Yalong Tai | Flexible Sensor | Research Excellence Award

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Dr. Yalong Tai | Flexible Sensor | Research Excellence Award

Professor | Shenzhen Institute of Advanced Technology | China

Prof. Yanlong Tai is a researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, specializing in flexible interface electronics, reliability analysis, health monitoring and imaging interaction technologies. He holds doctoral training from institutions in China and Germany with a focus on advanced engineering and micro-nanotechnology, and serves as a principal investigator and leader of multiple joint laboratories involving academic and industrial partners. His professional experience includes dual appointments, committee memberships in national and regional professional societies, and editorial roles for international journals. He has conducted long-term collaborative research with internationally recognized experts across the United States, Europe and the Middle East, contributing extensively to interdisciplinary advancements. His work has resulted in numerous peer-reviewed publications in high-impact journals, international patents, and research outputs covered by global scientific media. He has led and participated in major research programs supported by national and international funding bodies across multiple regions, contributing to projects in fundamental research, technological innovation and translational applications. His contributions have been recognized through scientific awards from national organizations and professional societies, reflecting his influence in advancing micro-electromechanical systems, biomedical engineering and flexible electronic technologies.

Profiles: Google Scholar

Featured Publications

1. Chonghaile, T. N., Sarosiek, K. A., Vo, T. T., Ryan, J. A., Tammareddi, A., et al. (2011). Pretreatment mitochondrial priming correlates with clinical response to cytotoxic chemotherapy. Science, 334(6059), 1129–1133.

2. Tai, Y., Mulle, M., Ventura, I. A., & Lubineau, G. (2015). A highly sensitive, low-cost, wearable pressure sensor based on conductive hydrogel spheres. Nanoscale, 7(35), 14766–14773.

3. Tai, Y., Qian, J., Zhang, Y., & Huang, J. (2008). Study of surface modification of nano-SiO₂ with macromolecular coupling agent (LMPB-g-MAH). Chemical Engineering Journal, 141(1–3), 354–361.

4. Tai, Y. L., & Yang, Z. G. (2011). Fabrication of paper-based conductive patterns for flexible electronics by direct-writing. Journal of Materials Chemistry, 21(16), 5938–5943.

5. Chang, Y., Wang, D. Y., Tai, Y. L., & Yang, Z. G. (2012). Preparation, characterization and reaction mechanism of a novel silver-organic conductive ink. Journal of Materials Chemistry, 22(48), 25296–25301.

Prof. The nominee’s work advances flexible interface electronics and intelligent sensing technologies that enhance reliability, safety and precision in health monitoring and next-generation diagnostic systems. By integrating materials innovation with scalable engineering approaches, the research contributes to scientific progress, supports emerging industrial applications and drives global technological innovation with meaningful societal impact.

Zbigniew Ras | Recommender Systems | Best Researcher Award

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Dr. Zbigniew Ras | Recommender Systems | Best Researcher Award

Professor of Computer Science & KDD Lab Director at University of North Carolina at Charlotte, United States

Zbigniew W. Ras is a distinguished computer scientist renowned for his extensive contributions to artificial intelligence, data mining, and intelligent information systems. With a career spanning multiple decades, he has held professorships and research positions at leading institutions in the United States, Europe, and Asia. His research interests span a broad range of interdisciplinary areas, including sentiment analysis, medical informatics, music information retrieval, and recommender systems. He serves as Editor-in-Chief of the Journal of Intelligent Information Systems and has been a pivotal figure in various international academic boards and conference steering committees. Recognized for both his research and teaching, he has received numerous awards and honors, including inclusion in the global list of top scientists. His leadership extends beyond academia through advisory roles in global AI organizations and think tanks. Ras’s work reflects a rare blend of theoretical depth, innovative application, and commitment to the advancement of both knowledge and academic communities.

