Chengyan Liu | Advanced Computing | Best Researcher Award

Prof. Chengyan Liu | Advanced Computing | Best Researcher Award

Henan University | China

Professor Chengyan Liu is a distinguished scholar in Condensed Matter Physics and Computational Physics, currently serving as a Full Research Professor at the Institute of Future Technologies, Henan University. He is a Doctoral Supervisor and a recognized Yellow River Scholar. With academic roots from Fudan University and an international postdoctoral stint at UC Irvine, Prof. Liu has become a leading authority on defect physics, semiconductor interfaces, and photoelectronic materials. His prolific output includes over 20 high-impact publications, multiple national research grants, and a reputation for pushing the boundaries of theoretical materials science.

👨‍🎓Profile

Google scholar

Scopus

ORCID

🎓 Early Academic Pursuits

Prof. Liu’s academic journey began with a B.Sc. in Physics from Zhengzhou University in 2011, followed by an M.Sc. in Theoretical Physics at the same institution in 2014. He then pursued a Ph.D. at Fudan University, completing it in 2017 under a rigorous theoretical physics program. During this formative period, he laid a solid foundation in quantum theory, computational modeling, and condensed matter systems, which would become central to his future research. His early interest in semiconductor materials and grain boundary phenomena steered him toward the path of advanced computational materials physics.

🏛️ Professional Endeavors 

After earning his Ph.D., Prof. Liu expanded his expertise as a postdoctoral researcher at the University of California, Irvine, where he worked in the Department of Astrophysics. He returned to China to join Henan University, rapidly progressing from Lecturer (2020) to Distinguished Professor, and most recently, a Fast-Tracked Full Professor (2024) under Henan’s elite talent program. At Henan, he spearheads critical research in the Quantum Materials and Quantum Energy Lab, leads provincial and national-level projects, and serves as a doctoral mentor. His role bridges academic leadership, institutional innovation, and scientific advancement.

🔬 Contributions and Research Focus

Prof. Liu specializes in theoretical studies of defect physics, excited-state dynamics, and optoelectronic behavior in multicomponent semiconductors. His pioneering work on Cuâ‚‚ZnSn(SSe)â‚„ solar cells, defect passivation, and p-type transparent conductors has led to material innovations critical for next-generation solar energy devices. He is known for integrating first-principles calculations, nonadiabatic molecular dynamics, and interface engineering to resolve longstanding efficiency bottlenecks in photovoltaics. His research also touches on phonon imaging, bandgap tuning, and nanostructure thermodynamics, cementing his role as a cross-disciplinary leader in materials computation and energy physics.

🌏 Impact and Influence

Prof. Liu’s research has significantly impacted the fields of photovoltaics, defect engineering, and quantum materials. His work in kesterite solar cells has advanced understanding of Voc-deficits and interface stability, directly influencing experimental design across China and abroad. He has published in Nature, Advanced Energy Materials, and npj Computational Materials, garnering citations and collaborations globally. As a corresponding or first author on most of his publications, he shapes scholarly discourse and sets research directions. His mentorship and visibility in national projects further amplify his influence on China’s renewable energy research landscape.

📚 Academic Citations

Prof. Liu has authored or co-authored over 20 peer-reviewed publications in journals with impact factors exceeding 50 (Nature, AFM, Nano Letters, etc.). His works are widely cited in the fields of materials chemistry, physics, and energy science. His contributions to defect theory, interface passivation, and electronic structure analysis are frequently referenced by experimentalists and theorists alike. Notably, his 2021 Nature paper on single-defect phonons and his 2017 work in Advanced Energy Materials are seminal in their respective domains. His consistent authorship and citation metrics mark him as a globally recognized scholar in computational materials science.

đź§  Research Skills

Prof. Liu possesses deep expertise in first-principles modeling, density functional theory (DFT), nonadiabatic dynamics, and defect analysis. His ability to combine quantum simulations with applied material design allows him to bridge theory and experiment. He has demonstrated prowess in bandgap engineering, passivation chemistry, and interface defect control. His skillset includes advanced tools like VASP, Quantum ESPRESSO, and phonon analysis frameworks. He leads multi-disciplinary teams, mentors graduate researchers, and designs custom simulation frameworks to address complex materials problems placing him at the frontier of computational materials innovation.

🎓 Teaching Experience

Since 2020, Prof. Liu has taught Advanced Quantum Mechanics for graduate students, delivering 54 hours annually. He is renowned for blending rigorous theoretical depth with computational applications, making abstract quantum concepts tangible. His textbook contribution, Study Guide to Griffiths’ Quantum Mechanics, demonstrates his pedagogical commitment and ability to clarify complex physics. Students under his mentorship have contributed to publications, signaling his effectiveness in academic training and talent development. Prof. Liu emphasizes problem-solving, analytical thinking, and research integration, providing a strong foundation for emerging physicists and materials scientists under his guidance.

🏆 Awards and Honors

Prof. Liu was awarded the prestigious Yellow River Scholar title a top provincial honor recognizing distinguished academic performance. His selection as a Fast-Tracked Full Professor under Henan’s High-Level and Urgently Needed Talent Program attests to his scientific merit and leadership potential. He has received multiple NSFC research grants and is the recipient of the Henan Excellent Young Scientists Fund. His inclusion on the Board of the Henan Physical Society further highlights his stature in the academic community. These honors reflect not only his past accomplishments but also his promise for future breakthroughs.

