Dr. Ting Li | Fault prediction | Best Researcher Award

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Dr. Ting Li | Fault prediction | Best Researcher Award 

Researcher and Lecturer, Guangxi University, China

Dr. Ting Li is a distinguished researcher and lecturer at the College of Computer and Electronic Information, Guangxi University, recognized for her outstanding contributions in the fields of mobile edge computing, resource allocation, optimization theory, and networked intelligent systems. She earned her Doctor of Philosophy (Ph.D.) in Cyberspace Security from the Institute of Information Engineering, Chinese Academy of Sciences, where she focused on developing intelligent, secure, and privacy-aware computational frameworks that address real-world challenges in edge computing environments. Her academic foundation began with a Bachelor’s degree in Communication Engineering from Chongqing University, equipping her with strong interdisciplinary expertise bridging communication networks and artificial intelligence. Since joining Guangxi University as a lecturer, Dr. Ting Li has been actively involved in teaching, mentoring, and research, leading and participating in several high-impact projects funded by the National Natural Science Foundation of China and the National Key R&D Program, focusing on cross-modal recognition, task offloading, and secure data processing for IoT systems. Her research interests encompass intelligent task scheduling, distributed optimization, cross-modal data analysis, and AI-driven resource management for next-generation computing systems. Dr. Ting Li’s research skills include advanced algorithm design, deep reinforcement learning, model caching, multi-hop task offloading, and edge intelligence optimization, all of which contribute to enhancing efficiency and security in distributed networks. Her publications in world-renowned journals such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Letters, and IEEE Internet of Things Journal have established her as an influential scholar in AI-powered edge computing research. She has been recognized for her research excellence through multiple institutional and academic commendations, including awards for innovation and outstanding scientific contributions in the domain of intelligent communication systems. Her scholarly work, reflected in her growing citation record, demonstrates both academic rigor and global impact.

Profiles: Google Scholar

Featured Publications

  • Li, T., Liu, Y., Ouyang, T., Zhang, H., Yang, K., & Zhang, X. (2025). Multi-hop task offloading and relay selection for IoT devices in mobile edge computing. IEEE Transactions on Mobile Computing, 24(1), 466–481. Cited by: 13

  • Li, T., Sun, J., Liu, Y., Zhang, X., Zhu, D., Guo, Z., & Geng, L. (2023). ESMO: Joint frame scheduling and model caching for edge video analytics. IEEE Transactions on Parallel and Distributed Systems, 34(8), 2295–2310. Cited by: 10

  • Zhu, D., Liu, H., Li, T., Sun, J., Liang, J., Zhang, H., & Geng, L. (2021). Deep reinforcement learning-based task offloading in satellite-terrestrial edge computing networks. IEEE Wireless Communications and Networking Conference (WCNC), 1–7. Cited by: 63

  • Zhu, D., Li, T., Tian, H., Yang, Y., Liu, Y., Liu, H., Geng, L., & Sun, J. (2021). Speed-aware and customized task offloading and resource allocation in mobile edge computing. IEEE Communications Letters, 25(8), 2683–2687. Cited by: 17

  • Li, T., Liu, H., Liang, J., Zhang, H., Geng, L., & Liu, Y. (2020). Privacy-aware online task offloading for mobile-edge computing. International Conference on Wireless Algorithms, Systems, and Applications (WASA), Qingdao, China. Cited by: 12

Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award

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Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award 

Emerging Research Scholar, Banaras Hindu University, India

Mr. Ankush Sharma is a dynamic and emerging research scholar in the domain of Statistics, specializing in Survival Analysis, Reliability Engineering, Degradation Modeling, Bayesian Estimation, and Functional Modeling. He is currently pursuing his Ph.D. in Statistics from Banaras Hindu University, Varanasi, India, where his research focuses on Statistical Modeling and Experimental Designs Planning for Highly Reliable Products under the supervision of Prof. Sanjeev Kumar. He has contributed actively to the global research community through publications in reputed Scopus and SCI-indexed journals and has served as a reviewer for distinguished journals such as the International Journal of Quality & Reliability Management and the Asia Pacific Prognostics and Health Management Conference. His research interests include the design of experiments for high-reliability systems, stochastic degradation modeling, and Bayesian hierarchical analysis for predictive maintenance and reliability forecasting.  His published work demonstrates his capacity for innovation and rigor, as seen in his research on thermal damage modeling, accelerated degradation testing, and stochastic EM approaches for reliability prediction. With a clear vision toward academic and research excellence, Mr. Ankush Sharma continues to contribute meaningfully to the statistical sciences community through teaching assistance, peer reviewing, and mentoring junior researchers. His professional trajectory, marked by academic distinction, research innovation, and scientific integrity, positions him as a promising scholar and future academic leader in applied statistics and reliability research.