Professional Profile 

Google Scholar | Scopus Profile

Education

Zbigniew W. Ras has a rich and internationally respected academic background rooted in mathematics and computer science. He earned his M.S. in Mathematics and Ph.D. in Computer Science from the University of Warsaw, Poland. His academic journey advanced further with the prestigious D.Sc. (Habilitation) from the Polish Academy of Sciences, which marks a high level of scholarly independence and achievement in European academia. His accomplishments were further recognized when he was awarded the National Professorship Title by the President of Poland—one of the highest honors in the Polish academic system. This educational foundation not only highlights his expertise in formal systems and computation but also reflects a rigorous and progressive development of academic excellence. His training under globally respected mathematicians and computer scientists helped shape a career focused on both theoretical innovation and practical application in emerging areas of artificial intelligence, making him a well-rounded and influential academic figure.

Experience

Zbigniew W. Ras has accumulated a distinguished career in academia and research, with extensive international experience. He has been a professor at the University of North Carolina at Charlotte for several decades, where he also held leadership and mentoring roles. His experience includes appointments at the Polish-Japanese Academy of Information Technology, Warsaw University of Technology, and the Polish Academy of Sciences, where he contributed to both research and academic development. Ras has also held visiting professorships at respected institutions across Europe, North America, and Asia, including in Germany, Sweden, Spain, Italy, and Japan. Beyond academia, he has collaborated with industry through advisory roles with organizations in the United States, Switzerland, and Australia. His participation in international advisory boards and departmental councils reflects a strong commitment to institutional leadership. This wide-ranging experience underlines his global academic presence, multidisciplinary engagement, and capacity to foster international research collaboration and innovation.

Research Focus

Zbigniew W. Ras’s research encompasses a broad spectrum of topics within artificial intelligence and intelligent information systems. His work primarily focuses on data mining, granular computing, sentiment analysis, recommender systems, and actionability in decision-making processes. He has also explored emerging areas like music information retrieval, business and art analytics, and medical informatics, showcasing a commitment to applying AI methods across various domains. His research is characterized by a strong theoretical foundation coupled with practical applications, which has made significant contributions to fields such as flexible query answering and algebraic logic. Ras emphasizes the importance of actionable knowledge, striving to ensure that the outcomes of machine learning and data analysis can be effectively used in real-world decision contexts. His interdisciplinary focus demonstrates both depth and adaptability, enabling his work to impact diverse sectors including healthcare, education, cultural preservation, and enterprise intelligence, all while pushing the boundaries of computational and information sciences.

Award and Honor

Zbigniew W. Ras has received numerous awards and honors that reflect his excellence in both research and teaching. He was named among the world’s top 2% scientists in a list compiled by Stanford University and Elsevier, underscoring his global academic impact. He has received several institutional awards, including best paper honors and team achievement recognitions from Warsaw University of Technology. At the University of North Carolina at Charlotte, he was honored with multiple awards for outstanding faculty performance and mentorship, such as the Harshini V. de Silva Graduate Mentor Award. In addition, he was recognized as a finalist for major teaching excellence awards. Ras is also a Distinguished Fellow of the Kosciuszko Foundation’s Collegium of Eminent Scientists. His leadership roles include chairing and serving on advisory boards of several major academic conferences and societies, illustrating his influence within the international research community. These honors collectively affirm his sustained academic excellence and global recognition.

Publications Top Notes

  • Title: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Preface
    Authors: M Dorigo, M Birattari, GA Di Caro, R Doursat, AP Engelbrecht, D Floreano, ZW Ras, et al.
    Year: 2010
    Citations: 280

  • Title: Action-rules: How to increase profit of a company
    Authors: ZW Ras, A Wieczorkowska
    Year: 2000
    Citations: 256

  • Title: Multi-label classification of emotions in music
    Authors: A Wieczorkowska, P Synak, ZW Ras
    Year: 2006
    Citations: 175

  • Title: Association action rules
    Authors: ZW Ras, A Dardzinska, LS Tsay, H Wasyluk
    Year: 2008
    Citations: 105

  • Title: Action rules discovery: system DEAR2, method and experiments
    Authors: LS Tsay, ZW Ras
    Year: 2005
    Citations: 96

