Dr. Xiaojuan Pang | Technologies | Best Researcher Award

Dr. Xiaojuan Pang | Technologies | Best Researcher Award

lecturer, China University of Mining and Technology, China

Dr. Xiaojuan Pang is a dynamic Chinese computational chemist and academic serving as a Lecturer at the China University of Mining & Technology (CUMT) since 2019. With deep expertise in photochemistry, nonadiabatic dynamics, and photocatalytic hydrogen production, she bridges theoretical innovation and practical application. Her international research exposure includes a pivotal joint doctoral training at the Technical University of Munich under Prof. Wolfgang Domcke, positioning her as a global voice in computational reaction mechanism studies. 🌍

Publication Profile

ORCID

🎓 Education Background

Dr. Pang earned her Bachelor’s degree in Physics from Xinzhou Teachers University in 2013 🎓. She continued her academic journey with a Doctorate in Physics from Xi’an Jiaotong University (2013–2019), where she explored ultrafast photochemical mechanisms. Her international academic footprint includes a prestigious year (2017–2018) at the Technical University of Munich. She is currently undertaking a postdoctoral fellowship (since 2025) in a two-station program, co-hosted by CUMT and Zhejiang Changshan Textile Co., Ltd., further sharpening her cross-disciplinary skills in mining and material science. 📘🧪

👩‍🏫 Professional Experience

Dr. Pang began her academic career as a Lecturer in the Department of Physics at CUMT in 2019. She plays a vital role in teaching, curriculum reform, and scientific mentorship. Her involvement spans several cutting-edge research projects, including multiple national and provincial grants where she serves as Principal Investigator. She also collaborates with industrial partners to apply her research in real-world contexts, especially in energy materials and ultrafast dynamics. 🏫🧑‍🔬

🏅 Awards and Honors

Dr. Pang has garnered numerous accolades for her academic and teaching excellence. Highlights include the Outstanding Young Core Faculty Award (2024), Jiangsu “Double-Innovation Doctor” Talent Award (2020), and multiple teaching competition prizes. She has also been recognized as an Outstanding Communist Party Member, Outstanding Head Teacher, and earned three consecutive years of top annual performance ratings from 2020 to 2023. 🏆🎖️

🔍 Research Focus

Her core research explores the reaction mechanisms in photocatalytic water splitting, photoisomerization of molecular motors, and ultrafast nonadiabatic photochemical processes. Dr. Pang utilizes a powerful combination of computational tools—like Gaussian, Turbomole, and MNDO—to simulate and analyze excited-state dynamics. Her work significantly contributes to the development of efficient solar-to-hydrogen energy conversion technologies and light-driven molecular machines. 💡⚛️

🧩 Conclusion

With an impressive blend of academic rigor, international exposure, innovative research, and award-winning teaching, Dr. Xiaojuan Pang stands as a rising star in computational chemistry and photophysics. Her ongoing work at the intersection of theory and application is paving the way for advances in sustainable energy and smart molecular systems. 🚀

📚 Top Publications

Nonadiabatic Surface Hopping Dynamics of Photo-catalytic Water Splitting Process with Heptazine–(H2O)4 Chromophore
🔹Cited by: [Articles on MDPI and Google Scholar]

Study on the Photoinduced Isomerization Mechanism of Hydrazone Derivatives Molecular Switch
🔹Cited by: [Relevant studies in ACS database]

Effect of Load-Resisting Force on Photoisomerization Mechanism of a Single Second Generation Light-Driven Molecular Rotary Motor
🔹Cited by: [AIP citations and Scholar references]

Ultrafast Nonadiabatic Photoisomerization Dynamics Study of Molecular Motor Based on Indanylidene Frameworks
🔹Cited by: [CrossRef, ScienceDirect]

Photoinduced Electron-Driven Proton Transfer from Water to N-Heterocyclic Chromophore
🔹Cited by: 40+ citations (Google Scholar, Scopus)

Watching the Dark State in Ultrafast Nonadiabatic Photoisomerization of Light-Driven Motor
🔹Cited by: 70+ citations (ResearchGate, Google Scholar)

QIANG QU | Artificial Intelligence Award | Best Researcher Award

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

🎓 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 Skłodowska-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.

💼 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.

🏅 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.

🔬 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.

🔚 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. 🚀

📚 Publications

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

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

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

On Time-Aware Cross-Blockchain Data MigrationTsinghua Science and Technology (2024). Cited in Tsinghua University 📖

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

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

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment ClassificationIEEE Transactions on Affective Computing (2021). Cited in IEEE 📖

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