🚀 Legacy and Future Contributions

Prof. Liu is poised to leave a lasting legacy in quantum materials research and solar energy innovation. His pioneering work on transparent conductors, defect-tolerant semiconductors, and carrier lifetime enhancement will continue to shape the next wave of clean energy technology. As a mentor, author, and national project leader, he is building a robust academic ecosystem in Henan Province and beyond. Looking ahead, he aims to expand international collaborations, transition more research toward real-world applications, and foster interdisciplinary integration. His legacy will likely include both scientific excellence and the nurturing of future scientific leaders.

Publications Top Notes

  • Title: Defect inducing large spin orbital coupling enhances magnetic recovery dynamics in CrI3 monolayer
    Authors: Yu Zhou, Ke Zhao, Zhenfa Zheng, Huiwen Xiang, Jin Zhao,* Chengyan Liu,*
    Journal: npj Computational Materials
    Year: 2025

  • Title: Interfacial passivation of kesterite solar cells for enhanced carrier lifetime: Ab initio nonadiabatic molecular dynamics study
    Authors: Huiwen Xiang, Zhenfa Zheng, Ke Zhao, Chengyan Liu,* Jin Zhao,*
    Journal: Advanced Functional Materials
    Year: 2024

  • Title: Synergistic densification in hybrid organic-inorganic MXenes for optimized photothermal conversion
    Authors: Tong Xu, Shujuan Tan,* Shaoxiong Li, Tianyu Chen, Yue Wu, Yilin Hao, Chengyan Liu,* Guangbin Ji,*
    Journal: Advanced Functional Materials
    Year: 2024

  • Title: Defect-complex engineering to improve the optoelectronic properties of CuInS2 by phosphorus incorporation
    Authors: Huiwen Xiang, Jinping Zhang, Feifei Ren, Rui Zhu, Yu Jia, Chengyan Liu,*
    Journal: Physical Review Applied
    Year: 2023

  • Title: Analytical energy formalism and kinetic effects of grain boundaries: A case study of graphene
    Authors: Chengyan Liu, Zhiming Li, Xingao Gong,*
    Journal: Applied Physics Letters
    Year: 2024

 

Mehdi gheisari | Cyber security | Member

Dr. Mehdi gheisari | Cyber security | Member

PHD at Guangzhou University, China

Mehdi Gheisari is a cybersecurity researcher specializing in privacy-preserving solutions for IoT at Harbin Institute of Technology, Shenzhen. With a PhD in Cyber Space Security from Guangzhou University, China, and a background in Software Engineering from Iran, he brings expertise in SDN, WSN, and Semantic Web. As a lecturer for eight years and experienced project manager, he’s contributed to international and national research projects. Notable for his extensive publication record and awards, Mehdi is known for his active role in conferences and as an associate editor. He’s committed to advancing cybersecurity and fostering cross-cultural collaboration in technology.

Professional Profiles:

Education

Faculty member, Harbin Institute of Technology, Shenzhen, China (Aug 2021 – Present) Researcher, SUSTECH, Shenzhen, China (July 2020 – Aug 2021) Visiting Researcher, ITMO, St. Petersburg, Russia (Sep 2020 – Aug 2021) PhD in Cyber Space Security, Guangzhou University, China (Sep 2016 – Dec 2019) MSc in Software Engineering, IAU, and Amirkabir University of Technology, Iran (Sept 2007 – June 2010)

Work Experiences

  • Programmer with .NET, IGS, Iran (Sept 2009 – Dec 2009)
  • Software Engineer, ICDL, Iran (JUN 2013 – Dec 2013)
  • Project Manager, Fanava, Iran (Sept 2013 – Jan 2016)
  • Tech Consultant at Bull Run Consulting Company, bullrun.biz/leadership-team

Teaching Experience:

Lecturer, IAU, Shamsipour, UAST, Iran (Sept 2008 – Aug 2015)

Achievements/Awards:

Guangzhou University award (2016-2019) Winner of Guangdong Government Outstanding Student Award (2017) Best Researcher Award, Islamic Azad University (2015)

Research Interests

Security and Privacy in IoT and Autonomous Vehicles Software Defined Networking (SDN) Wireless Sensor Networks Semantic Web Big Data

Research Focus:

Mehdi Gheisari’s research primarily focuses on privacy-preserving solutions within IoT-based smart cities. He has made significant contributions to this field, as evidenced by his publications in reputable journals and conferences. His work encompasses the development of frameworks, architectures, and methodologies that address the complex challenges of privacy and security in IoT environments. Gheisari’s research extends to areas such as edge computing, deep learning, semantic web, and software-defined networking, aiming to create robust and efficient systems for protecting user data and ensuring the integrity of IoT infrastructure.

Publications

  1. Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification, Publication: 2024.
  2. Spatio-temporal Data Analytics for e-Waste Management System Using Hybrid Deep Belief Networks, Publication: 2024.
  3. Correction: Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review, Publication: 2024.
  4. A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks, Publication: 2024.
  5. Accident reduction through a privacy-preserving method on top of a novel ontology for autonomous vehicles with the support of modular arithmetic, Publication: 2024.
  6. Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques,  Publication: 2024.
  7. AI in Nuclear Medical Applications: Challenges and Opportunities, Publication: 2024.
  8. CAPPAD: a privacy-preservation solution for autonomous vehicles using SDN, differential privacy and data aggregation, Publication: 2024.
  9. Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review, Publication: 2024.
  10. An efficient computer-aided diagnosis model for classifying melanoma cancer using fuzzy-ID3-pvalue decision tree algorithm, Publication: 2024.
.