Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

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Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

Student, Guangxi University, China

Pingjie Ou is a passionate master’s student at Guangxi University, China, specializing in edge computing, cloud computing, and machine learning. With a strong academic foundation and growing research portfolio, he is actively contributing to next-generation computing paradigms. His early contributions in deep reinforcement learning applications for vehicular networks have already gained traction within the academic community. πŸ§ πŸ’‘

Professional Profile

Scopus

πŸŽ“ Education Background

Pingjie Ou is currently pursuing his master’s degree at Guangxi University, one of the prominent institutions in China. His academic focus lies in electrical and computer engineering, with emphasis on distributed computing and artificial intelligence. πŸ“˜πŸ«

πŸ’Ό Professional Experience

Although a student, Pingjie Ou has engaged in substantial research activities under funded projects including The National Natural Science Foundation of China (No. 62162003) and GuikeZY24212059 supported by the Guangxi Province. His active involvement in real-time research scenarios demonstrates promising professional potential. πŸ”¬πŸ“Š

πŸ… Awards and Honors

As an emerging scholar, Pingjie Ou has not yet accumulated major awards but has gained recognition through impactful publications and research citations. His growing citation record and h-index reflect the potential for future accolades. πŸ†πŸ“ˆ

πŸ” Research Focus

His core research interests include edge computing, cloud computing, vehicular networks, and machine learning. He is particularly focused on cooperative caching, resource management, and optimizing network efficiency using artificial intelligence approaches such as deep reinforcement learning. πŸš—β˜οΈπŸ“Ά

🧾 Conclusion

Pingjie Ou is a driven young researcher dedicated to advancing intelligent computing technologies. With strong academic grounding, collaborative research exposure, and early citation impact, he stands as a promising candidate for recognition in the domain of computer science and engineering. His scholarly journey is on a clear upward trajectory. πŸš€πŸ“š

πŸ“š Publication Top Note

  1. PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning
    πŸ“… Published Year: 2025
    πŸ“– Journal: Ad Hoc Networks
    πŸ”— 10.1016/j.adhoc.2025.103888

 

Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

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Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

Assist. Prof. Dr. Yuchae Jung, Open Cyber University of Korea , South Korea.

Dr. Yuchae Jung is an accomplished Affiliated Professor at KAIST School of Computing, Seoul, South Korea. With an interdisciplinary background spanning computer science, medical sciences, and artificial intelligence, she brings a unique integration of biomedical knowledge and computational innovation to her research. Over the years, Dr. Jung has held key academic and research roles in prestigious institutions, including Harvard Medical School and State University of New York. Her professional journey reflects a strong commitment to advancing digital healthcare, AI-driven diagnostics, and computational biology. πŸ§ πŸ’»πŸ§¬

Professional Profile

Google Scholar

πŸŽ“ Education Background

Dr. Jung earned her Ph.D. and M.S. in Medical Science from The Catholic University of Korea (2008, 2002), following her undergraduate degree in Computer Science from Sookmyung Women’s University in 2000. This solid academic foundation has enabled her to contribute innovatively to both computer science and medical informatics. πŸŽ“πŸ“š

πŸ§ͺ Professional Experience

Dr. Jung is currently affiliated with KAIST’s School of Computing as a professor. She has previously held significant roles at The Catholic University of Korea, Boin IT, Seoul National University, and Sookmyung Women’s University. She has also conducted postdoctoral research at Brigham & Women’s Hospital (Harvard Medical School) and State University of New York. Her professional engagements include lectures, research leadership, and AI-based system development across medical and computing fields. πŸ₯πŸ–₯οΈπŸ“Š

πŸ… Awards and Honors

Dr. Jung has been the Principal Investigator of several prestigious grants from organizations such as the Ministry of SMEs and Startups, National Library of Korea, Ministry of Science, and Ministry of Education. Her projects span from NLP-based clinical dialogue systems to cancer therapy algorithms and bioinformatics applications in glioblastoma research. She was also honored as a keynote speaker by The Korean Society of Pathologists. πŸ†πŸ“œπŸ‡°πŸ‡·

πŸ”¬ Research Focus

Her core research interests lie in Medical AI, including deep transfer learning for digital pathology image analysis, clinical Natural Language Processing (Bio-NLP), and cancer genomics (TFs, repeat sequences, miRNAs). She also explores gene expression network analysis in cancer and functional informatics for precision diagnostics. Her work bridges cutting-edge AI with real-world healthcare applications. πŸ§¬πŸ€–πŸ“ˆ

