutions for global sustainability 🌍. Feng’s research on sustainable industrial transformation and energy efficiency plays a pivotal role in shaping strategies that support carbon neutrality and environmental governance 🌱. Through his interdisciplinary approach, Feng is actively contributing to the global shift toward low-carbon futures 🌏. As he continues to collaborate with governments, industries, and researchers, Feng’s work will accelerate climate goals and contribute to a sustainable and green world 🌳.

Publications Top Notes

Unlocking the potential of natural resources, fintech and fiscal policy for carbon neutrality; evidence from N-11 nations

  • Authors: Shaohuai Feng, Mohd Wira Mohd Shafiei, Theam Foo Ng, Jie Ren

  • Year: November 2024

  • Source: Resources Policy 🌍

The intersection of economic growth and environmental sustainability in China: Pathways to achieving SDG

  • Authors: Shaohuai Feng, Mohd Wira Mohd Shafiei, Theam Foo Ng, Yefeng Jiang

  • Year: September 2024

  • Citations: 9 📚

  • Source: Energy Strategy Reviews ⚡

 

Sandipan Mondal | Data Science | Young Scientist Award

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Dr. Sandipan Mondal | Data Science | Young Scientist Award

Post Doctoral Researcher & Adjunct Assistant Professor at National Taiwan Ocean University, India

Dr. Sandipan Mondal is a Post-Doctoral Researcher and Adjunct Assistant Professor at the National Taiwan Ocean University, specializing in fisheries oceanography, climate change effects, species distribution modeling, and marine ecosystem dynamics. His expertise spans fish feeding ecology, taxonomic identification, stable isotope analysis, and fishing gear technology. With a strong research background, he has contributed significantly to habitat modeling and the impact of climate variability on fisheries in the Indian Ocean and Taiwan Strait. Dr. Mondal has an impressive publication record in top-tier journals and has received accolades such as the Young Academician Award and a research grant from the National Science and Technology Council of Taiwan. His ability to integrate advanced computational techniques, machine learning models, and remote sensing in ecological research sets him apart. A dedicated scientist committed to environmental sustainability, he actively collaborates on interdisciplinary projects and participates in academic awards, driving impactful contributions to marine and fishery sciences.

Professional Profile 

Google Scholar
Scopus Profile

Education

Dr. Sandipan Mondal holds a Ph.D. in Fisheries Resource Management from ICAR-Central Institute of Fisheries Education, India. His doctoral research focused on the impact of climate variability on fisheries and marine ecosystems, integrating statistical and computational approaches. Prior to this, he earned a Master’s degree in Fisheries Science with a specialization in Fisheries Resource Management, where he developed expertise in species distribution modeling and fish population dynamics. He also holds a Bachelor’s degree in Fisheries Science, which laid the foundation for his knowledge in aquaculture, fish biology, and marine ecology. His academic journey has been marked by rigorous training in advanced data analysis, remote sensing applications in fisheries, and ecological modeling. Throughout his education, he actively participated in research projects, workshops, and field studies, refining his skills in experimental design and marine biodiversity assessment. His strong academic background and multidisciplinary expertise have enabled him to contribute significantly to fisheries and marine research.

Professional Experience

Dr. Sandipan Mondal is currently a Post-Doctoral Researcher and Adjunct Assistant Professor at the National Taiwan Ocean University. In this role, he conducts research on fisheries oceanography, climate change impacts, and ecosystem-based fisheries management. He has extensive experience in species distribution modeling, stable isotope analysis, and fish feeding ecology, contributing to marine conservation and sustainable fisheries management. Before this, he worked as a Research Associate at ICAR-Central Institute of Fisheries Education, where he engaged in projects related to fisheries stock assessment, climate resilience, and marine habitat modeling. He has also collaborated with international research teams on machine learning applications in ecological studies. Additionally, he has served as a mentor and guest lecturer, sharing his expertise in fisheries science, oceanography, and statistical modeling. His professional journey reflects a strong commitment to interdisciplinary research, academic mentorship, and practical applications of fisheries and marine ecosystem studies.

