Pengfei Wei | Computer Science | Best Researcher Award

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Dr. Pengfei Wei | Computer Science | Best Researcher Award 

Senior Engineer at Guangdong University of Technology | China

Dr. Pengfei Wei is a Senior Engineer at Guangdong University of Technology, recognized for his pioneering contributions to the field of computer science, particularly in multimodal learning, knowledge tracing, edge artificial intelligence, and task-oriented dialogue systems. He holds a Ph.D. in Computer Science, where his research focused on integrating deep learning models with practical applications in intelligent education and human–machine interaction. Combining academic rigor with industrial innovation, he brings substantial experience from both enterprise research and academic development, bridging the gap between theory and real-world technology deployment. His work encompasses advanced methods such as visual-enhanced transformers for multimodal named entity recognition, genetic-inspired relation extraction, and the introduction of Kolmogorov–Arnold representations in knowledge tracing, which have improved model interpretability and performance in AI-based learning systems. In addition to his theoretical advancements, he has successfully led projects on real-time lab-safety analytics and large-scale AI deployment using Huawei Ascend, Nvidia, and TPU platforms, contributing to the broader industrial adoption of edge AI technologies. Dr. Pengfei Wei has authored numerous peer-reviewed papers in top-tier international journals and conferences, including Neural Networks, ICMR, and IJCAI, and serves as a reviewer for several prestigious publications such as Neural Networks, Pattern Recognition Letters, AAAI, and IJCNN. His collaborative initiatives with research teams and institutions have fostered multidisciplinary innovation, emphasizing the integration of AI with blockchain, big data, and education systems. A dedicated mentor and research leader, he actively supports student-led research and fosters the development of next-generation AI scholars. His professional memberships with the China Computer Federation (CCF) and the Association for Computing Machinery (ACM) reflect his strong engagement in the global computing community. Dr. Pengfei Wei’s research continues to push the boundaries of multimodal understanding and intelligent systems, driving transformative progress in computational learning and applied artificial intelligence. Through his sustained contributions, he remains committed to advancing the capabilities of intelligent technologies that enhance human productivity, knowledge discovery, and digital transformation.

Profile: Orcid | Google Scholar

Featured Publications:

  • Liao, W., B. Zeng, Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533.

  • Liao, W., Zeng, B., Liu, J., Wei, P., Cheng, X., & Zhang, W. (2021). Multi-level graph neural network for text sentiment analysis. Computers & Electrical Engineering, 92, 107096.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2022). Image-text interaction graph neural network for image-text sentiment analysis. Applied Intelligence, 52(10), 11184–11198.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Cheng, X. (2022). Taxi demand forecasting based on the temporal multimodal information fusion graph neural network. Applied Intelligence, 52(10), 12077–12090.

  • Wei, P., Zeng, B., & Liao, W. (2022). Joint intent detection and slot filling with wheel-graph attention networks. Journal of Intelligent & Fuzzy Systems, 42(3), 2409–2420.

  • Wei, P., Ouyang, H., Hu, Q., Zeng, B., Feng, G., & Wen, Q. (2024). VEC-MNER: Hybrid transformer with visual-enhanced cross-modal multi-level interaction for multimodal NER. Proceedings of the International Conference on Multimedia Retrieval (ICMR 2024).

  • Wen, S., Zeng, B., Liao, W., Wei, P., & Pan, Z. (2021). Research and design of credit risk assessment system based on big data and machine learning. Proceedings of the IEEE 6th International Conference on Big Data Analytics (ICBDA 2021), 9–13.

Abdullah Al Mamun | Machine Learning | Young Scientist Award

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Mr. Abdullah Al Mamun | Machine Learning | Young Scientist Award

Lecturer at Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh.

Abdullah Al Mamun is an emerging researcher and academic professional 🌟 specializing in cutting-edge fields like IoT and Sustainability, Machine Learning, Computer Vision, and Explainable Artificial Intelligence 🤖🌿. Currently serving as a Lecturer at the Model Institute of Science and Technology in Gazipur, he is also pursuing his Master of Science in Computer Science and Engineering at Dhaka University of Engineering & Technology (DUET) 🎓. He has authored multiple peer-reviewed journal and conference papers 📚, many of which are published in IEEE and MDPI journals. Abdullah has been actively involved in several national and international research projects and has collaborated with scholars globally 🌐. His drive to explore solutions for environmental monitoring, medical diagnostics, and smart systems using intelligent technology sets him apart 🚀. Outside of academia, Abdullah engages in social volunteering, tech events, and academic clubs, continuously contributing to the student and research community 💡👥.