Profile: Google Scholar | ORCID

Featured Publications

  • Sharma, A. (2025). Determination of Thermal Damage and Failure Time Analysis in Rocks Using Stochastic Models. Quality Reliability Engineering International, 2 citations.

  • Sharma, A., Tomer, S. K., & Panwar, M. S. (2025). Optimal Plans for Accelerated Destructive Degradation Tests with Stress Interaction Effects. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Modeling Degradation Processes with Covariate-Dependent Random Initiation: A Stochastic EM Approach with Application to Rock Mechanics. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Survival Adjusted Sequential Bayesian Experimental Designs for Degradation Models. Manuscript under review.

Dongfang Zhao | Fault Diagnosis | Best Researcher Award

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Dr. Dongfang Zhao | Fault Diagnosis | Best Researcher Award

Lecturer at Shanghai Polytechnic University, China

Dr. Dongfang Zhao is currently serving as a lecturer and master’s supervisor at Shanghai Polytechnic University. He embarked on his research journey in artificial intelligence and mechanical fault diagnosis in 2014. Earning his Ph.D. from Shanghai University in 2021, he immediately pursued postdoctoral research at the Control Science and Engineering Research Station. His doctoral dissertation was distinguished with a nomination for the National Outstanding Doctoral Dissertation Award in the field of measurement control and instrumentation.

👨‍🔬 Profile

ORCID

🏆 Suitable for “Best Researcher Award”

Dr. Dongfang Zhao demonstrates exceptional merit for the Best Researcher Award through his impactful contributions in artificial intelligence and mechanical fault diagnosis. With a rapidly evolving research profile, he has delivered high-quality publications, led prestigious projects, and developed novel AI diagnostic frameworks. His work significantly advances diagnostic accuracy and robustness in complex environments, positioning him as a leading voice in his field.

🎓 Education

Dr. Zhao obtained his Ph.D. from Shanghai University in 2021. His academic journey reflects a deep and continuous commitment to advanced research in AI applications for engineering diagnostics. His dissertation stood out nationally in the field, affirming both the quality and relevance of his academic training.

💼 Experience

Dr. Zhao has built a strong professional trajectory by joining leading research stations and universities in China. Since October 2024, he has held the position of lecturer at Shanghai Polytechnic University. His prior engagement as a postdoctoral researcher at Shanghai University significantly contributed to national-level research projects, and he continues to mentor students and lead research as a master’s supervisor.

🔬 Research Interest

Dr. Zhao’s research spans Artificial Intelligence, Mechanical Fault Diagnosis, and Control Engineering. His work targets real-world engineering problems, especially fault detection under variable operational conditions. He introduced networks like DRANet, DBANet, and SCANet, each addressing specific challenges such as strong noise environments, variable speeds, and unlabeled data — critical innovations in industrial AI.

📚 Publications

Dr. Zhao has published more than 20 high-quality SCI/EI papers, with over 10 as first or corresponding author. His research appears in reputed journals like Advanced Engineering Informatics, Computers and Electronics in Agriculture, and IEEE Journals, and his work has received nearly 500 citations globally, reflecting its influence.

Notable Published Journals (with Year):

  • Zhao, D. (2021). “AI-based DRANet for Noisy Fault Detection.” Advanced Engineering Informatics

  • Zhao, D. (2022). “Extreme Multi-Scale Entropy-Based Fault Diagnosis.” IEEE Transactions on Instrumentation and Measurement

  • Zhao, D. (2023). “SCANet: Unlabeled Fault Recognition Network.” Computers and Electronics in Agriculture