  • Title: The Wisdom Web: New Challenges for Web Intelligence (WI)
    Authors: J Liu, N Zhong, Y Yao, ZW Ras
    Year: 2003
    Citations: 95

  • Title: Action rules mining
    Authors: AA Tzacheva, ZW Ras
    Year: 2005
    Citations: 86

  • Title: ARAS: Action rules discovery based on agglomerative strategy
    Authors: ZW Ras, E Wyrzykowska, H Wasyluk
    Year: 2007
    Citations: 83

  • Title: Extracting emotions from music data
    Authors: A Wieczorkowska, P Synak, R Lewis, ZW Ras
    Year: 2005
    Citations: 80

  • Title: Discovering extended action-rules (System DEAR)
    Authors: ZW Ras, LS Tsay
    Year: 2003
    Citations: 80

  • Title: Foundations of Intelligent Systems
    Authors: ZW Ras, A Skowron
    Year: 1999
    Citations: 72

  • Title: Action rule extraction from a decision table: ARED
    Authors: S Im, ZW Ras
    Year: 2008
    Citations: 67

  • Title: Advances in Music Information Retrieval
    Authors: Z Ras, A Wieczorkowska
    Year: 2010
    Citations: 64

  • Title: How to support consensus reaching using action rules: a novel approach
    Authors: J Kacprzyk, S Zadrozny, ZW Ras
    Year: 2010
    Citations: 59

  • Title: Analysis of sound features for music timbre recognition
    Authors: X Zhang, ZW Ras
    Year: 2007
    Citations: 59

Conclusion

The publication record of Zbigniew W. Ras clearly demonstrates a sustained and impactful contribution to the fields of artificial intelligence, data mining, and intelligent information systems. His most cited works reveal a strong emphasis on action rule discovery, emotion analysis in music, and decision support systems—topics of continuing relevance in both academic and applied contexts. Collaborations with various researchers across countries and institutions also reflect his ability to work across disciplinary and cultural boundaries. The high citation counts of multiple papers show that his research has not only been influential but also foundational in shaping discussions and developments within the AI and data science communities. His scholarly output highlights both theoretical depth and practical innovation, underscoring his position as a thought leader in his domain. This consistent and wide-ranging academic influence positions him as a strong candidate for recognition through prestigious awards and honors in research excellence.

 

Yang Han | Computer Science | Best Researcher Award

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Dr. Yang Han | Computer Science | Best Researcher Award

Associate Researcher at Tianjin University, China

Yang Han is an emerging researcher with a strong academic background in mathematics, having completed both his Master’s and PhD at Nankai University, followed by a research position at Tianjin University. His work bridges mathematical theory and practical applications in engineering, focusing on areas such as topological data analysis, signal processing, and intelligent fault diagnosis. In recent years, he has published extensively in high-impact journals like IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals, and presented at reputable international conferences such as IEEE PESGM and ACPEE. His interdisciplinary research is marked by innovation and relevance, especially in appliance identification, load forecasting, and fault detection using advanced mathematical tools. Though early in his research career, Yang has demonstrated strong potential and a clear trajectory of growth. His dedication, academic rigor, and collaborative approach position him as a promising candidate for the Best Researcher Award.

🔹Professional Profile 

Google Scholar
ORCID Profile 

🏆Strengths for the Award

Yang Han demonstrates a highly impressive academic and research trajectory. With a strong foundation in mathematics from Nankai University, progressing through a Master’s and PhD (2015–2023), and currently holding an associate researcher position at Tianjin University, he shows continuity and growth in academic rigor. His research spans interdisciplinary areas, merging topological data analysis, signal processing, machine learning, and fault diagnosis—fields of significant importance in both academia and industry. Notably, his recent publications in high-impact journals such as IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals reflect both quality and innovation. Additionally, his contributions to top-tier conferences like IEEE PESGM and ACPEE signal strong peer recognition. The combination of applied AI techniques and deep mathematical theory shows versatility, a rare and commendable strength for a young researcher.