Professional Profile:

Google Scholar

Suitability for Young Scientist Award – Mr. Abdullah Al Mamun

Abdullah Al Mamun is an exceptionally promising early-career researcher and educator whose work spans IoT, Sustainability, Machine Learning, Computer Vision, and Explainable AI. His multidisciplinary contributions, especially in the areas of environmental monitoring, healthcare systems, and smart technologies, exhibit both innovation and societal relevance—key elements sought in a Young Scientist Awardee. His academic journey, technical expertise, international collaborations, and impactful project involvement establish him as a capable and committed scientist at the frontier of modern computing and intelligent systems.

📘 Education

Abdullah Al Mamun earned his Bachelor of Science in Computer Science and Engineering from Dhaka University of Engineering & Technology (DUET), Gazipur 🎓💻. Currently, he is pursuing his Master of Science in Engineering in the same department at DUET (2024–Present) 🎓🧠. His academic focus is rooted in data-driven research, intelligent systems, and digital sustainability 🌱📊. With a CGPA of 3.64 in the final 21.25 credits, Abdullah shows consistent improvement and dedication to advanced technical learning 📈🧑‍💻.

🧑‍💼 Professional Development 

Abdullah Al Mamun has accumulated diverse professional experiences in both academia and the tech industry 🧑‍🏫💼. Currently, he is working as a Lecturer in the Department of CSE at the Model Institute of Science and Technology, Gazipur 🎓. He has served as a Research Assistant in South Korea’s Woosong University under the Multimedia Signal & Image Processing Group 🌐🖼️. In addition, he worked as a Tutor for over 3 years, teaching programming, data structures, and system analysis 📚👨‍🏫. He also completed internships in web development and CMS-based platforms, gaining practical expertise in frontend and backend tools like HTML, CSS, JavaScript, PHP, and WordPress 💻🔧. He has contributed to government-funded projects like LICT and EDGE, further solidifying his experience in IT and system development for public infrastructure 🏛️🇧🇩.

🧪 Research Focus 

Abdullah’s research focus lies primarily at the intersection of IoT and environmental sustainability 🌍, Machine Learning and Artificial Intelligence 🤖, and Computer Vision and Explainable AI 👁️🔍. His projects include smart solar monitoring, child safety systems, and efficient deep learning models for medical applications like skin cancer detection 🏥⚡. He aims to address real-world challenges through scalable, intelligent technologies that enhance both safety and efficiency in smart cities and healthcare systems 🏙️🚑. His recent work under review explores mental health classification in Thalassemia patients, digital land monitoring, and cyber intrusion detection—illustrating a commitment to data ethics and sustainable innovation 🔐📊. With a mix of theoretical foundations and practical system implementations, Abdullah’s research contributes significantly to modern computational solutions in healthtech, sustainability, and cybersecurity 🌐💡.

🛠️ Research Skills

Abdullah possesses a diverse and robust research skill set 🎯. His core technical skills include Python programming 🐍, machine learning models 🤖, deep learning frameworks like YOLOv8 🎯, and simulation tools such as Origin, Matplotlib, and Seaborn 📊. He is proficient in both supervised and unsupervised learning, especially in outlier detection, parameter optimization, and data visualization 🧠🖼️. His hands-on work with Arduino, image processing, and web-based monitoring systems demonstrates strong integration of hardware-software synergy 🔧💻. He is also adept in Explainable AI, which enhances transparency in decision-making algorithms 🔍🧾. Abdullah’s ability to manage end-to-end pipelines from data collection to model deployment, along with experience in collaborative and interdisciplinary projects, sets a strong foundation for innovative research 🌐🔬. His publications and ongoing research underline his capabilities in academic writing, critical thinking, and experimental design 📚🧪.

