Khawaja Iftekhar Rashid | Computer Science | Research Excellence Award

Published on by Academic Awards

Dr. Khawaja Iftekhar Rashid | Computer Science | Research Excellence Award

Xiamen University | China

Dr. Khawaja Iftekhar Rashid is an emerging researcher in artificial intelligence, machine learning, and computer vision, with a strong specialization in semantic image segmentation for urban scenes, autonomous driving, and medical imaging. His work focuses on advanced deep learning models, including attention mechanisms, GANs, vision transformers, graph neural networks, and semi-/few-shot learning frameworks. He has published in high-impact, peer-reviewed journals such as Neurocomputing, Engineering Applications of Artificial Intelligence, and Expert Systems with Applications, reflecting both theoretical innovation and real-world applicability. His research profile demonstrates growing scholarly impact with 46 citations, an h-index of 5, and an i10-index of 1.

Citation Metrics (Scopus)

50

40

30

20

10

0

Citations 45

Documents 8

h-index
5

Citations
Documents
h-index


View Google Scholar Profile

Featured Publications

Rounak Raman | Information Technology | Outstanding Scientist Award

Published on by Academic Awards

Mr. Rounak Raman | Information Technology | Outstanding Scientist Award

Netaji Subhas University of Technology | India

Mr. Rounak Raman is an emerging researcher specializing in computer networking, IoT security, wireless sensor networks, AI-driven network management, and Generative AI. His scholarly contributions include CONTEXT-NET, a context-aware aggregation protocol for opportunistic networks, and ARMor-IoT, a trust-optimized mechanism enhancing IoT reliability, reflecting innovation in secure communication systems. He has also developed EAHCP, an energy-aware hybrid clustering protocol improving network lifetime, and HKRISRP, a hierarchical key-rotation framework for strengthened WSN security. His interdisciplinary work spans neurofeedback analytics, semantic search, YOLO-based computer vision, and enterprise generative AI tools. Overall, his research demonstrates strong technical depth, real-world impact, and a focus on secure, intelligent, and energy-efficient networked systems.

Citation Metrics (Google Scholar)

6
4
2
0

Citations
5

Documents
2

h-index
1

Citations

Documents

h-index

View Google Scholar Profile

Featured Publications

ARMor-IoT: Aggregated Reliable Mechanism for Optimized Trust in IoT
– International Conference on Artificial Intelligence and Its Application, 2025

Kachi Anvesh | Machine Learning | Best Researcher Award

Published on by Academic Awards

Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Lingxin Jin | Computer Science | Best Researcher Award

Published on by Academic Awards

Dr. Lingxin Jin | Computer Science | Best Researcher Award

Dr. Lingxin Jin, University of Electronic Science and Technology of China

Dr. Lingxin Jin, based in Chengdu, China, is a Ph.D. candidate in Software Engineering at the University of Electronic Science and Technology of China, where he also completed his Bachelor’s degree with a GPA of 3.8/4.0. His academic focus includes artificial intelligence, machine learning, network security, and software systems. Dr. Jin has gained international experience through an exchange program at the International Technological University in Silicon Valley and held internships involving front-end development and research on backdoor attacks against deep neural networks. His research contributions include publications in high-impact journals such as IEEE Transactions on Computers and the Journal of Circuits, Systems, and Computers, with additional submissions to ACM and IJCAI. Dr. Jin has worked on projects ranging from Linux shell simulations to public opinion analysis systems. He has received several scholarships and honors, including direct Ph.D. program recommendation, and is recognized for his promising research in AI security and adversarial attacks.

Publication Profile

Scopus

 Orcid

🎓 Educational Background

Dr. Lingxin Jin pursued his academic journey in Software Engineering at the University of Electronic Science and Technology of China. He completed his Bachelor’s degree from September 2018 to June 2022, achieving an impressive GPA of 3.8/4.0. During his undergraduate studies, he built a strong foundation through comprehensive coursework in Software Engineering, Computer Networks, Operating Systems, and Artificial Intelligence. Driven by academic excellence, Dr. Jin was recommended for direct entry into the Ph.D. program, which he began in September 2022. Currently, he is a Ph.D. candidate in Software Engineering at the same university, maintaining a GPA of 3.71/4.0. His advanced studies focus on cutting-edge topics such as Information Security Fundamentals and Frontiers, Network Security Theory and Technology, Machine Learning Theory and Algorithms, and Statistical Machine Learning. This academic background highlights his commitment to research and innovation in secure intelligent systems and computational technologies.

