Jian Zhao | Image processing | Best Researcher Award

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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

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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

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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

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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