Dandan Zhu | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Dandan Zhu | Artificial Intelligence | Best Researcher Award

Deputy Director of Department at China University of Petroleum, Beijing, China

Professional Profile

Google Scholar

Summary

Dr. Dandan Zhu is an Associate Professor in the College of Artificial Intelligence at China University of Petroleum, Beijing (CUPB). With a solid background in precision engineering and aircraft design, she specializes in integrating artificial intelligence with petroleum engineering. Since joining CUPB in 2015, she has led numerous national and industry-supported projects, focusing on intelligent drilling, geological modeling, and data-driven automation in subsurface energy exploration. Her work bridges fundamental research and field-based application, contributing to advancements in digital oilfield technologies.

Educational Details:

Dr. Zhu holds a Ph.D. in Precision Engineering from the University of Tokyo, Japan, and a Master’s degree in Aircraft Design from Beihang University, China. Her interdisciplinary academic training provides a unique foundation for innovation at the intersection of engineering mechanics, AI systems, and petroleum technology.

Professional Experience:

Since 2015, Dr. Zhu has been serving as Associate Professor at CUPB’s College of Artificial Intelligence. She has directed over 40 research projects and 27 consultancy collaborations, often partnering with major energy enterprises such as CNPC, Sinopec, and CNOOC. Her initiatives span intelligent trajectory control systems, hydraulic fracturing optimization, and 3D geological simulations. She is also a contributing reviewer and participant in technical forums under IEEE, ACM, and SPE.

Research Interests:

Her core research areas include reinforcement learning, trajectory control algorithms, wellbore guidance systems, and generative simulation environments. Dr. Zhu has developed an integrated learning framework for intelligent drilling that combines offline training, real-time decision-making, and post-drilling optimization. This system improves adaptability in uncertain geological formations and supports automation in energy extraction operations.

Author Metrics:

Dr. Zhu has authored 39 peer-reviewed journal publications indexed in SCI and Scopus. Her work has received a total of 62 citations since 2020. She has published 1 academic book (ISBN: 978-7-3025-3524-9) and holds 5 patents in the domain of AI-enhanced petroleum engineering technologies. Her Google Scholar profile can be accessed here.

Awards and Honors:

Dr. Zhu’s contributions have earned recognition through national-level research grants and industry partnerships. She has been instrumental in building interdisciplinary teams and field-tested innovations that have enhanced both academic understanding and operational performance in petroleum systems. She is actively involved in professional networks such as IEEE, ACM, and SPE, contributing to peer reviews and international conferences.

Publication Top Notes

1. End-to-End Multiplayer Violence Detection Based on Deep 3D CNN

Conference: Proceedings of the 2018 VII International Conference on Network Technology (ICNT)
Publication Date: 2018
Contributors: C. Li, L. Zhu, D. Zhu, J. Chen, Z. Pan, X. Li, B. Wang
Citations (as of latest record): 19
Abstract Summary: This study introduces an end-to-end multiplayer violence detection system utilizing deep 3D convolutional neural networks (CNNs). By analyzing spatial-temporal features in video frames, the model effectively detects violent interactions in multiplayer scenarios, offering potential applications in surveillance and security systems.

2. Investigation on Automatic Recognition of Stratigraphic Lithology Based on Well Logging Data Using Ensemble Learning Algorithm

Conference: SPE Asia Pacific Oil and Gas Conference and Exhibition (Paper ID: D021S016R003)
Publication Date: 2018
Contributors: K. Gong, Z. Ye, D. Chen, D. Zhu, W. Wang
Citations: 12
Abstract Summary: This paper presents an ensemble learning-based methodology for automatically identifying stratigraphic lithology using well logging data. The integration of multiple machine learning models significantly improves lithology classification accuracy, providing intelligent support for geological interpretation in drilling operations.

3. A Reinforcement Learning Based 3D Guided Drilling Method: Beyond Ground Control

Conference: Proceedings of the 2018 VII International Conference on Network Technology (ICNT)
Publication Date: 2018
Contributors: H. Liu, D. Zhu, Y. Liu, A. Du, D. Chen, Z. Ye
Citations: 8
Abstract Summary: The study introduces a reinforcement learning-based 3D guided drilling method to improve directional control in subsurface operations. The framework leverages real-time learning and environmental feedback to optimize drilling paths in complex geological settings, advancing autonomy in petroleum engineering.