💼 Professional Experience

Dr. Lingxin Jin has gained diverse and valuable professional experience that complements his academic pursuits in software engineering and artificial intelligence. In July 2019, he participated in an exchange program at the International Technological University in Silicon Valley, where he engaged in programming robot motion manipulation using Raspberry Pi and Arduino, as well as composing songs using MATLAB—demonstrating his multidisciplinary creativity. From January to June 2021, Dr. Jin interned at Xi’an Deta Information Technology Co., where he focused on front-end development and contributed to building an opinion analysis system. This role honed his skills in UI/UX and real-time data interpretation. He later served as a Software Engineer Intern at Sichuan Meiliankai Science and Technology Co. from September 2021 to June 2022, where he conducted advanced research on backdoor attacks against deep neural networks. These experiences collectively reflect Dr. Jin’s technical versatility and growing expertise in cybersecurity and intelligent systems.

🏅 Additional Experience and Awards

Dr. Lingxin Jin has consistently demonstrated academic excellence throughout his educational journey, earning multiple awards and recognitions. During his undergraduate studies from 2018 to 2022 at the University of Electronic Science and Technology of China, he was honored with first and second-class scholarships for outstanding academic performance. His dedication and scholarly achievements earned him a prestigious recommendation for direct admission into the Ph.D. program, a distinction reserved for top-performing students. As a postgraduate student from 2022 onward, Dr. Jin continued to excel, receiving scholarships for new students as well as second-class scholarships for academic distinction. These accolades not only highlight his strong academic capabilities but also reflect his commitment to advancing in the field of software engineering and artificial intelligence. Dr. Jin’s consistent recognition at both undergraduate and postgraduate levels underscores his potential as a future leader in cutting-edge technological research and innovation.

🧠 Research Focus

Dr. Lingxin Jin’s research primarily focuses on adversarial machine learning, with a particular emphasis on Trojan attacks and security vulnerabilities in deep neural networks (DNNs). His scholarly work explores the life-cycle threats faced by DNNs, covering both attack strategies and defensive countermeasures. His publication in ACM Computing Surveys titled “Trojan Attacks and Countermeasures on Deep Neural Networks from Life-Cycle Perspective” provides a comprehensive overview of attack surfaces throughout a model’s development and deployment phases. Additionally, his work in IEEE Transactions on Computers, “Highly Evasive Targeted Bit-Trojan on Deep Neural Networks”, introduces novel methods of crafting stealthy, highly targeted Trojans that evade standard detection techniques. Through these contributions, Dr. Jin is advancing the field of AI security, focusing on the resilience and trustworthiness of neural networks in critical applications. His research is vital for developing robust defense frameworks and ensuring safe deployment of AI systems in real-world scenarios.

Publication Top Notes

  • 📄 2024: “Highly Evasive Targeted Bit‑Trojan on Deep Neural Networks” (IEEE Trans. on Computers) – DOI:10.1109/TC.2024.3416705; introduces stealthy bit-level Trojans; cited 2 times

  • 📄 2023: “Iterative Training Attack: A Black‑Box Adversarial Attack via Perturbation Generative Network” (J. of Circuits, Systems and Computers) – DOI:10.1142/S0218126623503140; black-box generative adversarial method;

  • 📄 2023: “A Survey of Trojan Attacks and Defense to Neural Networks” (under review at ACM Computing Surveys); comprehensive lifecycle review of Trojan threats

  • 📄 2024: “Data Poisoning‑based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks” (submitted to IJCAI ’25); extends backdoor threats to spiking neural models

 

Rania Hamdani | Computer science | Best Researcher Award

Published on by Academic Awards

Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

Publication Profile

Orcid

🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

Jian Zhao | Image processing | Best Researcher Award

Published on by Academic Awards

Dr. Jian Zhao | Image processing | Best Researcher Award

Lecturer at Nanjing Institute of Technology, China

Dr. Jian Zhao is a Lecturer at the School of Computer Engineering, Nanjing Institute of Technology. He earned his PhD in Physical Electronics from Southeast University (2019) and was a visiting scholar at Newcastle University, UK, specializing in Stereoscopic Vision. His research focuses on light field displays, deep learning for micro-expression analysis, and ultrafast spatial light modulation. He has secured multiple grants, including from the National Natural Science Foundation of China. Dr. Zhao has published in OPTICS EXPRESS, IEEE Photonics Journal, and IET Image Processing, contributing significantly to computational imaging and display technologies. 📡📸