Research Interest

Dr. Sandipan Mondal’s research interests focus on fisheries oceanography, marine ecosystem modeling, and the impact of climate change on aquatic biodiversity. He specializes in species distribution modeling, habitat suitability analysis, and the use of remote sensing and GIS in fisheries research. His work integrates machine learning techniques and advanced statistical approaches to predict fish population dynamics and assess marine environmental changes. He is particularly interested in the trophic interactions of marine species, stable isotope applications in food web studies, and the sustainability of fisheries resources under changing climatic conditions. His research extends to the development of fishing gear technology, ecological niche modeling, and conservation strategies for commercially important fish species. By combining computational tools and field-based studies, he aims to contribute to sustainable fisheries management and marine biodiversity conservation. His interdisciplinary approach enables him to address complex challenges in fisheries science and oceanographic research.

Awards and Honors

Dr. Sandipan Mondal has received several prestigious awards and honors in recognition of his outstanding contributions to fisheries and marine sciences. He was honored with the Young Academician Award for his groundbreaking research in fisheries oceanography and climate change impacts. He has also been awarded a research grant by the National Science and Technology Council of Taiwan, supporting his innovative work in species distribution modeling and marine ecosystem dynamics. His academic excellence has been acknowledged through multiple best paper and presentation awards at international awards. Additionally, he has been a recipient of merit-based scholarships and fellowships during his academic journey. His commitment to research and innovation has positioned him as a leading expert in fisheries resource management. These accolades reflect his dedication to advancing marine science and his continuous pursuit of solutions for sustainable fisheries and environmental conservation.

Conclusion

Dr. Sandipan Mondal is a dedicated marine scientist whose expertise in fisheries oceanography, climate change impacts, and ecosystem modeling has significantly contributed to the field of fisheries and marine research. His strong academic background, combined with extensive research experience, has allowed him to integrate advanced computational tools and ecological theories to address critical challenges in fisheries management. Through his interdisciplinary approach, he has made notable contributions to marine conservation, sustainable fisheries practices, and climate adaptation strategies. His work has been recognized through numerous awards, grants, and publications in high-impact journals. Beyond research, he is actively involved in academic mentorship and collaborative projects, driving innovation and knowledge exchange in the scientific community. With a passion for environmental sustainability and marine biodiversity conservation, Dr. Mondal continues to explore new frontiers in fisheries science, aiming to bridge the gap between research and practical applications for the benefit of global aquatic ecosystems.

Publications Top Noted

1.

  • Title: Habitat suitability modeling for the feeding ground of immature albacore in the southern Indian Ocean using satellite-derived sea surface temperature and chlorophyll data
  • Authors: S Mondal, AH Vayghan, MA Lee, YC Wang, B Semedi
  • Year: 2021
  • Citations: 26
  • Source: Remote Sensing 13 (14), 2669

2.

3.

  • Title: Long-term observations of sea surface temperature variability in the Gulf of Mannar
  • Authors: S Mondal, MA Lee
  • Year: 2023
  • Citations: 10
  • Source: Journal of Marine Science and Engineering 11 (1), 102

4.

  • Title: Seasonal distribution patterns of Scomberomorus commerson in the Taiwan Strait in relation to oceanographic conditions: An ensemble modeling approach
  • Authors: S Mondal, MA Lee, JS Weng, KE Osuka, YK Chen, A Ray
  • Year: 2023
  • Citations: 9
  • Source: Marine Pollution Bulletin 197, 115733

5.

  • Title: Ensemble modeling of black pomfret (Parastromateus niger) habitat in the Taiwan Strait based on oceanographic variables
  • Authors: S Mondal, MA Lee, YK Chen, YC Wang
  • Year: 2023
  • Citations: 9
  • Source: PeerJ 11, e14990

6.

  • Title: Habitat modeling of mature albacore (Thunnus alalunga) tuna in the Indian Ocean
  • Authors: S Mondal, MA Lee
  • Year: 2023
  • Citations: 8
  • Source: Frontiers in Marine Science 10, 1258535

7.