Areas for Improvement

While the publication record is strong and growing, most of the impactful work is very recent (primarily in 2024–2025), indicating that Yang Han is in the early stages of building a long-term research profile. Sustained contributions over a longer timeline will better establish him as a leading authority. Another point of improvement would be to take on more lead or sole authorship roles in future publications, as many current works are collaborative with shared credit, which can make it harder to isolate individual impact. Additionally, while his interdisciplinary work is a strength, expanding his network internationally through collaborations beyond China and participating in global research programs could enhance the visibility and influence of his work.

Conclusion

Yang Han is a highly promising and impactful early-career researcher with a unique blend of mathematical depth and applied AI-driven engineering. His recent output demonstrates a clear upward trajectory, both in productivity and innovation. While there is room to further solidify his independent research identity and global presence, his current achievements strongly support his candidacy for the Best Researcher Award. Given his solid grounding, interdisciplinary focus, and growing impact, he is indeed a suitable and deserving nominee for this recognition.

🎓Education

Yang Han began his academic journey at Nankai University, a prestigious institution known for mathematical excellence. From 2015 to 2018, he completed his Master’s degree at the School of Mathematical Sciences and LPMC, focusing on advanced mathematical theories and computational techniques. His strong academic performance and deep interest in topology, algebra, and their applications led him to continue his research as a PhD student in the same department from 2019 to 2023. During his doctoral studies, he expanded his expertise into applied mathematics and began to explore connections with engineering systems and data-driven problem solving. His doctoral research provided the foundation for his transition into interdisciplinary areas such as topological data analysis and graph signal processing. His time at Nankai University was marked by academic growth, critical thinking, and active participation in scholarly research. This rigorous educational background prepared him for a successful research career bridging mathematics and electrical engineering.

💼Experience

Yang Han currently holds the position of Associate Researcher at the School of Electrical and Information Engineering, Tianjin University. Since assuming this role in 2023, he has actively contributed to research in intelligent systems, signal processing, and data analytics. Before this, he spent nearly a decade at Nankai University, where he completed his Master’s and PhD studies, engaging in teaching support and foundational research. His experience spans a variety of projects focused on non-intrusive load monitoring, equipment fault diagnosis, and appliance identification—often leveraging advanced mathematical tools like topological data analysis and fast Fourier transforms. He has contributed to both national and international research collaborations, presented at prestigious conferences, and published in leading journals. His ability to blend abstract mathematical methods with real-world engineering challenges exemplifies his versatile experience. His role also involves mentoring junior researchers and contributing to interdisciplinary innovation at the intersection of mathematics, artificial intelligence, and electrical engineering.

🏆Awards and Honors

While formal individual awards are not explicitly listed in the available data, Yang Han’s growing list of high-impact publications and conference presentations serves as strong evidence of professional recognition. His work has been published in top-tier journals such as IEEE Transactions on Instrumentation and Measurement, Chaos, Solitons & Fractals, and Engineering Applications of Artificial Intelligence, reflecting a high level of peer recognition. He has also contributed to leading international conferences, including IEEE PESGM and the Asia Conference on Power and Electrical Engineering (ACPEE), where selection itself is a mark of merit. These platforms are known for their rigorous review processes, indicating that his work meets and often exceeds international research standards. Additionally, his involvement in collaborative, interdisciplinary projects and authorship in multiple papers shows that he is a valued team member in academic and industrial circles. As his career progresses, further formal awards and honors are likely to follow.

🔬 Research Focus on Computer Science

Yang Han’s research is centered at the intersection of applied mathematics, artificial intelligence, and electrical engineering. His primary focus lies in topological data analysis, signal processing, and machine learning techniques for complex system monitoring and fault detection. He has contributed significantly to non-intrusive load monitoring (NILM), using graph signal processing to identify energy consumption patterns without intrusive sensors. He also works on fault diagnosis through time-frequency analysis and the application of mathematical topology in real-world engineering systems. His innovative approach often involves transforming abstract mathematical concepts—such as Betti curves and topological invariants—into practical tools for appliance identification and power grid analysis. Furthermore, Yang Han is exploring adaptive methods for equipment behavior modeling and data-driven forecasting. This unique research blend offers both theoretical advancements and immediate practical value, demonstrating his ability to tackle emerging challenges in intelligent energy systems and industrial diagnostics with precision and depth.