Publication Profile

Orcid

Educational Background 🎓📚

Dr. Jian Zhao holds a Doctoral Degree in Physical Electronics from Southeast University (2012-2019), where he specialized in advanced optical and electronic systems. To enhance his expertise, he pursued a research stay as a visiting student at Newcastle University, UK (2017-2018), focusing on stereoscopic vision. His academic journey reflects a strong foundation in optics, imaging, and display technologies, equipping him with the skills to innovate in light field displays and computational imaging. His international experience has further broadened his research perspective, enabling him to contribute to cutting-edge developments in visual perception and display systems. 🌍🔬

Research and Academic Work Experience 🔬📡

Dr. Jian Zhao has led multiple research projects in cutting-edge imaging and display technologies. He has secured funding from the National Natural Science Foundation of China for projects on deep network models for micro-expression analysis in complex environments and ultrafast phase-type spatial light modulation using disordered structure metasurfaces. Additionally, his work, supported by the Natural Science Foundation of Jiangsu Province, explores near-eye light field imaging with polarization volume holographic gratings. He also received funding from the Jiangsu Provincial Department of Education to study near-eye display systems based on human visual perception. His research contributes significantly to computational imaging advancements. 🎥📊

Research Focus Areas

Dr. Jian Zhao specializes in computational imaging, display technology, and deep learning applications. His research spans autostereoscopic displays 🖥️, light field imaging 📸, and human visual perception 👀. He applies AI and deep learning 🤖 to urban waterlogging detection 🌊, visual fatigue assessment 👓, and surface defect detection 📱. His expertise extends to virtual avatars 🧑‍💻 and photonic nanotechnology 🔬. Dr. Zhao contributes significantly to metasurface optics, spatial light modulation, and advanced display systems. His interdisciplinary work impacts computer vision, optoelectronics, and smart imaging technologies. 🚀✨

Publication Top Notes

  • 2025: “Urban Waterlogging Monitoring and Recognition in Low-Light Scenarios Using Surveillance Videos and Deep Learning”

    semanticscholar.org

  • 2024: “A Multimodal Visual Fatigue Assessment Model Based on Back Propagation Neural Network and XGBoost”

    semanticscholar.org

  • 2023: “Study on Random Generation of Virtual Avatars Based on Big Data”

    semanticscholar.org

  • 2023: “Viewing Zone Expansion of Autostereoscopic Display With Composite Lenticular Lens Array and Saddle Lens Array”

    semanticscholar.org

  • 2023: “Mobile Phone Screen Surface Scratch Detection Based on Optimized YOLOv5 Model (OYm)”

    semanticscholar.org

  • 2019: “Spatial Loss Factor for the Analysis of Accommodation Depth Cue on Near-Eye Light Field Displays”

    semanticscholar.org

  • 2019: “Tilted LCD Pixel With Liquid Crystal GRIN Lens for Two-Dimensional/Three-Dimensional Switchable Display”

    semanticscholar.org

  • 2019: “Hybrid Computational Near-Eye Light Field Display”

    semanticscholar.org

  • 2019: “Switchable Photonic Nanojet by Electro-Switching Nematic Liquid Crystals”

    semanticscholar.org

 

Mohit Kataria | Machine Learning | Best Researcher Award

Published on by Academic Awards

Mr. Mohit Kataria | Machine Learning | Best Researcher Award

Professor at IIT-Delhi

📌  Mohit Kataria is a 4th-year Ph.D. scholar at the School of Artificial Intelligence, IIT Delhi, India, specializing in Graph Machine Learning. His research focuses on scalability of graph algorithms, including graph coarsening, structure learning, federated learning, and large-scale applications. He has published in top venues like NeurIPS, MICAAI, and CBME. Mohit holds a Master’s in Computer Applications (80.1%) and has expertise in Python, PyTorch, TensorFlow, CUDA, and C/C++. His skill set spans deep learning (GNNs, CNNs, RNNs), machine learning (SVM, XGBoost), and mathematical optimization.

Publication Profile

Google Scholar

Academic Background 🎓🔬

📌 Mohit Kataria is a Ph.D. scholar in Graph Machine Learning at the MISN Lab, IIT Delhi, maintaining an 8.0 CGPA since August 2021. He holds a Master’s in Computer Applications (80.1%) from May 2020. His technical expertise spans Python, PyTorch, TensorFlow, CUDA, MPI, C/C++, Java, MySQL, and Erlang. 🖥️ He specializes in Machine Learning (SVM, Random Forest, XGBoost, Decision Trees) and Deep Learning (ANNs, GNNs, CNNs, RNNs, LSTM, VAE, GANs). 📊 His strong foundation in Linear Algebra, Probability, and Optimization fuels his research in scalable graph algorithms and AI applications. 🚀