🏅 Awards and Honors

Abdullah has earned recognition for his academic and technical excellence 🏆🎖️. He won the Second Runner-Up prize at BEYOND THE METRICS-2023, hosted by the Department of Business and Technology Management, IUT 🌍📈. He was also the Runner-Up in the Intra DUET Programming Contest (IDPC) 2022 organized by DUET’s CSE Department 🧑‍💻🥈. Additionally, he has participated and been selected in prestigious competitions such as the NASA Space App Challenge 2024 🚀, DUET TECH FEST, and ROBO MANIA 🤖. These accolades reflect his commitment to innovation, teamwork, and competitive programming skills 🌟💡.

Publication Top Notes

1. Software Defects Identification: Results using Machine Learning and Explainable Artificial Intelligence Techniques

  • Authors: M. Begum, M.H. Shuvo, I. Ashraf, A. Al Mamun, J. Uddin, M.A. Samad

  • Published in: IEEE Access, Volume 11, Pages 132750-132765

  • Year: 2023

  • Citations: 13

  • Summary:
    This paper investigates how machine learning (ML) and explainable artificial intelligence (XAI) methods can enhance the identification of software defects. The study uses multiple ML models (such as Random Forest, SVM, and XGBoost) and applies explainability techniques (e.g., SHAP, LIME) to interpret model decisions. The results show improved defect prediction accuracy and transparency, contributing to software reliability and maintainability.

2. Developed an IoT-Based Smart Solar Energy Monitoring System for Environmental Sustainability

  • Authors: A. Al Mamun, M.H. Shuvo, T. Islam, D. Islam, M.J. Islam, F.A. Tanvir

  • Published in: 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

  • Year: 2024

  • Citations: 4

  • Summary:
    This paper presents an Internet of Things (IoT)-enabled smart solar energy monitoring system. The system tracks and analyzes real-time data such as voltage, current, and energy output to promote environmental sustainability and efficient energy usage. Cloud-based dashboards and mobile alerts enhance usability. The innovation supports green energy adoption, especially in remote or resource-limited areas.

3. Developing an IoT-Based Child Safety and Monitoring System: An Efficient Approach

  • Authors: K.I. Masud, M.H. Shuvo, A. Al Mamun, J. Mallick, M.R. Jannat, M.O. Rahman

  • Published in: 2023 26th International Conference on Computer and Information Technology (ICCIT)

  • Year: 2023

  • Citations: 4

  • Summary:
    This paper proposes an IoT-driven child safety and monitoring system that integrates GPS tracking, wearable sensors, and mobile app notifications. Designed to prevent child abduction and accidents, the system provides real-time location updates and safety alerts to parents or guardians. The study highlights its effectiveness, low cost, and adaptability in both urban and rural settings.

4. Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8

  • Authors: M. Begum, A.K.I. Riad, A.A. Mamun, T. Hossen, S. Uddin, M.N. Absur, …

  • Published in: Future Internet, Volume 17, Issue 6, Article 254

  • Year: 2025

  • Summary:
    This study introduces an IoT-based system that leverages the YOLOv8 deep learning model to detect road anomalies such as potholes and bumps. The system uses real-time video analytics and onboard sensors to inform vehicle control systems, improving passenger comfort and road safety. The approach demonstrates high accuracy and responsiveness in urban mobility applications.

🏁 Conclusion

Abdullah Al Mamun is highly suitable for the Young Scientist Award. His commitment to solving critical real-world problems through interdisciplinary research, coupled with his consistent academic performance, global exposure, and technical leadership, make him an outstanding candidate. His trajectory clearly reflects the potential to become a thought leader in the fields of AI for sustainability and healthcare, justifying recognition through this prestigious award.

Hongchen Wu | Computer Science | Best Researcher Award

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Prof. Hongchen Wu | Computer Science | Best Researcher Award

Associate Professor at Shandong Normal University, China.

