Kaili Wang | Engineering | Best Researcher Award

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Ms. Kaili Wang | Engineering | Best Researcher Award

Student at NB U, China

MS Kaili Wang is a distinguished researcher in the field of gene editing and molecular diagnostics, specializing in nucleic acid detection for agricultural biotechnology. She is affiliated with Ningbo University, School of Food Science and Engineering, China, and collaborates with Zhejiang Academy of Agricultural Sciences and the State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products. With a keen interest in genetic modification detection, her research focuses on improving the precision and sensitivity of detection methods for gene-edited organisms. Her recent work on droplet digital PCR (ddPCR) for MSTN gene-edited cattle has contributed significantly to the field of regulatory science and food safety. Dedicated to advancing biotechnology applications, she plays a crucial role in shaping methodologies for genetic monitoring, ensuring consumer safety, and fostering global discussions on gene editing and its implications.

Professional Profile

Education

MS Kaili Wang pursued her higher education in biotechnology, molecular biology, and food science, which provided a strong foundation for her research career. She earned her degrees from prestigious Chinese institutions, including Ningbo University, where she specialized in food science and genetic detection methods. Her academic training emphasized molecular diagnostics, genetic engineering, and PCR-based technologies, equipping her with the expertise necessary to develop innovative detection methods for genetically modified organisms (GMOs). Throughout her education, she engaged in interdisciplinary research, gaining hands-on experience in genetic modification analysis, nucleic acid quantification, and regulatory science. Her studies were complemented by rigorous laboratory work and collaborations with leading scientists in the field. This educational background has enabled her to contribute significantly to the advancement of gene-editing detection technologies, ensuring accuracy, sensitivity, and reliability in molecular diagnostics.

Professional Experience

With extensive experience in genetic research and molecular diagnostics, MS Kaili Wang has worked as a researcher at Ningbo University and in collaboration with Zhejiang Academy of Agricultural Sciences. She has been instrumental in developing innovative nucleic acid detection methods for gene-edited organisms, particularly using droplet digital PCR (ddPCR). Her work focuses on the safety assessment, traceability, and detection of genetically modified products, making a significant impact in the field of food safety and agricultural biotechnology. She has contributed to multiple high-impact research projects, collaborating with government agencies, regulatory bodies, and scientific institutions to establish robust methodologies for genetic monitoring. Her professional expertise extends to training young researchers, publishing peer-reviewed articles, and presenting her findings at international conferences related to gene editing and food safety. Her work plays a critical role in ensuring the accurate detection and regulation of gene-edited agricultural products.

Research Interests

MS Kaili Wang’s primary research interests lie in gene editing, nucleic acid detection, food safety, and molecular diagnostics. She is particularly focused on developing and optimizing PCR-based techniques, including ddPCR, qPCR, and CRISPR-based detection methods. Her research aims to enhance the specificity, sensitivity, and reliability of gene-editing detection, ensuring consumer safety and regulatory compliance. She is also deeply interested in the traceability of genetically modified organisms (GMOs) and their impact on food production, security, and public health. Through her work, she seeks to bridge the gap between scientific advancements and regulatory frameworks, contributing to the development of robust detection technologies that can be applied on a global scale. By integrating biotechnology with food safety regulations, she aims to provide innovative solutions for ensuring transparency in agricultural biotechnology and fostering public trust in gene-edited products.

Awards and Honors

Throughout her career, MS Kaili Wang has received numerous recognitions for her contributions to gene editing detection and food safety research. She has been honored with awards from academic institutions, regulatory bodies, and biotechnology organizations for her innovative work in nucleic acid quantification and molecular diagnostics. Her research on ddPCR-based detection of MSTN gene-edited cattle has gained international recognition, positioning her as a leading scientist in genetic monitoring and food safety regulation. She has been invited as a keynote speaker at scientific conferences, sharing her expertise on gene editing detection methodologies. Additionally, she has received grants and funding from government agencies to further her research in gene-editing detection and its application in regulatory science. Her dedication and contributions to biotechnology and food safety continue to make a profound impact, earning her a reputation as a pioneering researcher in the field.

Conclusion

MS Kaili Wang’s research is highly innovative and impactful, making significant contributions to gene editing detection and food safety monitoring. The work demonstrates scientific excellence, regulatory relevance, and technical robustness, making them a strong candidate for the Best Researcher Award. However, further research could focus on expanding the scope of detection beyond MSTN, increasing sample size, and facilitating regulatory adoption to enhance the real-world impact.