💼 Professional Experience of Mohit Kataria

📌 Mohit Kataria has been actively involved in AI/ML training at IIT Delhi (2021-Present), where he has helped train 260+ industry experts in a six-month AI/ML program, covering fundamentals to advanced ML models. 🎓 He also conducted 5-day ML training programs for CAG and CRIS, Government of India. As a WebMaster (2022-Present), he manages the Yardi-ScAI and MISN group websites. 🌐 Previously, as a Member of Technical Staff at Octro.Inc (2020-2021), he led a team of four and contributed to the backend architecture of multiplayer games like Poker3D and Soccer Battles. 🎮🚀

🔬 Research Focus of Mohit Kataria

📌 Mohit Kataria specializes in Graph Machine Learning, focusing on graph coarsening, structure learning, and scalable AI applications. His work enhances GNN performance on heterophilic datasets 🧠, improves large-scale single-cell data analysis 🧬, and optimizes histopathological image processing 🔍. His research, published in NeurIPS, MICAAI, and CBME, develops efficient graph-based frameworks for biomedical and computational applications. 🏥 His expertise spans AI-driven healthcare, graph-based AI models, and machine learning scalability, making significant contributions to bioinformatics, medical imaging, and large-scale data processing. 🚀

Publication Top Notes 

 

 

 

Zhidan Ran | Computer Vision | Best Researcher Award

Published on by Academic Awards

Dr. Zhidan Ran | Computer Vision | Best Researcher Award

Dr. Zhidan Ran, Southeast University, China

Dr. Zhidan Ran is a Ph.D. candidate in Control Science and Engineering at Southeast University, specializing in computer vision, person re-identification, and image retrieval. With multiple high-impact publications in IEEE Transactions and Pattern Recognition, he focuses on advancing security technologies through person re-identification and anomaly detection. He holds several patents, including methods for oil stain detection in vehicles. Dr. Ran has received notable awards, such as the Jiangsu College Student Electronic Design Competition (First Prize). His contributions to AI and automation continue to push boundaries in both theory and application. 🧠✨

 

Publication Profile

Scopus

Education 🎓

Dr. Zhidan Ran has pursued all levels of his higher education at Southeast University, Nanjing, China, showcasing his dedication to academic excellence. He is currently a Ph.D. candidate in Control Science and Engineering (2021–present), under the guidance of Dr. Xiaobo Lu, focusing on advanced technologies in computer vision and automation. Previously, he completed his Master’s degree (2019–2021) in the same field, mentored by Dr. Haikun Wei, where he deepened his expertise in innovative control systems. Dr. Ran earned his Bachelor’s degree in Automation (2015–2019), laying the foundation for his impactful career in automation and engineering. 🌟📚

 

Research Interests

Dr. Zhidan Ran is a dedicated researcher specializing in computer vision, person re-identification, and image retrieval. His work focuses on leveraging advanced technologies to improve security and automation systems. As a Ph.D. candidate in Control Science and Engineering at Southeast University, he has contributed to several cutting-edge projects and high-impact publications. His expertise in developing innovative solutions for image-based recognition and retrieval demonstrates his commitment to advancing AI and machine learning applications. Dr. Ran’s research aims to bridge theoretical advancements and real-world implementations, driving progress in smart systems and intelligent automation. 🧠✨

 

Awards and Achievements

Dr. Zhidan Ran has been honored with numerous prestigious awards, showcasing his exceptional talent in technology and innovation. He secured first prize in the Jiangsu College Student Electronic Design Competition (2018) and achieved third prize in both the China College Students Computer Design Competition and the Jiangsu Mathematical Contest in Modeling (2017). His ingenuity was further recognized with an Excellence Award at the Southeast University Smart Car Competition (2017). Additionally, he earned the coveted Southeast University President Scholarship for 2016-2017. These accolades reflect his dedication to pushing the boundaries of automation and engineering. 🥇🤖

 

Research Focus

Dr. Zhidan Ran specializes in cutting-edge research areas, including computer vision, person re-identification, and image retrieval. His work extends to video-based anomaly detection and camera domain adaptation, as evident in studies like Multiscale Aligned Spatial-Temporal Interaction and Camera Domain Adaptation Using Transformers. Additionally, he contributes to transportation safety, focusing on oil stain detection for high-speed trains through advanced networks like MFANet and PCCN. With innovations in top-view fisheye cameras and adaptive frameworks, Dr. Ran’s interdisciplinary expertise bridges automation and visual intelligence, pushing the boundaries of smart systems and transport technologies. 🚉📷💡

 