Dr. Hongchen Wu 🌏💻 is an associate professor in the School of Information Science & Engineering at Shandong Normal University. After earning his Ph.D. in Computer Science & Technology in 2016—supplemented by a two‑year joint Ph.D. stay at the University of California, Irvine—he has built a vibrant career at the crossroads of next‑generation Internet, information security, and AI‑driven data science. Wu leads multiple national and provincial projects on cross‑domain recommendation, privacy management, and online‑payment fraud, publishing widely in Neurocomputing, Information Processing & Management, IEEE Access, and other high‑impact venues. A committee member of the China Computer Federation (CCF) and active reviewer for top IEEE Transactions titles, he blends rigorous theory with real‑world impact—pushing the envelope on fake‑news detection, multimodal content analysis, and privacy‑aware personalization. Outside the lab, Wu mentors students, collaborates globally, and champions ethical AI practices, making him a dynamic force in contemporary computer science. 🚀📈

Professional  Profile:

Scopus

Google Scholar

Suitability For Best Researcher Award – Prof. Hongchen Wu

Dr. Hongchen Wu exemplifies the qualities of an outstanding researcher whose contributions span both theoretical innovation and real-world application. His research seamlessly integrates AI, information security, and digital ethics to address urgent challenges in privacy, fraud detection, and misinformation. His active leadership in prestigious national and international projects, high-impact publications, and dedication to mentorship make him a highly suitable candidate for the Best Researcher Award.

🎓 Education:

Wu completed his B.Eng. and M.Eng. at Shandong University 🏫 before obtaining his Ph.D. in Computer Science & Technology there in 2016. Thanks to a prestigious exchange program, he spent 2013‑2015 at UC Irvine, USA 🌎, sharpening his expertise in networked systems and machine learning. This bicultural training equipped him with a global view of AI ethics, security, and large‑scale data processing. 🧑‍🎓🔗

🚀 Professional Development :

Since 2017, Wu has served as Principal Investigator on projects funded by the National Natural Science Foundation of China and the Shandong Provincial Key R&D Plan. These initiatives—covering cross‑platform privacy mining, emotional contagion modeling, and payment‑fraud risk analytics—have yielded deployable prototypes and policy recommendations for e‑commerce stakeholders. Within the CCF, he helps steer the Service Computing Technical Committee, organizing workshops that connect academia and industry. As a meticulous peer reviewer for IEEE TCYB, TNNLS, and Information Sciences, he advances scholarly quality while staying abreast of frontier research. Wu also champions open‑source culture, supervising student hackathons and offering guest lectures on reproducible AI. Together, these activities reflect a career trajectory marked by leadership, mentorship, and continuous upskilling. 🛠️📚✨

🔍 Research Focus:

 Wu’s lab explores privacy‑conscious AI and trustworthy media analytics. Key threads include (1) 🤖 Deep‑learning architectures for multimodal fake‑news detection—fusing text, imagery, and voice to flag disinformation early; (2) 🔒 Cross‑domain recommender systems that balance personalization with minimal privacy intrusion through adaptive default settings; (3) 💳 Behavior‑aware fraud prediction for online payments, leveraging temporal event graphs and sentiment drift; (4) 🧠 Behavioral analytics in educational platforms to support adaptive tutoring. By uniting computational linguistics, computer vision, and behavioral science, Wu delivers end‑to‑end frameworks that are both explainable and scalable. The overarching ambition: create a safer, more transparent digital ecosystem without sacrificing user experience. 🌐⚖️

🏆 Awards & Honors:

 Wu’s leadership has been recognized through consecutive NSFC Young Scientists Awards for outstanding PIs 🥇, a Shandong Provincial Science‑and‑Technology Progress Excellence Citation 🌟, and multiple “Outstanding Reviewer” certificates from IEEE and Elsevier journals 📜. His projects on privacy‑aware recommendation earned a Top‑Ten Innovation Achievement nod at the 2022 Shandong Digital Economy Expo 🏅, while his teaching excellence garnered a university‑level Mentor of the Year award 🎖️. Collectively, these accolades highlight his dual impact on scientific discovery and community service. 👏

Publication Top Notes

1. Multimodal Fake News Detection via Progressive Fusion Networks

  • Authors: J. Jing, H. Wu, J. Sun, X. Fang, H. Zhang

  • Journal: Information Processing & Management

  • Volume/Issue: 60 (1)

  • Article Number: 103120

  • Year: 2023

  • Citations: 155

  • Summary: This paper presents a progressive fusion network approach to detect fake news by integrating multimodal data sources (e.g., text, images). The proposed framework captures both fine-grained and high-level correlations across modalities to improve detection accuracy.

