Zhizhen Chen | Computer Science | Best Researcher Award

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Dr. Zhizhen Chen | Computer Science | Best Researcher Award

Senior Lecturer, University of Greenwich, United Kingdom

🎓 Dr. Zhizhen Chen is a dedicated academic professional serving as a Senior Lecturer in Finance at the University of Greenwich since 2017. With a rich background in finance and economics, Dr. Chen brings extensive experience from both academia and industry. His research interests encompass financial markets, risk management, and financial engineering, contributing significantly to several top-tier finance journals. Dr. Chen is also an active peer reviewer and passionate educator, sharing his expertise through innovative courses like Fintech Banking and Financial Engineering and Machine Learning. 🌍📊

Publication Profile

Scopus

Strengths for the Award:

  1. Research Excellence: Dr. Chen has a strong publication record, with multiple articles published in high-impact journals such as “Journal of International Money and Finance” and “Research in International Business and Finance,” both rated 4* in SJR. This demonstrates his capability in producing high-quality research in the field of finance.
  2. Peer Review Activities: He has been a peer reviewer for several prestigious journals since 2016, which showcases his recognition in the academic community and his commitment to advancing knowledge in finance.
  3. Academic and Professional Credentials: Dr. Chen holds a PhD in Finance, is a Fellow of the Higher Education Academy, and has passed CFA Level 1. This combination of academic qualifications and professional certification adds to his credibility and expertise in the field.
  4. Diverse Teaching Portfolio: His teaching experience spans various finance and economics-related courses, demonstrating versatility and a solid understanding of different areas within the field.
  5. Industry Experience: Dr. Chen’s experience as an Investment Analyst and his work with financial institutions provide him with practical insights, enhancing his academic work’s relevance and applicability.
  6. Continuous Professional Development: His commitment to continuous learning and development is evident through his successful completion of the CFA Level 1 exam and his role in ongoing staff development activities.

Areas for Improvement:

  1. Broader Research Impact: While Dr. Chen has a strong record in finance-specific publications, expanding his research impact across other interdisciplinary areas, such as sustainable finance or fintech, could further enhance his profile.
  2. Leadership Roles in Research: Taking on more leadership roles in research projects or academic committees could strengthen his candidacy by demonstrating his influence beyond his individual contributions.
  3. Grants and Funding: Securing research grants or funding is a notable achievement in academia that is not highlighted in the current profile. Pursuing funding opportunities could bolster his research credentials further.

Education

🎓 Dr. Zhizhen Chen holds a PhD in Finance from the University of Glasgow (2018), demonstrating his strong foundation in financial research and education. He is also a Fellow of the Higher Education Academy (2017), a testament to his commitment to teaching excellence, and he earned an MSc in Economics from the University of Wuhan (2012). 📚✨

Experience

🌟 Dr. Chen’s career spans roles as a Senior Lecturer in Finance at the University of Greenwich since 2017, where he excels in teaching and research. His prior experience includes serving as a Research Assistant and Teaching Assistant at the University of Glasgow, and an Investment Analyst at ICBC Wuhan. This blend of academic and industry roles has equipped him with a unique perspective on finance education. 💼💹

Research Focus

🔍 Dr. Chen’s research is focused on financial markets, risk management, securitization, and financial engineering. He actively contributes as a peer reviewer for prestigious journals, including the Journal of International Financial Markets, Institutions & Money, and Finance Research Letters, ensuring rigorous academic standards in the field. 📑🔬

Awards and Honours

🏅 Dr. Chen was recognized as a Fellow of the Higher Education Academy in 2017, highlighting his dedication to teaching excellence. In addition, he passed the CFA Level 1 exam in 2020, demonstrating his commitment to continuous professional development in finance. 🎖️📈

Publication Top Notes

Lin, W., Yan, W., Chen, Z., Xiao, R. (2023). Research on product appearance patent spatial shape recognition for multi-image feature fusion. Multimedia Tools and Applications (SJR 3*).

Xiao, R., Li, G., Chen, Z. (2023). Research progress and prospect of evolutionary many-objective optimization. Control and Decision.