📚 Publications Top Notes

  • Title: Energy dissipation analysis of elastic–plastic materials
    Authors: H Yang, SK Sinha, Y Feng, DB McCallen, B Jeremić
    Year: 2018
    Citations: 94

  • Title: Study on the mechanical behavior of sands using 3D discrete element method with realistic particle models
    Authors: WJ Xu, GY Liu, H Yang
    Year: 2020
    Citations: 46

  • Title: Nonlinear finite elements: Modeling and simulation of earthquakes, soils, structures and their interaction
    Authors: B Jeremić, Z Yang, Z Cheng, G Jie, N Tafazzoli, M Preisig, P Tasiopoulou, …
    Year: 2018
    Citations: 37

  • Title: The real-ESSI simulator system
    Authors: B Jeremić, G Jie, Z Cheng, N Tafazzoli, P Tasiopoulou, F Pisanò, JA Abell, …
    Year: 1988
    Citations: 35

  • Title: Study on the meso-structure development in direct shear tests of a granular material
    Authors: H Yang, WJ Xu, QC Sun, Y Feng
    Year: 2017
    Citations: 28

  • Title: Energy dissipation analysis for inelastic reinforced concrete and steel beam-columns
    Authors: H Yang, Y Feng, H Wang, B Jeremić
    Year: 2019
    Citations: 27

  • Title: Time domain intrusive probabilistic seismic risk analysis of nonlinear shear frame structure
    Authors: H Wang, F Wang, H Yang, Y Feng, J Bayless, NA Abrahamson, B Jeremić
    Year: 2020
    Citations: 22

  • Title: Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil
    Authors: C Kanellopoulos, N Psycharis, H Yang, B Jeremić, I Anastasopoulos, …
    Year: 2022
    Citations: 21

  • Title: Energy dissipation in solids due to material inelasticity, viscous coupling, and algorithmic damping
    Authors: H Yang, H Wang, Y Feng, F Wang, B Jeremić
    Year: 2019
    Citations: 20

  • Title: 3-d non-linear modeling and its effects in earthquake soil-structure interaction
    Authors: SK Sinha, Y Feng, H Yang, H Wang, B Jeremic
    Year: 2017
    Citations: 19

  • Title: Plastic-energy dissipation in pressure-dependent materials
    Authors: H Yang, H Wang, Y Feng, B Jeremić
    Year: 2020
    Citations: 18

  • Title: Relationship between multifunctionality and rural sustainable development: Insights from 129 counties of the Sichuan Province, China
    Authors: X Li, J Liu, J Jia, H Yang
    Year: 2022
    Citations: 17

  • Title: Modeling and simulation of earthquake soil structure interaction excited by inclined seismic waves
    Authors: H Wang, H Yang, Y Feng, B Jeremić
    Year: 2021
    Citations: 17

  • Title: An energy-based analysis framework for soil structure interaction systems
    Authors: H Yang, H Wang, B Jeremić
    Year: 2022
    Citations: 14

  • Title: A robust and efficient federated learning algorithm against adaptive model poisoning attacks
    Authors: H Yang, D Gu, J He
    Year: 2024
    Citations: 11

Milind Cherukuri | Computer Science | Young Researcher Award

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Mr. Milind Cherukuri | Computer Science | Young Researcher Award

Salesforce Business Analyst & Administrator at University of North Texas, United States

Milind Cherukuri is a dynamic early-career researcher and technologist with a strong foundation in artificial intelligence, machine learning, and software engineering. With a Master’s in Computer Science from the University of North Texas, he has applied his expertise across leading organizations such as Caris Life Sciences, Amazon, and Infor. His research spans sentiment analysis, AI safety, LLM prompt engineering, and image segmentation, resulting in five peer-reviewed publications and presentations at major conferences like IEEE AI Summit and EEET 2024. Milind has a proven ability to translate research into real-world impact, particularly in healthcare, where he optimized clinical systems through AI-driven automation and data integration. Recognized as a Senior Member of IEEE in 2025, he actively contributes to the research community through peer review and technical leadership. His innovative mindset, technical depth, and cross-domain contributions position him as a strong candidate for the Young Researcher Award.