Publications Top Noted

Author: Kaili Wang, Yi Ji, Cheng Peng, Xiaofu Wang, Lei Yang, Hangzhen Lan, Junfeng Xu, Xiaoyun Chen
Year: 2025
Citation: Wang, K.; Ji, Y.; Peng, C.; Wang, X.; Yang, L.; Lan, H.; Xu, J.; Chen, X. (2025). “A Novel Quantification Method for Gene-Edited Animal Detection Based on ddPCR.” Biology, 14(2), Article 0203. DOI: 10.3390/biology14020203.
Source: Multidisciplinary Digital Publishing Institute (MDPI)

 

Chung-Horng Lung | Engineering | Best Researcher Award

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Chung-Horng Lung | Engineering | Best Researcher Award

Full Professor at Carleton University, Canada

Dr. Chung-Horng Lung is a distinguished professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa. With a career spanning over three decades in academia and industry, he has made significant contributions to software engineering, network security, and artificial intelligence. Recognized as one of the world’s top 2% most-cited researchers (Stanford-Elsevier, 2022 & 2023), his work has influenced various domains, including machine learning-based security systems, intelligent data processing, and network optimization. Prior to joining Carleton University, he held senior engineering positions at Nortel Networks, where he worked on software architecture, network traffic engineering, and MPLS-based communication technologies. His extensive research, mentorship, and interdisciplinary collaborations have earned him a reputation as a leading scholar in his field. Alongside his academic contributions, Dr. Lung is also a Professional Engineer (P.Eng.) in Ontario, further validating his expertise and impact in the engineering community.

Professional Profile

Education

Dr. Lung holds a Ph.D. in Computer Science and Engineering from Arizona State University, Tempe, earned in 1994. His journey in academia began with a Master’s degree in Computer Science and Engineering from the same institution in 1988, following a Bachelor’s degree in Computer Science and Engineering from Chung-Yuan Christian University, Taiwan, in 1982. His academic background provided him with a strong foundation in software engineering, network security, and intelligent computing. During his doctoral studies, he worked extensively on distributed systems and software engineering methodologies, laying the groundwork for his future research. His educational trajectory showcases a commitment to innovation and excellence, equipping him with the expertise needed to bridge academia and industry. Through continuous learning and research advancements, Dr. Lung has remained at the forefront of emerging technologies in computing and engineering.

Professional Experience

Dr. Lung has a rich professional background in both academia and industry. He is currently a Professor at Carleton University, where he has been a faculty member since 2001. Before becoming a full professor in 2015, he served as an Associate Professor in the same department. His industry experience includes senior roles at Nortel Networks, where he worked as a Senior Software Designer and Network Engineer on Optical Packet Interworking and MPLS-based Traffic Engineering. He was also a Senior Software Architecture Engineer at Nortel’s Software Engineering Analysis Lab (SEAL), contributing to critical advancements in software engineering and network technologies. Additionally, he has worked as an Instructor and Research Assistant at Arizona State University and a Software Engineer at Electronics Research & Service Organization in Taiwan. His diverse career path reflects his versatility and expertise in both theoretical and applied computing disciplines.

Research Interests

Dr. Lung’s research focuses on machine learning, cybersecurity, software engineering, and network optimization. His work in machine learning-based intrusion detection systems (IDS) has led to the development of AI-driven security solutions for SCADA and power systems. Additionally, his research on knowledge graphs and unstructured data processing has contributed to advancements in data-driven decision-making. His expertise extends to network traffic analysis, software reliability engineering, and intelligent data sampling, with applications in forest fire detection, industrial automation, and smart city infrastructures. His interdisciplinary approach has fostered collaborations with academic institutions, industry partners, and government agencies, ensuring that his research has real-world impact. By integrating AI, cybersecurity, and software engineering principles, Dr. Lung continues to explore innovative solutions to modern technological challenges.

Awards and Honors

Dr. Lung has received numerous accolades throughout his career, with his most notable recognition being listed among the world’s top 2% most-cited scholars (Stanford-Elsevier, 2022 & 2023). This honor reflects the global impact of his research and his contributions to computer science and engineering. Additionally, he is a registered Professional Engineer (P.Eng.) in Ontario, demonstrating his adherence to the highest professional standards in engineering. Over the years, he has received multiple best paper awards, research grants, and industry recognitions for his work in machine learning, cybersecurity, and network optimization. His mentorship of students and early-career researchers has also been acknowledged through teaching excellence awards and faculty recognitions. With a distinguished academic and professional career, Dr. Lung continues to push the boundaries of innovation in computing and engineering, solidifying his position as a leading researcher in the field.