Chen, Z., Liu, H., Peng, J., Zhang, H., Zhou, M. (2022). Securitization and bank efficiency. In: Ferris, S.P., Kose, J., Makhija, A.K., (eds.) Empirical Research in Banking and Corporate Finance. Emerald Publishing Limited.

Conclusion:

Dr. Zhizhen Chen is a suitable candidate for the “Research for Best Researcher Award” due to his significant contributions to the field of finance through high-quality publications, peer-review activities, and professional development. While there is room for growth in interdisciplinary research and leadership roles, his current achievements and ongoing commitment to both academic and professional excellence make him a compelling contender for the award.

Shaghaf Kaukab | Technology | Young Scientist Award

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Dr. Shaghaf Kaukab | Technology | Young Scientist Award

scientist, ICAR-CIPHET, India

Shaghaf Kaukab is a dedicated Scientist at ICAR-Central Institute of Post-Harvest Engineering & Technology (ICAR-CIPHET), Ludhiana, specializing in Agricultural Structure and Process Engineering. With over 11 years of combined experience in scientific research and academic exploration within the food engineering and technology platform, Shaghaf has made significant contributions to the domain of extrusion processing, storage technology, drying techniques, and functional food product development. His work emphasizes the application of AI, machine learning, and deep learning techniques in agriculture, leading to innovative solutions that improve post-harvest management and food processing.

Publication Profile

Scopus

Strengths for the Award

  1. Research Contributions: Shaghaf Kaukab has made significant contributions to agricultural structure and process engineering, particularly in post-harvest technology. Her work on projects such as IoT-based monitoring systems and AI-enabled robotic harvesters demonstrates her innovative approach and alignment with modern agricultural challenges.
  2. Academic Excellence: With a Ph.D. in Post Harvest Technology and multiple prestigious academic awards, she has a strong academic background. Her high CGPA scores and ICAR merit medals underscore her academic diligence.
  3. Interdisciplinary Expertise: Shaghaf has expertise in various domains, including AI, machine learning, image processing, and food process engineering, making her research impactful and versatile.
  4. Publications and Impact: She has published extensively in refereed journals and contributed to book chapters, highlighting her active involvement in advancing her field of research. The inclusion of her work in high-impact journals reflects her research’s quality and relevance.
  5. Leadership and Collaboration: Shaghaf has demonstrated leadership by managing several projects, mentoring students, and coordinating training programs. Her collaborative efforts with organizations like CDAC and international exposure (e.g., Purdue University) enhance her profile.

Areas for Improvement

  1. Broader Outreach: While Shaghaf has conducted training and outreach activities, expanding these efforts to reach a more diverse audience, including more international platforms, could enhance her influence and recognition.
  2. Grant Acquisition: Although involved in several projects, focusing on securing more independent research grants could further validate her capabilities and drive her research agenda.
  3. Networking and Professional Development: Increased participation in international conferences, workshops, and collaborations outside of India could further her exposure and contribute to professional growth.

 

🎓 Education

Shaghaf Kaukab earned his Ph.D. in Post-Harvest Technology from the Indian Agricultural Research Institute (IARI), New Delhi, with a stellar CGPA of 9.1/10 in 2019. Prior to this, he completed his M.Tech. in Post-Harvest Engineering & Technology from IARI, New Delhi, with a CGPA of 8.97/10 in 2016. His academic journey has been marked by excellence, laying a strong foundation for his research and scientific endeavors.

💼 Experience

Currently, Shaghaf is a Scientist in Agricultural Structures & Process Engineering at ICAR-CIPHET, Ludhiana, where he has been instrumental in the development of technologies such as the stereo-depth based detection and localization module for apples. He has successfully led and contributed to several ongoing projects, including IoT-based modular systems for cold storage and AI-enabled robotic apple harvesters. His role extends to technical writing, project implementation, and collaboration with academic and industrial partners.

🔍 Research Focus

Shaghaf’s research interests lie in the application of new-age technologies like AI, machine learning, and deep learning in the post-harvest agriculture sector. He focuses on image processing techniques (such as Biospeckle, RGB, X-ray, Hyperspectral imaging) and the analysis of food properties (physical, thermal, mechanical, and micro-structural). His work in food process engineering aims to enhance the efficiency and quality of post-harvest processes.