Professional Profile

Google Scholar

Education

Milind Cherukuri holds a Master’s degree in Computer Science from the University of North Texas, where he deepened his expertise in artificial intelligence, data science, and advanced software systems. Prior to that, he earned his Bachelor’s degree in Computer Science from SRM University, Chennai, India. His academic journey reflects a consistent focus on technical excellence, with coursework and projects covering machine learning, sentiment analysis, and cloud computing. During his graduate studies, Milind engaged in applied research initiatives and honed his skills in experimental design, statistical analysis, and academic writing. He leveraged these experiences to produce scholarly work and effectively bridge theory with practice. His education provided a strong foundation for multidisciplinary research, particularly in AI-driven applications across healthcare and enterprise environments. The blend of technical depth and research exposure during his formative academic years has directly influenced his ability to contribute meaningfully to both industrial innovation and scientific advancement.

Professional Experience

Milind Cherukuri’s professional journey spans prominent roles at Caris Life Sciences, Amazon, and Infor, reflecting a robust blend of research, software development, and systems integration experience. At Caris Life Sciences, he currently serves as a Salesforce Business Analyst and Administrator, where he leads automation, healthcare data integration, and clinical research optimizations. His work has directly impacted clinical decision-making by aligning technology with operational and regulatory needs. At Amazon, he developed scalable microservices, optimized APIs, and applied AI insights to enhance customer experience and personalization. Prior to that, at Infor in India, Milind supported legacy modernization and contributed to internal research on sentiment analysis and recommendation systems. Across these roles, he demonstrated an ability to innovate at scale while contributing to internal research pipelines and tool development. His hands-on experience across cloud platforms, AI tools, and enterprise software showcases a rare ability to move seamlessly between engineering execution and applied research.

Research Interest

Milind Cherukuri’s research interests lie at the intersection of artificial intelligence, machine learning, sentiment analysis, and safe AI deployment. He is passionate about building explainable, reliable, and application-driven AI systems that serve real-world domains such as healthcare, e-commerce, and cloud ecosystems. His work focuses on areas like multi-dimensional emotion representation, AI safety frameworks for large language models, and optimization techniques for prompt engineering. Milind is particularly interested in how AI can be made more context-aware, ethically responsible, and efficient when integrated into critical infrastructure. His research explores both the theoretical underpinnings of AI algorithms and their translation into user-centric applications. He uses tools such as TensorFlow, scikit-learn, Databricks, and Keras for prototyping and experimentation. Milind’s commitment to conducting reproducible and impactful research is evident through his multiple peer-reviewed publications and active participation in academic peer review and conference presentations.

Award and Honor

Milind Cherukuri has received several accolades that underscore his excellence in both research and professional performance. In 2025, he was elevated to the grade of Senior Member of IEEE, recognizing his significant contributions to engineering and AI research at a relatively early stage in his career. He has authored five peer-reviewed publications across reputable venues and conferences, including IEEE AI Summit and EEET 2024. His work has been cited in discussions on AI safety and ethics, especially regarding GPT-5 development strategies. Within industry roles, Milind earned recognition for developing fault-tolerant systems at Amazon and for improving automation workflows at Caris Life Sciences, boosting operational efficiency by over 30%. He has also contributed as a peer reviewer for research journals, enhancing his engagement with the broader scientific community. These honors reflect a balanced profile of innovation, leadership, and commitment to advancing technology responsibly and effectively.