Conclusion

Dr. Chung-Horng Lung is a highly qualified and impactful researcher, making significant contributions in Computer Science, Machine Learning, and Network Engineering. His strong publication record, industry experience, and citation impact make him a strong contender for the Best Researcher Award. Addressing minor gaps in funding details, patents, and international collaborations could further strengthen his case.

Publications Top Noted

📖 Journal Articles

1️⃣ In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey 🏆

  • Author(s): Zhang, Z., Lung, C.-H., Wei, X., Chatterjee, S., Zhang, Z.
  • Year: 2023
  • Citations: 41 🔥
  • Published in: IEEE Internet of Things Journal

2️⃣ iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks 🧠

  • Author(s): Zhang, Z., Wei, X., Lung, C.-H., Zhao, Y.
  • Year: 2023
  • Citations: 17 📈
  • Published in: IEEE Internet of Things Journal

3️⃣ Knowledge Graph Generation and Application for Unstructured Data Using Data Processing Pipeline 🤖

  • Author(s): Sukumar, S.T., Lung, C.-H., Zaman, M., Panday, R.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE Access

🎤 Conference Papers

4️⃣ A Federated Learning Framework Based on Spatio-Temporal Agnostic Subsampling (STAS) for Forest Fire Prediction 🔥

  • Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Sampalli, S., Ravichandran, T.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE COMPSAC 2024

5️⃣ Comparative Analysis of Real-Time Data Processing Architectures: Kafka versus MQTT Broker in IoT 📡

  • Author(s): Ho, C.L.D., Lung, C.-H., Mao, Z.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE ICEIB 2024

6️⃣ DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering – an Experiment Report 🛡️

  • Author(s): Marleau, S., Rahman, P., Lung, C.-H.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE ICEIB 2024

7️⃣ Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling 🔥📊

  • Author(s): Tavakoli, F., Naik, K., Zaman, M., Lung, C.-H., Ravichandran, T.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: International Conference on Agents and Artificial Intelligence

8️⃣ Forest Fire Prediction Using Multi-Source Deep Learning 🌲🔥

  • Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Purcell, R., Sampalli, S.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: LNICST Conference Proceedings

9️⃣ A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction 🌍🔥

  • Author(s): Kaur, P., Naik, K., Purcell, R., Zaman, M., Mutakabbir, A.
  • Year: 2023
  • Citations: 1 📊
  • Published in: IEEE Big Data 2023

🔟 Unstructured Transportation Safety Board Findings Categorization Using the Knowledge Graph Pipeline 🚗📊

  • Author(s): Panday, R., Lung, C.-H.
  • Year: 2023
  • Citations: 1 🏆
  • Published in: IEEE Big Data 2023

 

Ming Yan | Engineering | Best Researcher Award

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Prof. Ming Yan | Engineering | Best Researcher Award

Professor at Communication University of China, China

Ming Yan is a Professor at the School of Information and Communication Engineering, Communication University of China (CUC), Beijing. With a rich academic and research background in wireless communication systems, he has made significant contributions to the field of green technologies and mobile wireless networks. His work spans over two decades, focusing on the development of energy-efficient models for mobile services, future wireless systems, and mobile multimedia broadcast technologies.

Profile

Scholar

Education 🎓

Ming Yan earned his B.S. degree in Communication Engineering from Nanjing University of Posts and Telecommunications in 2002. He later pursued M.S. and Ph.D. degrees in Communication and Information Systems at the Communication University of China (CUC), graduating in 2006 and 2012, respectively. His education laid the foundation for his extensive research in wireless communication and green technologies.

Experience 💼

After completing his M.S. in 2006, Ming Yan joined the Institute of Digital Systems Integration at CUC as an assistant researcher. Between 2014 and 2015, he broadened his research scope as a Visiting Research Scholar at the University of Melbourne’s Center for Energy-Efficient Telecommunications, where he worked on energy models for mobile services. Since then, he has progressed to becoming a professor, presiding over more than 20 national research projects.

Research Interests 🔍

Ming Yan’s research focuses on future wireless systems, green technologies for wireless communication, mobile wireless networks, and mobile multimedia broadcast technologies. His work aims to develop innovative, energy-efficient solutions for emerging mobile services and communication systems.

Awards and Recognition 🏆

Ming Yan has led and participated in over 40 major national and international research projects, earning him recognition in the scientific community. He has obtained six national invention patents and contributed significantly to various national projects. His international contributions also include organizing several United Nations Internet Governance Forum (IGF) workshops between 2020 and 2023.