8.

  • Title: Ensemble three-dimensional habitat modeling of Indian Ocean immature albacore tuna (Thunnus alalunga) using remote sensing data
  • Authors: S Mondal, YC Wang, MA Lee, JS Weng, BK Mondal
  • Year: 2022
  • Citations: 8
  • Source: Remote Sensing 14 (20), 5278

9.

  • Title: Long-term variation of sea surface temperature in relation to sea level pressure and surface wind speed in the southern Indian Ocean
  • Authors: S Mondal, MA Lee, YC Wang, B Semedi
  • Year: 2022
  • Citations: 7
  • Source: Journal of Marine Science and Technology 29 (6), 784-793

10.

  • Title: Changes in properties of polyamide netting materials exposed to different environments
  • Authors: S Mondal, SN Thomas, B Manoj Kumar
  • Year: 2019
  • Citations: 7
  • Source: J Fish Res. 2019; 3 (2): 1-3. J Fish Res 2019 Volume 3 Issue 2

11.

  • Title: Impact of climatic oscillations on marlin catch rates of Taiwanese long-line vessels in the Indian Ocean
  • Authors: S Mondal, A Ray, KE Osuka, RI Sihombing, MA Lee, YK Chen
  • Year: 2023
  • Citations: 6
  • Source: Scientific Reports 13 (1), 22438

12.

  • Title: Detecting the feeding habitat zone of albacore tuna (Thunnus alalunga) in the southern Indian Ocean using multisatellite remote sensing data
  • Authors: S Mondal, YC Lan, MA Lee, YC Wang, B Semedi, WY Su
  • Year: 2022
  • Citations: 5
  • Source: Journal of Marine Science and Technology 29 (6), 794-807

13.

  • Title: Habitat modelling of escolar fish (Lepidocybium flavobrunneum, Smith 1843) in the southwestern Indian Ocean using remote sensing data
  • Authors: RI Sihombing, A Ray, S Mondal, MA Lee
  • Year: 2024
  • Citations: 4
  • Source: International Journal of Remote Sensing 45 (23), 8722-8741

14.

15.

  • Title: Fishery-based adaptation to climate change: The case of migratory species flathead grey mullet (Mugil cephalus L.) in Taiwan Strait, Northwestern Pacific
  • Authors: MA Lee, S Mondal, SY Teng, ML Nguyen, P Lin, JH Wu, BK Mondal
  • Year: 2023
  • Citations: 4
  • Source: PeerJ 11, e15788

16.

  • Title: Distribution patterns of grey mullet in the Taiwan Strait in relation to oceanographic conditions
  • Authors: SY Teng, S Mondal, QH Lu, P Lin, MA Lee, LG Korowi
  • Year: 2024
  • Citations: 2
  • Source: Journal of Marine Science and Engineering 12 (4), 648

17.

  • Title: Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices
  • Authors: A Haghi Vayghan, A Ray, S Mondal, MA Lee
  • Year: 2024
  • Citations: 2
  • Source: Frontiers in Marine Science 11, 1329254

18.

  • Title: Total catch variability in the coastal waters of Taiwan in relation to climatic oscillations and possible impacts
  • Authors: MA Lee, S Mondal, JH Wu, M Boas
  • Year: 2022
  • Citations: 2
  • Source: Journal of Taiwan Fisheries Society 49 (2), 127-143

19.

  • Title: Cyclic variation in fishing catch rates influenced by climatic variability in the waters around Taiwan
  • Authors: MA Lee, S Mondal, JH Wu, YH Huang, M Boas
  • Year: 2022
  • Citations: 2
  • Source: Journal of Taiwan Fisheries Society 49 (2), 113-125

20.

  • Title: The influence of sea surface temperature on the distribution of albacore tuna (Thunnus alalunga) in the southern Indian Ocean
  • Authors: S Mondal, MA Lee
  • Year: 2018
  • Citations: 1
  • Source: Journal of Fisheries Society Taiwan 45 (4), 253-260