βœ… Conclusion

Dr. Yuchae Jung is a pioneering figure in interdisciplinary AI and bioinformatics, contributing impactful research to cancer genomics and healthcare AI. With a dynamic academic trajectory and a clear focus on translational science, she continues to be a driving force in computational medicine and smart health systems. Her extensive contributions position her as a deserving candidate for recognition in digital healthcare innovation. πŸŒπŸ’‘πŸ‘©β€βš•οΈ

πŸ“ Top Publications Highlights

  1. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning
    πŸ“… Published: 2021 in MDPI Sensors
    πŸ“Š Cited by: 39 articles (Google Scholar)
    πŸ” A groundbreaking study applying deep transfer learning for pathology image classification.

  2. Impact of tumor purity on immune gene expression and clustering analyses across multiple cancer types
    πŸ“… Published: 2018 in Cancer Immunology Research
    πŸ“Š Cited by: 107 articles
    πŸ”¬ Investigates how tumor purity affects gene expression in cancer immunology.

  3. Hybrid-Aware Model for Senior Wellness Service in Smart Home
    πŸ“… Published: 2017 in MDPI Sensors
    πŸ“Š Cited by: 25 articles
    🏑 Explores smart health monitoring using a hybrid AI model in smart homes.

  4. Aneuploidy meets network analysis: leveraging copy number alterations
    πŸ“… Published: 2017 in Translational Cancer Research
    πŸ“Š Cited by: 15 articles
    🧬 Integrates systems biology with cancer genomics.

  5. Cancer stem cell targeting: Are we there yet?
    πŸ“… Published: 2015 in Archives of Pharmacal Research
    πŸ“Š Cited by: 55 articles
    πŸ’‘ Reviews strategies to target elusive cancer stem cells.

  6. Systemic approaches identify Z-ajoene as a GBM stem cell-specific targeting agent
    πŸ“… Published: 2014 in Molecules and Cells
    πŸ“Š Cited by: 40+ articles
    πŸ§ͺ Identifies garlic-derived compound with anti-glioblastoma activity.

  7. Numb regulates glioma stem cell fate and growth
    πŸ“… Published: 2012 in Stem Cells
    πŸ“Š Cited by: 100+ articles
    πŸ“ˆ A critical study in stem cell regulation in glioma.

  8. GEAR: Genomic Enrichment Analysis of Regional DNA Copy Number Changes
    πŸ“… Published: 2008 in Bioinformatics
    πŸ“Š Cited by: 80+ articles
    🧬 Proposes a novel method for regional DNA copy number analysis.

  9. DNA methylation patterns of ulcer-healing genes in gastric cancers
    πŸ“… Published: 2010 in Journal of Korean Medical Science
    πŸ“Š Cited by: 35 articles
    πŸ”¬ Connects epigenetics with cancer pathology.

  10. PathCluster: a framework for gene set-based hierarchical clustering
    πŸ“… Published: 2008 in Bioinformatics
    πŸ“Š Cited by: 90+ articles
    πŸ“‚ Presents a tool widely adopted in gene expression analysis.

 

QIANG QU | Artificial Intelligence Award | Best Researcher Award

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Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

πŸŽ“ Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie SkΕ‚odowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

πŸ’Ό Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIAT’s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

πŸ… Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

πŸ”¬ Research Focus

Dr. Qu’s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

πŸ”š Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. πŸš€

πŸ“š Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphs – IEEE Big Data Mining and Analytics (2024). Cited in IEEE πŸ“–

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoT – IEEE Internet of Things Journal (2024). Cited in IEEE πŸ“–

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing – IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE πŸ“–

On Time-Aware Cross-Blockchain Data Migration– Tsinghua Science and Technology (2024). Cited in Tsinghua University πŸ“–

Few-Shot Relation Extraction With Automatically Generated Prompts – IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE πŸ“–

Opinion Leader Detection: A Methodological Review – Expert Systems with Applications (2019). Cited in Elsevier πŸ“–

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification– IEEE Transactions on Affective Computing (2021). Cited in IEEE πŸ“–

Efficient Online Summarization of Large-Scale Dynamic Networks – Β IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE πŸ“–