2. Matrix Factorization for Personalized Recommendation with Implicit Feedback and Temporal Information in Social E-Commerce Networks

  • Authors: M. Li, H. Wu, H. Zhang

  • Journal: IEEE Access

  • Volume: 7

  • Pages: 141268–141276

  • Year: 2019

  • Citations: 31

  • Summary: This work enhances traditional matrix factorization techniques for recommendation systems by integrating users’ implicit feedback and temporal behaviors within social e-commerce platforms.

3. NSEP: Early Fake News Detection via News Semantic Environment Perception

  • Authors: X. Fang, H. Wu, J. Jing, Y. Meng, B. Yu, H. Yu, H. Zhang

  • Journal: Information Processing & Management

  • Volume/Issue: 61 (2)

  • Article Number: 103594

  • Year: 2024

  • Citations: 27

  • Summary: The paper introduces NSEP, a model designed for early fake news detection by perceiving the semantic environment surrounding the news content. The framework captures contextual cues from related articles to support early-stage detection.

4. Div-Clustering: Exploring Active Users for Social Collaborative Recommendation

  • Authors: H. Wu, X. Wang, Z. Peng, Q. Li

  • Journal: Journal of Network and Computer Applications

  • Volume/Issue: 36 (6)

  • Pages: 1642–1650

  • Year: 2013

  • Citations: 20

  • Summary: This study proposes Div-Clustering, a method that leverages active users’ social influence and clustering behavior to enhance collaborative filtering in recommendation systems.

5. Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services

  • Authors: H. Wu, M. Li, H. Zhang

  • Journal: IEEE Access

  • Volume: 7

  • Pages: 50031–50040

  • Year: 2019

  • Citations: 17

  • Summary: The paper presents a smart anonymity scheme to enhance security and privacy in location-based services, allowing secure collaboration among users without revealing sensitive information.

✅ Conclusion

Dr. Hongchen Wu is an exemplar of 21st-century research excellence. His interdisciplinary innovation, societal relevance, global collaboration, and academic integrity firmly position him as a deserving recipient of the Best Researcher Award. His work not only advances the frontiers of computer science but also addresses some of the most pressing technological challenges facing society today.

Weichen Zhang | Deep Learning | Best Researcher Award

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Dr. Weichen Zhang | Deep Learning | Best Researcher Award

Postdoc Researcher at University of Sydney, Australia.

Dr. Weichen Zhang 🎓 is a passionate AI Researcher and Engineer specializing in 2D/3D visual applications, currently working as a Postdoctoral Researcher at the University of Sydney 🇦🇺. With a strong academic background and over 8 years of hands-on experience, he has contributed significantly to deep learning, point cloud analysis, and 3D human body reconstruction 🤖. Formerly the R&D Team Lead at Bodymapp Pty Ltd, he led innovations in AI, SLAM, and AWS MLOps pipelines ☁️. His work is well-recognized with publications in top-tier venues like CVPR, T-PAMI, and T-IP, and two U.S. patents for AI technologies 🏆.

Professional Profile:

ORCID

Google Scholar

Suitability for Best Researcher Award – Dr. Weichen Zhang

Dr. Weichen Zhang is an outstanding candidate for the Best Researcher Award due to his groundbreaking contributions to the fields of AI, 2D/3D computer vision, and deep learning. With over 8 years of rich academic and industrial experience, he has demonstrated a rare combination of technical depth, publication excellence, and innovation in applied AI. His research has made substantial impact in areas such as point cloud analysis, 3D human body reconstruction, SLAM, and domain adaptation, positioning him as a rising star in AI and computer vision.