4. A Target-Aware Well Path Control Method Based on Transfer Reinforcement Learning

Journal: SPE Journal
Publication Date: 2024
Contributors: Dandan Zhu, Q. Xu, F. Wang, D. Chen, Z. Ye, H. Zhou, K. Zhang
Citations: 6
Abstract Summary: This paper proposes a novel well path control strategy using transfer reinforcement learning to enhance adaptability in trajectory optimization. The model incorporates prior knowledge from similar geological environments to accelerate convergence and ensure target-aware control in complex drilling tasks.

5. Deep Learning Approach of Drilling Decision for Subhorizontal Drain Geosteering Based on APC-LSTM Model

Journal: SPE Drilling & Completion, Volume 38, Issue 01, Pages 1–17
Publication Date: 2023
Contributors: D. Zhu, X. Dai, Y. Liu, F. Wang, X. Luo, D. Chen, Z. Ye
Citations: 6
Abstract Summary: The article introduces an APC-LSTM (Adaptive Prediction and Control with Long Short-Term Memory) deep learning model to enhance real-time decision-making in subhorizontal geosteering operations. This approach improves trajectory accuracy and decision response under uncertain formation conditions.

Conclusion

Assoc. Prof. Dr. Dandan Zhu is a strong and deserving candidate for the Best Researcher Award in Artificial Intelligence. Her innovative, interdisciplinary research, leadership in AI-driven drilling automation, and commitment to real-world applications make her stand out. With a growing publication record, industrial collaboration, and dedication to AI advancement in energy, she embodies the qualities of a forward-thinking and impactful researcher. Strategic international exposure and editorial roles can further enhance her already impressive research trajectory.

Li Qianmu | Technology | Best Researcher Award

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Prof. Dr. Li Qianmu | Technology | Best Researcher Award

Professor at Nanjing University of Science and Technology, China

Professional Profile

Orcid
Scopus

Summary

Professor Li Qianmu is an eminent academic and research leader at Nanjing University of Science and Technology, serving as Deputy Dean of its Research Institute and a PhD supervisor. Internationally recognized for his groundbreaking work in cybersecurity, Professor Li has held influential positions across academia, government, and industry, including as a foreign academician, Tencent Cloud Most Valuable Expert, and Deputy Director of the Expert Committee of the Talent Center under China’s Ministry of Industry and Information Technology. He has contributed significantly to trustworthy intelligent systems and data space technologies, and has published over 70 high-impact papers and authorized more than 100 patents.

Educational Details

Professor Li Qianmu holds a doctoral degree and has cultivated a career anchored in scientific excellence and innovation. As a doctoral supervisor, he plays a pivotal role in mentoring the next generation of researchers in cybersecurity and trustworthy systems.

Professional Experience

Professor Li currently serves as Professor and Deputy Dean at the School of Science and Technology, Nanjing University of Science and Technology. He is a member of the university’s Academic Committee and has also taken on leadership roles such as Vice President of the Jiangsu Computer Society, Vice President of the Jiangsu Cyberspace Security Society, and Team Leader of the General Group of the Jiangsu Digital Standardization Technical Committee. Nationally, he has been involved in shaping AI investment and standards as an Expert Member of the National Artificial Intelligence Industry Investment Fund Advisory Committee and Member of IEC SEG13.

Research Interests

His core research areas include cybersecurity in computing power networks, trustworthy intelligent systems, ontology-based security architectures, industrial internet security, and intelligent perception in large-scale computing networks. His research emphasizes multi-scenario threat modeling, autonomous defense systems, and cognitive countermeasure technologies for critical infrastructure.

Author Metrics 

Professor Li has published over 70 high-level scientific papers indexed in SCI and Scopus journals and conferences, including multiple top-tier international venues. He is the author of the book “Multi-Scenario Threat Endogenous Defense Architecture and Ontology Security Key Technologies” (ISBN: 978-1631815652). His work has garnered extensive citations, and his publications have been included in the 2023 Highly Cited Papers of Wiley and reprinted by NASA laboratories. He was also named the 2019 Challenge Problem Winner at AAAI and authored one of the Top 50 Best Papers at TRB’s centennial conference.