Conclusion

Milind Cherukuri embodies the qualities of a forward-thinking, multidisciplinary researcher who bridges the worlds of academia and industry with exceptional skill. His educational foundation, professional achievements, and focused research trajectory demonstrate a rare combination of depth and adaptability. From developing scalable software at Amazon to integrating AI solutions in clinical workflows at Caris Life Sciences, he has consistently shown the ability to convert research insights into real-world impact. Milind’s publications, IEEE recognition, and conference engagements highlight his dedication to advancing AI in safe, ethical, and application-driven ways. His involvement in peer review and technical documentation further signals his readiness to contribute to and shape the global research landscape. With a passion for innovation, a track record of scholarly contributions, and strong industry credibility, Milind stands out as a compelling candidate for honors such as the Young Researcher Award, and is poised for continued impact in the field of computer science and artificial intelligence.

Publications Top Notes

  • Title: Comparing Image Segmentation Algorithms
    Author: M. Cherukuri
    Year: 2024
    Citations: 3

  • Title: Cost, Complexity, and Efficacy of Prompt Engineering Techniques for Large Language Models
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: WebChecker: A Versatile EVL Plugin for Validating HTML Pages with Bootstrap Frameworks
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: Advancing AI Safely: Frameworks and Strategies for the Development of GPT-5 and Beyond
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: Exploring Multi-Dimensional Sentiment Analysis: A Study on Emotion Representation Structures and Prediction Models
    Author: M. Cherukuri
    Year: 2024

Yanming Zhao | Computer Science | Best Researcher Award

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Prof. Yanming Zhao | Computer Science | Best Researcher Award

Professor at Hebei MINZU Normal University, China

Yanming Zhao is a distinguished Professor at Hebei University of Nationalities, specializing in visual computing and deep neural networks. With a commitment to advancing technology and innovation, he has made significant contributions to the field of computer application technology, evidenced by his extensive research and numerous publications. 🌟

Profile 

Scopus Profile

Education🎓

Yanming graduated with a Master’s degree in Computer Application Technology from the School of Information at Shenyang University of Technology in 2010. His academic background laid a solid foundation for his future research endeavors and leadership in academia.

Experience🏛️💼

As a Master’s Supervisor and experienced researcher, Professor Zhao has participated in over nine provincial-level research projects and has consulted on over 500 industry projects. His work not only showcases his expertise but also his dedication to bridging the gap between academia and industry.

Research Interests🔬📈

Professor Zhao’s research primarily focuses on visual computing and deep neural networks. He has developed innovative algorithms, including the visual selectivity-based 3D graph convolutional algorithm (VS-3DGCN), aimed at enhancing point cloud segmentation performance and addressing key challenges in 3D graph convolutional algorithms.

Awards 🏆

Throughout his career, Yanming has received numerous accolades, including the title of Excellent Scientific and Technological Worker in Hebei Province and Outstanding Expert Managed by Chengde City. These awards reflect his significant contributions to the scientific community and his leadership in research.

Publications

Professor Zhao has published more than 30 academic papers in esteemed journals, such as:

  • Multi-channel depth segmentation network based on 3D graph convolution algorithm and its application in point cloud segmentation
    • Authors: Zhao, Y.
    • Journal: Alexandria Engineering Journal
    • Year: 2024
    • Citations: 0
  • The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud
    • Authors: Fan, H., Zhao, Y., Su, G., Zhao, T., Jin, S.
    • Journal: Traitement du Signal
    • Year: 2023
    • Citations: 4
  • Graph Convolution Algorithm Based on Visual Selectivity and Point Cloud Analysis Application
    • Authors: Zhao, Y., Su, G., Yang, H., Jin, S., Yang, J.
    • Journal: Traitement du Signal
    • Year: 2022
    • Citations: 2
  • Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application
    • Authors: Yang, H., Zhao, Y., Su, G., Fan, H., Shang, Y.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 0
  • Design and application of a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks
    • Authors: Zhao, Y., Yang, H., Su, G.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 1

These contributions have garnered a total citation index of 102 times, illustrating the impact of his work on the research community. 📚🔗

Conclusion🌍✨

In summary, Professor Yanming Zhao stands out as a leading figure in the fields of visual computing and deep learning. His extensive research, numerous publications, and accolades make him a deserving candidate for the Best Researcher Award. His ongoing commitment to innovation and excellence continues to inspire colleagues and students alike.