Publication Top Notes  

📝 Anomaly-Aware Semantic Self-Alignment Framework for Video-Based Person Re-Identification (2024) – Cited by: 0
📝 Multiscale Aligned Spatial-Temporal Interaction for Video-Based Person Re-Identification (2024) – Cited by: 0
🛤️ MFANet: Multifaceted Feature Aggregation Network for Oil Stains Detection of High-Speed Trains (2023) – Cited by: 2
📷 DCPB: Deformable Convolution Based on the Poincaré Ball for Top-view Fisheye Cameras (2023) – Cited by: 0
🛠️ PCCN: Progressive Context Comprehension Network for Oil Stains Detection of High-Speed Trains (2023) – Cited by: 2
🎥 Camera Domain Adaptation Based on Cross-Patch Transformers for Person Re-Identification (2022) – Cited by: 7

 

Chao-Ming Wang | Computer Vision | Best Researcher Award

Published on by Academic Awards

Prof Dr. Chao-Ming Wang | Computer Vision | Best Researcher Award

Professor, National Yunlin University of Science and Technology, Taiwan

Chao-Ming Wang is a distinguished Professor at the Department of Digital Media Design at National Yunlin University of Science and Technology (YunTech) in Yunlin County, Taiwan. With a rich background in computer science and engineering, Dr. Wang has been a pivotal figure in advancing the fields of signal processing, computer vision, tech art, and interactive multimedia design. His career spans several prestigious institutions, reflecting his commitment to both research and education. 🌟

Publication Profile

Strengths for the Award:

  1. Extensive Experience and Expertise: Dr. Chao-Ming Wang has a distinguished academic and professional background in computer science and information engineering, with degrees from National Chiao Tung University and a career spanning over four decades. His long-term commitment and extensive experience in his field are significant assets.
  2. Leadership and Contributions: His roles as the Head of the Department of Digital Media Design and Director of the Design-led Innovation Center at National Yunlin University of Science and Technology highlight his leadership and ability to influence academic and research directions. His presidency at the Taiwan Society of Basic Design and Art further showcases his impact on the broader research community.
  3. Research Focus: Dr. Wang’s research interests in signal processing, computer vision, tech art, and interactive multimedia design align with cutting-edge technologies and applications. His work in healthcare design applications is particularly relevant, given the increasing focus on integrating technology with healthcare.
  4. Professional Recognition: His long tenure as a senior specialist and faculty member at reputable institutions demonstrates his respected standing in the academic community. His ongoing involvement in significant research areas suggests a sustained impact and relevance in his field.

Areas for Improvement:

  1. Recent Research Output: While Dr. Wang has a notable background, recent updates on his research output or significant publications could provide a clearer picture of his current contributions. Ensuring visibility through recent high-impact publications or citations might enhance his candidacy.
  2. Broader Research Impact: Expanding the scope of his research to include more interdisciplinary collaborations or applications in emerging fields could strengthen his position. Highlighting any groundbreaking projects or innovations developed under his leadership would be beneficial.
  3. Visibility and Outreach: Increasing his presence in international conferences, journals, and collaborative research projects could amplify his contributions. Engaging more actively with global research communities and platforms may enhance his visibility.

Conclusion:

Dr. Chao-Ming Wang is a strong candidate for the Research for Best Researcher Award due to his extensive experience, leadership roles, and relevant research interests in computer vision, tech art, and interactive multimedia design. His contributions to the field, particularly in healthcare design, underscore his impact. Addressing areas for improvement, such as recent research output and broader visibility, could further bolster his candidacy. Overall, his distinguished career and ongoing research make him a noteworthy contender for this award.

 

Education

Dr. Wang earned his B.Sc. (1980), M.Sc. (1982), and Ph.D. (1993) degrees in Computer Science and Information Engineering from National Chiao Tung University, Hsinchu, Taiwan. His academic journey laid a solid foundation for his extensive contributions to the field. 🎓

Experience

From 1982 to 2003, Dr. Wang served as a senior specialist at the National Chung Shan Institute of Science and Technology. He then joined Yuan Ze University as a faculty member from 2003 to 2008. In 2008, he moved to YunTech, where he held leadership roles, including Head of the Department of Digital Media Design (2010-2013) and Director of the Design-led Innovation Center (2016-2017). He also served as President of the Taiwan Society of Basic Design and Art from 2010 to 2013. 🏛️

Research Focus

Dr. Wang’s research interests are diverse and include signal processing, computer vision, tech art, and interactive multimedia design. His work aims to integrate technological advancements with creative applications, particularly in healthcare design. 🔬💻

Awards

Dr. Wang’s contributions to the field have been recognized with various awards and honors throughout his career. His innovative research and leadership in academia have established him as a leading figure in his areas of expertise. 🏆

Publications