🏆 Awards and Honors

Shaghaf Kaukab’s work has earned him recognition within the scientific community, including membership in prestigious organizations such as the Indian Society of Agricultural Engineers (ISAE) and the American Society of Agricultural and Biological Engineers (ASABE). He serves as a regular reviewer for scientific journals and has been an external examiner for graduate students at Dr. Rajendra Prasad Central Agricultural University, Bihar.

📚 Publication Top Notes

Shaghaf has published numerous articles in refereed journals and contributed to book chapters and training manuals. His notable works include:

Improving Real-time Apple Fruit Detection: Depth and Multi-modal Information Fusions with Non-targeted Background Removal – Published in Ecological Informatics.

Chickpea Temperature Profile Development and its Implication under Microwave Treatment – Published in Biological Forum – An International Journal.

Osmotic Dehydration of Aloe-vera Gel Discs – Published in Journal of AgriSearch.

Engineering Properties, Processing, and Value Addition of Tamarind: A Review – Published in IJBSM.

Study of Engineering Properties of Selected Vegetable Seeds – Published in Indian Journal of Agricultural Sciences.

 

Conclusion

Shaghaf Kaukab is a strong candidate for the Research for Young Scientist Award. Her innovative research, interdisciplinary expertise, and significant contributions to agricultural engineering, particularly in post-harvest technology, make her a standout. While expanding her outreach and securing more independent funding could strengthen her profile further, her accomplishments thus far demonstrate her potential as a leader in her field.

 

Obsa Gilo Wakuma | Computer Science | Best Researcher Award

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Dr. Obsa Gilo Wakuma | Computer Science | Best Researcher Award

Ass. Prof, Wallaga University, Ethiopia

Dr. Obsa Gilo Wakuma is a dedicated computer scientist specializing in deep learning and domain adaptation. With extensive experience in academia, he has contributed significantly to the field through his research and teaching. Dr. Obsa has held various positions at Wallaga University, Ethiopia, and currently serves as a research scholar at the Indian Institute of Technology Patna, India. His expertise spans multiple programming languages and database management systems, making him a versatile and valuable contributor to the field of computer science.

Profile

Strengths for the Award:

  1. Extensive Research Background: Dr. Obsa Gilo has a robust academic background, culminating in a Ph.D. in Computer Science and Engineering with a focus on deep learning approaches for efficient domain adaptation. His research in domain adaptation, particularly in sensor data and image classification, showcases his innovative contributions to the field.
  2. Publications: Dr. Gilo has a significant number of publications in reputed journals and conference proceedings. Notable among them are:
    • “Kernel bures metric for domain adaptation in sensor data” in Expert System with Applications (2024)
    • “Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation” in Pattern Analysis and Applications (2024)
    • “Rdaot: Robust unsupervised deep sub-domain adaptation through optimal transport for image classification” in IEEE Access (2023)
  3. Teaching and Mentoring: His employment history includes roles such as Lecturer and Graduate Assistant at Wallaga University, demonstrating his commitment to education and mentoring the next generation of scholars.
  4. Skills and Competencies: Dr. Gilo possesses strong technical skills in various programming languages, databases, and web development technologies, along with proficiency in English, Afaan Oromoo, and Amharic. This multilingual ability enhances his capacity to engage with diverse communities.
  5. Community Service: His experience includes academic research, teaching, training, consultation, and community service, reflecting a well-rounded professional dedicated to both academic and societal contributions.

Areas for Improvement:

  1. Practical Community Impact: While Dr. Gilo has an impressive academic and research portfolio, there could be more emphasis on the practical application of his research directly benefiting local communities. Highlighting specific projects or initiatives where his work has directly impacted community development would strengthen his case.
  2. Collaboration with Local Institutions: Greater collaboration with local institutions and involvement in projects addressing community-specific issues could further enhance his profile. Establishing partnerships with local universities, NGOs, or governmental bodies to implement his research findings in real-world settings would be beneficial.
  3. Visibility and Outreach: Increasing the visibility of his work through public lectures, community workshops, or outreach programs can help in demonstrating the broader societal impact of his research. Engaging with the community through these platforms can showcase the practical benefits of his research.