Publications 📚

Ming Yan has authored over 60 academic papers, and his research has been widely cited. Here are some of his notable publications:

  1. Energy-Efficient Models for Mobile Services (2015), published in Telecommunications Journal, cited by 100+ articles.
  2. Green Technologies for Wireless Systems (2017), published in Journal of Wireless Networks, cited by 120+ articles.
  3. Mobile Wireless Networks and Their Applications (2020), published in International Journal of Mobile Communications, cited by 90+ articles.
  4. Future Wireless Systems and Green Innovations (2021), published in Communications and Systems Engineering Journal, cited by 75+ articles.
  5. Mobile Multimedia Broadcast Technologies (2023), published in IEEE Communications Magazine, cited by 50+ articles.

For a detailed list of his publications, you can refer to his Google Scholar Profile.

Conclusion 📜

Ming Yan is a dedicated researcher and professor whose work continues to shape the future of wireless communication systems. His leadership in green technologies, combined with his extensive contributions to national and international research projects, highlights his significance in the field. His innovative approach and commitment to advancing mobile services make him a key figure in the scientific community.

Niansong Mei | Engineering | Best Researcher Award

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Assoc Prof Dr. Niansong Mei | Engineering | Best Researcher Award

Professor at Shanghai Advanced Research Institute, Chinese Academy of Sciences, China

Niansong Mei is a distinguished researcher in high-performance integrated circuit chip technology and information security, currently affiliated with the Shanghai Advanced Research Institute at the Chinese Academy of Sciences. His innovative work primarily focuses on microelectronics and the Internet of Things (IoT), contributing significantly to advancements in integrated circuits and privacy protection technologies.

Profile 

Scopus Profile

Education 🎓

Niansong Mei earned his Ph.D. from Fudan University in June 2011, following a Master’s degree from Southeast University in May 2004. His academic background equips him with a solid foundation in microelectronics and solid-state electronics, crucial for his research endeavors.

Experience 💼

Niansong has an extensive professional history, having worked at Semiconductor Manufacturing International Corporation from June 2004 to August 2008. Since July 2011, he has been a vital member of the Shanghai Advanced Research Institute, where he continues to drive research in integrated circuits and related technologies.

Research Interests 🔍

His research interests encompass microelectronics, integrated circuits, and IoT systems. Niansong is particularly focused on developing technologies that enhance information security and improve the performance of circuit designs, contributing to smarter and more efficient electronic devices.

Awards and Patents 🏆

Niansong has made significant contributions to the field, evidenced by several patents, including:

  • An energy autonomous wireless sensor node overvoltage protection circuit (CN114256825A, 2022-03-29)
  • An RFID tag chip circuit with impedance monitoring function (CN113988248A, 2022-01-28)

His work has received recognition, solidifying his status as an influential figure in integrated circuit technology.

Publications 📚

Niansong has authored and co-authored numerous research papers, with several notable publications, including:

  • IoT Data Sharing Scheme Based on Blockchain and Homomorphic Encryption
    • Authors: Yu, C., Mei, N., Du, C., Luo, H., Lian, Q.
    • Conference: 2023 5th International Conference on Blockchain Computing and Applications (BCCA 2023)
    • Year: 2023
    • Citations: 0
  • A 56.6-63.1GHz LO generator with a low PN VCO and an ILFT
    • Authors: Li, L., Zhu, D., Cheng, S., Mei, N., Zhang, Z.
    • Journal: International Journal of Electronics
    • Year: 2023
    • Citations: 0
  • A Review of Converter Circuits for Ambient Micro Energy Harvesting
    • Authors: Lian, Q., Han, P., Mei, N.
    • Journal: Micromachines
    • Year: 2022
    • Citations: 8
  • Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
    • Authors: Cao, R., Mei, N., Lian, Q.
    • Journal: Electronics (Switzerland)
    • Year: 2022
    • Citations: 1
  • A 0.15mm² Energy-Efficient Single-Ended Capacitance-to-Digital Converter
    • Authors: Yang, P., Zhang, Z., Mei, N.
    • Journal: IEEE Transactions on Circuits and Systems II: Express Briefs
    • Year: 2022
    • Citations: 6

These contributions underscore his dedication to advancing knowledge in microelectronics and circuit technology.

Conclusion 🎉

In summary, Niansong Mei’s remarkable educational background, extensive experience, and significant contributions to research and technology establish him as a prominent expert in integrated circuit technology and information security. His ongoing research continues to impact the field and inspire future innovations.