Awards and Honors

Professor Li has received 5 first prizes and 9 second prizes in provincial and ministerial science and engineering categories. Notable achievements include:

  • First Prize, Jiangsu Provincial Science and Technology Progress Award (2023)

  • Top 10 Scientific and Technological Advances in Communications, China Institute of Communications (2024)

  • Second Prize, Wu Wenjun Artificial Intelligence Science and Technology Award (2025)

  • First Prize, Science and Technology Award, China Command and Control Society (2024)

  • Second Prize, Outstanding Achievements Award in Social Sciences, Ministry of Education

  • Second Prize, Jiangsu Provincial Philosophy and Social Sciences Award
    His technologies have been recognized by the China Education and Research Network and adopted into Jiangsu’s standardization initiatives supporting high-quality economic development.

Publication Top Notes

1. A Knowledge Distillation Enhanced Semi-Supervised Federated Learning Framework for Intrusion Detection in EV Charging Networks

  • Journal: IEEE Internet of Things Journal

  • Publication Date: 2025

  • DOI: 10.1109/JIOT.2025.3577666

  • Contributors: Luanjuan Jiang, Qianmu Li, Xun Che, Xin Chen

  • Abstract Summary: This paper presents a semi-supervised federated learning framework enhanced with knowledge distillation for detecting intrusions in electric vehicle (EV) charging networks. The framework addresses data privacy concerns while achieving high detection accuracy with limited labeled data.

2. A Novel Multi-Agent Game-Theoretic Model for Cybersecurity Strategies in EV Charging Networks: Addressing Risk Propagation and Budget Constraints

  • Journal: Energy

  • Publication Date: September 2025

  • DOI: 10.1016/j.energy.2025.136847

  • Contributors: Luanjuan Jiang, Qianmu Li, Xin Chen

  • Abstract Summary: The study introduces a game-theoretic model involving multiple agents to optimize cybersecurity strategies in EV charging networks, accounting for the spread of cyber risks and financial limitations.

3. Research on Hidden Backdoor Prompt Attack Method

  • Journal: Symmetry

  • Publication Date: June 16, 2025

  • DOI: 10.3390/sym17060954

  • Contributors: Huanhuan Gu, Qianmu Li, Yufei Wang, Yu Jiang, Aniruddha Bhattacharjya, Haichao Yu, Qian Zhao

  • Abstract Summary: This article proposes a new prompt-based hidden backdoor attack technique targeting large language models and neural networks, exploring stealth strategies and their implications for AI security.

4. Understanding Convolutional Neural Networks From Excitations

  • Journal: IEEE Transactions on Neural Networks and Learning Systems

  • Publication Date: May 2025

  • DOI: 10.1109/TNNLS.2024.3430978

  • Contributors: Zijian Ying, Qianmu Li, Zhichao Lian, Jun Hou, Tong Lin, Tao Wang

  • Abstract Summary: This research provides a new interpretability framework for convolutional neural networks (CNNs) based on excitation analysis, enhancing understanding of model behavior and feature relevance.

5. BioElectra-BiLSTM-Dual Attention Classifier for Optimizing Multilabel Scientific Literature Classification

  • Journal: The Computer Journal

  • Publication Date: May 15, 2025

  • DOI: 10.1093/comjnl/bxae132

  • Contributors: Muhammad Inaam ul Haq, Qianmu Li, Khalid Mahmood, Ayesha Shafique, Rizwan Ullah

  • Abstract Summary: The paper introduces a novel BioElectra-BiLSTM-Dual Attention model to enhance the multilabel classification of scientific documents, addressing semantic dependencies and optimizing classification accuracy.

Conclusion

Prof. Dr. Li Qianmu stands out as a top-tier candidate for the Best Researcher Award in Technology. His sustained contributions to cutting-edge cybersecurity and AI research, policy advisory roles, and patent output reflect a rare blend of scholarly excellence and practical impact. He represents the ideal embodiment of innovation-driven research leadership.