Sheng Xiang | AI | Research Excellence Award

Assoc. Prof. Dr. Sheng Xiang | AI | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Sheng Xiang is an Associate Researcher at Chongqing University, China, specializing in intelligent battery management systems, energy storage analytics, and data-driven prognostics for lithium-ion batteries. His research expertise lies at the intersection of artificial intelligence, machine learning, and electrochemical energy systems, with a particular focus on remaining useful life prediction, state-of-charge estimation, and lightweight deep learning models for real-world battery applications. He has authored 30 peer-reviewed publications, which have collectively received 1,655 citations, reflecting strong international recognition and an h-index of 17. His recent contributions in high-impact journals such as Energy and Journal of Energy Storage demonstrate methodological innovation and practical relevance, especially for electric vehicles and smart energy systems. With an active collaboration network involving 57 co-authors, his work supports interdisciplinary research and global knowledge exchange. The societal impact of his research is evident in its potential to enhance battery safety, efficiency, sustainability, and lifecycle management in next-generation energy technologies.

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Top 5 Featured Publications

Mohammad Amin | Gradient boosting | Young Scientist Award

Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.