📘 Education

  • 🎓 Ph.D. in Deep Transfer Learning, University of Sydney, Australia (2017–2021)

  • 🎓 B.I.T. (Hons Class I), University of Sydney, Australia (2013–2016)

  • 🎓 High School, Affiliated to Nanjing Normal University, China (2009–2012)

💼 Experience

  • 🧠 Postdoctoral Research Associate, University of Sydney (Sep 2024–Present)

  • 🔬 R&D Team Lead & Research Engineer, Bodymapp Pty Ltd (May 2021–Aug 2024)

  • 👨‍🏫 Academic Tutor, University of Sydney (Jul 2019–Dec 2019)

🛠️ Professional Development 

Dr. Zhang continually evolves his professional skillset through advanced research collaborations 🤝, cross-disciplinary project leadership, and active participation in academic publishing 📚. His professional growth is marked by practical application of machine learning, neural networks, DevOps, and 3D vision technologies across academia and industry. With a strong command of programming languages like Python, C++, and MATLAB, he has built scalable AI systems and AWS MLOps pipelines ☁️. He also contributes to academic instruction, tutoring deep learning and transfer learning concepts. Zhang’s engagement with top research institutions and conferences keeps him at the cutting edge of AI innovation 🚀.

🧪 Research Focus Area

Dr. Weichen Zhang’s research is focused on 2D/3D visual understanding, including point cloud analysis, 3D mesh reconstruction, SLAM, and deep learning model generalization 🔍. His projects span cross-domain recognition, domain adaptation, and privacy-preserving avatar reconstruction technologies 👤. He integrates supervised and unsupervised learning methods to solve complex problems in autonomous driving 🚗, human body tracking, and forestry analytics 🌳. Zhang’s contributions are reflected in CORE A* journals and conferences, showing his commitment to pushing the boundaries of scalable, multimodal, and multi-sensor visual systems in real-world applications 📊.

🏅 Awards and Honours

  • 🥇 Research Training Program (RTP) Scholarship, University of Sydney (2017–2021)

  • 🎖️ Norman I. Price Scholarship, University of Sydney (2018–2019)

  • 🌟 CVPR 2018 Paper Spotlight, Salt Lake City, USA (2018)

  • 🏆 USYD-CSIRO Summer Research Scholarship, University of Sydney (2016–2017)

Publication Top Notes

📘 1. Collaborative and Adversarial Network for Unsupervised Domain Adaptation

  • Authors: W. Zhang, W. Ouyang, W. Li, D. Xu

  • Published in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • Year: 2018

  • Cited by: 628

  • Summary: This paper presents a novel framework combining collaborative learning and adversarial training to improve performance in unsupervised domain adaptation. It effectively aligns feature distributions between source and target domains.

📘 2. Model Compression using Progressive Channel Pruning

  • Authors: J. Guo, W. Zhang, W. Ouyang, D. Xu

  • Published in: IEEE Transactions on Circuits and Systems for Video Technology

  • Year: 2020

  • Cited by: 74

  • Summary: The authors propose a progressive pruning method that reduces the number of channels in convolutional neural networks, enabling model compression with minimal accuracy loss. The method is data-driven and layer-wise.

📘 3. SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection

  • Authors: W. Zhang, W. Li, D. Xu

  • Published in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • Year: 2021

  • Cited by: 54

  • Summary: SRDAN is a domain adaptation network designed for 3D object detection. It introduces scale-aware and range-aware components to address distribution shifts between datasets, improving detection across varying sensor configurations.

📘 4. Progressive Modality Cooperation for Multi-modality Domain Adaptation

  • Authors: W. Zhang, D. Xu, J. Zhang, W. Ouyang

  • Published in: IEEE Transactions on Image Processing, Vol. 30, pp. 3293–3306

  • Year: 2021

  • Cited by: 24

  • Summary: This work proposes a progressive learning scheme where different modalities (e.g., RGB and depth) are learned in stages to enhance domain adaptation. The model progressively integrates information, improving generalization.

📘 5. 3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space

  • Authors: J. Gu, Z. Wang, W. Ouyang, W. Zhang, J. Li, L. Zhuo

  • Published in: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 391–400

  • Year: 2020

  • Cited by: 24

  • Summary: This paper introduces a cross-modal learning framework that disentangles modality-specific and shared features, enabling accurate 3D hand pose estimation from multi-view data.

Conclusion

Dr. Zhang’s sustained contributions to cutting-edge AI research, proven innovation, and recognized leadership in both academia and industry make him an ideal recipient of the Best Researcher Award. His visionary work continues to push the boundaries of what is possible in machine learning and computer vision, with real-world impact and academic distinction.