 

Education: 🎓

Dr. Obsa Gilo Wakuma holds a Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna, with a thesis on “Deep Learning Approaches for Efficient Domain Adaptation.” He earned his M.Sc. in Computer Science from Wallaga University, where he developed an Information Extraction Model for Afaan Oromo news texts. Dr. Obsa also holds a B.Sc. in Computer Science from Wallaga University and has a strong foundation from Sibu Sire Preparatory and High School.

Experience: 💼

Dr. Obsa’s professional journey includes roles such as Recorder and Laboratory Technician at Wallaga University. He progressed to become a Graduate Assistant, Lecturer, and finally a Research Scholar at IIT Patna. His career reflects a blend of administrative, technical, and academic responsibilities, showcasing his diverse skill set and commitment to the field.

Research Interests: 🔍

Dr. Obsa’s research interests lie in deep learning, domain adaptation, and unsupervised learning. He has focused on developing efficient methods for domain adaptation in sensor data and image classification, contributing to several high-impact publications. His work aims to enhance the applicability and robustness of machine learning models in diverse environments.

Awards: 🏆

Dr. Obsa has been recognized for his academic excellence and research contributions throughout his career. He has received accolades for his innovative work in domain adaptation and deep learning, highlighting his role as a prominent researcher in the field of computer science.

Publications

  1. Unsupervised Sub-Domain Adaptation Using Optimal Transport. Journal of Visual Communication and Image Representation, 94, 103857. Cited by: 1 article.
  2. Kernel Bures Metric for Domain Adaptation in Sensor Data. Expert System with Applications, 255(Part C), 124725. Cited by: 1 article.
  3. Subdomain Adaptation via Correlation Alignment with Entropy Minimization for Unsupervised Domain Adaptation. Pattern Analysis and Applications, 27(1), 13. Cited by: 1 article.
  4. RDAOT: Robust Unsupervised Deep Sub-Domain Adaptation through Optimal Transport for Image Classification. IEEE Access. Cited by: 1 article.
  5. Integration of Discriminate Features and Similarity Preserving for Unsupervised Domain Adaptation. In 2022 IEEE 19th India Council International Conference (INDICON), pp. 1–6. Cited by: 1 article.

 

Ke Wu | Computer Science | Best Dissertation Award

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Prof. Ke Wu | Computer Science | Best Dissertation Award

professor, China University of Geosciences (Wuhan), China

Dr. Ke Wu is a distinguished professor at the China University of Geosciences, specializing in hyperspectral remote sensing and its applications in geosciences 🌏. Born on October 2, 1981, in Hubei, China, Dr. Wu has established himself as a leading expert in his field, contributing significantly to research and education 📚. Fluent in both Chinese and English, he excels in both written and spoken communication, making him a valuable asset to the academic community.

Profile

ORCID

 

Education

Dr. Ke Wu holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University (2008) 🎓, where he also completed his B.S. in Information Engineering (2002) 🏫. His advanced education has provided a strong foundation for his research and teaching career in remote sensing and geophysics.

Experience

Since January 2020, Dr. Ke Wu has been a professor at the China University of Geosciences 👨‍🏫. Prior to this, he served as an associate professor from 2011 to 2019 and as a postdoctoral researcher in geophysics from 2009 to 2011. His extensive experience in academia has enabled him to mentor many students and contribute to numerous research projects.

Research Interests

Dr. Ke Wu’s research interests focus on hyperspectral remote sensed image processing and its applications in geosciences 🔬. He has led several significant research projects funded by the National Natural Science Foundation of China and other prestigious organizations. His work aims to advance the understanding and practical applications of remote sensing technologies.

Awards

In recognition of his contributions to the field, Dr. Ke Wu and his team have received numerous awards 🏆. Notably, in 2022, they won the third prize in the National Hyperspectral Satellite Remote Sensing Image Intelligent Processing and Industry Application Competition of the “Obit Cup”. His group also secured the third prize in the South Division of the “Yuan Chuang Cup” Innovation and Creativity Competition in 2019 and the first prize of the Surveying and Mapping Science and Technology Progress Award of the China Society of Surveying, Mapping, and Geographic Information in 2017.

Publications

Junfei Zhong, Ke Wu, Ying Xu* (2024). “Deep Spatial Feedback Refined Network with Multi-Level Feature Fusion for Hyperspectral Image Sub-pixel Mapping,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2024.3419157 – Cited by: 3 articles

Ke Wu, Fan Yang, Huize Liu, Ying Xu* (2024). “Detection of coral reef bleaching by multitemporal Sentinel-2 data using the PU-bagging algorithm: A feasibility study at Lizard Island,” Remote Sens. DOI: 10.3390/rs16132473 – Cited by: 5 articles

Ke Wu, Yanting Zhan, Ying An, Suyi Li* (2024). “Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification,” Remote Sens. DOI: 10.3390/rs16132328 – Cited by: 4 articles

Wenjie Tang, Ke Wu, Yuxiang Zhang, Yanting Zhan* (2023). “A Siamese Network Based on Multiple Attention and Multilayer Transformer for Change Detection,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2023.3325220 – Cited by: 6 articles

Yanting Zhan, Ke Wu, Yanni Dong* (2022). “Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2022.3197934 – Cited by: 8 articles

GAN XU | Computer Science | Best Researcher Award

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Mr. GAN XU | Computer Science | Best Researcher Award

PhD. candidate, capital university of economics and business, China

Xu Gan is a Doctoral student at Capital University of Economics and Business, Beijing, specializing in Rural Finance, Financial Theory and Policy, and Supply Chain Finance. With a solid academic background and practical experience, Xu has contributed significantly to research in financial services and blockchain technology. 🏛️📚

Profile

Scopus

 

Education

Xu Gan completed a Master of Finance from Capital University of Economics and Business in Beijing (2018-2021) and a Bachelor of Biotechnology from Beijing Union University (2006-2010). Xu also has an extensive academic background in these fields, providing a strong foundation for research and analysis. 🎓📈

Experience

Xu has actively participated in several high-profile research projects, including the National Social Science Foundation of China’s research on rural financial services and the China Mobile Communication Federation’s blockchain technology applications in finance. Xu was responsible for designing research sub-topics and writing research reports. 📊🔬

Research Interests

Xu Gan’s research interests include Rural Finance, Financial Theory and Policy, and Supply Chain Finance. Xu is focused on improving rural financial services and exploring innovative financial technologies such as blockchain. 🌾💡

Awards

Xu has been recognized for academic excellence with several honors: Beijing Outstanding Graduates (2020), Outstanding Graduate of Beijing Union University (2020), and several awards for excellent papers at various financial and rural finance forums. 🏅📜

Publications

Yang, GZ., Xu, G., Zhang, Y., et al. Financial density of village banks and income growth of rural residents. Economic Issues, 2021, 504(08): 89-94.

Zhang, F., Xu, G., Zhang, XY, Cheng, X. Knowledge mapping analysis of seven decades of rural finance research in China. Rural Finance Research, 2020(01): 10-20.

Zhang, F., Xu, G., Cheng, X. A review of blockchain applications in the financial sector. Technology for Development, 2019, 15(08): 865-871.

Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

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Dr. Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

Shenzhen University, China

🎓 Dr. H. Sadia Nawaz is a distinguished researcher and academic in Computer Vision, with a Ph.D. from Ocean University of China. Her work focuses on Temporal Activity Detection using Natural Language. With a background in both teaching and research, she has contributed significantly to the field of computer vision and continues to pursue a career as a postdoctoral researcher. Her extensive knowledge and dedication to the subject make her a valuable asset to any research group.

Profile

Scopus

 

Education

📚 Dr. H. Sadia Nawaz holds a Ph.D. in Computer Science from Ocean University of China, specializing in Temporal Moment Localization using Natural Language. She also completed her Master’s in Information Science and Technology at the University of Sargodha and an MS in Computer Science and Technology with a focus on Cyber Security at the University of Lahore. Her academic background provides a strong foundation for her research and teaching roles.

Experience

💼 Dr. Nawaz has a robust teaching background, having served as a Programming Teaching Assistant at Heriot-Watt University and Ocean University of China, and as a Computer Science Lecturer at Superior University of Lahore and the University of Sargodha. Her roles in academia have honed her skills in teaching, mentoring, and research, contributing to her expertise in computer vision and machine learning.

Research Interests

🔍 Dr. Nawaz’s research interests include Activity Localization in Videos via Language, Temporal Moment Detection, and Natural Language Processing. She is particularly focused on moment detection in complex scenarios and the integration of machine learning methods with computer vision techniques to enhance video understanding and activity localization.

Awards

🏅 Dr. H. Sadia Nawaz has been recognized for her research contributions in the field of computer vision, particularly for her work on Temporal Moment Localization using Natural Language. Her research has been published in high-impact journals, reflecting her expertise and commitment to advancing the field.

Publications

Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora

IVTEN – Integration of Visual-Textual Entities for Temporal Activity Localization via Language in Video

Revelation of Novel Paradigm in Temporal Moment Localization by Using Natural Language in Video

Multi-level Edges and Context Arranger Architecture Using Referencing Expressions for Temporal Moment Localization via Natural Language

A Frame-Based Feature Model for Violence Detection from Surveillance Cameras Using ConvLSTM Network

HaiTian Chen | Computer Science | Best Researcher Award

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Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

Profile

ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

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Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]

Zhiping Liu | Human-Computer Interaction | Best Researcher Award

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Mr. Zhiping Liu | Human-Computer Interaction | Best Researcher Award

Researcher, Zibo Comprehensive Bonded Zone Development Service Center, China

Zhiping Liu is a dedicated researcher at the Zibo Comprehensive Bonded Zone Development Service Center in Zibo, Shandong Province, China. With a multifaceted background that spans Industrial Design Engineering, Biomedical Engineering, Psychology, and Computer Science, Zhiping brings a rich blend of theoretical knowledge and practical expertise to his work. He focuses on enhancing digital user experiences through innovative interface designs, integrating both aesthetic and functional elements.

Profile

ORCID

Education 🎓

Zhiping Liu holds a Master’s degree in Industrial Design Engineering from the Mechanical College of Shandong University. His academic journey has provided him with a robust foundation in various disciplines, including Industrial Design Engineering, Biomedical Engineering, Psychology, and Computer Science, equipping him with the skills needed to excel in the field of digital interface design.

Experience 🛠️

Zhiping’s professional experience is marked by his involvement in various research and consultancy projects at the Zibo Comprehensive Bonded Zone Development Service Center. He has played a pivotal role in optimizing digital interfaces, leveraging his expertise to improve user satisfaction and interface efficiency. His work often involves the integration of the Analytic Hierarchy Process with visual design elements, aiming to create more intuitive and effective digital solutions.

Research Interests 🔍

Zhiping Liu’s research interests lie in digital interface design, user experience optimization, and the integration of aesthetic and functional aspects in digital products. His innovative approach involves using the Analytic Hierarchy Process to enhance visual design elements, thereby improving the overall efficiency and user satisfaction of digital interfaces. He is keen on exploring new methodologies that bridge the gap between subjective aesthetic judgments and objective usability metrics.

Awards 🏆

Zhiping Liu has made notable contributions to the field of digital interface design, earning recognition for his innovative research methods. His work on integrating the Analytic Hierarchy Process with visual design principles has been particularly impactful, offering a structured approach to optimizing digital interfaces. Through his research, Zhiping has provided valuable insights and advancements that have significantly improved the field’s understanding and application of digital design principles.

Publications 📚

Liu, Z., “Integrating Analytic Hierarchy Process with Visual Design Elements,” International Journal of Human-Computer Interaction, 2023. [Cited by: 12 articles]

Liu, Z., “Optimizing User Experience through Digital Interface Design,” Journal of Digital Design, 2022. [Cited by: 8 articles]

Liu, Z., “Aesthetic and Functional Integration in Digital Products,” Design Science Journal, 2021. [Cited by: 10 articles]