Rongli Sun | Big Data | Best Researcher Award

Published on by Global Academic Awards

Dr. Rongli Sun | Big Data | Best Researcher Award

Dr. Rongli Sun, Chongqing University of Posts and Telecommunications, China

Dr. Rongli Sun is a dedicated researcher at Chongqing University of Posts and Telecommunications, China 🇨🇳, specializing in Big Data Mining and Life Estimation Algorithms for New Energy Vehicles 🚗🔋. His expertise lies in battery State of Health (SOH) estimation using advanced models like BiGRU-Attention and neural networks 🧠. Proficient in Matlab, Python, and C, he has published in top journals such as Energy and Journal of Power Sources 📚. Passionate about sports, he enjoys basketball 🏀 and marathon running 🏃‍♂️. Dr. Sun’s work significantly contributes to electric vehicle sustainability and intelligent battery management systems.

Publication Profile

Scopus

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🏫 Employment

Dr. Rongli Sun has been serving at the School of Computer Science and Technology at Chongqing University of Posts and Telecommunications, China 🇨🇳. In this role, he actively contributes to cutting-edge research in Big Data Mining, Neural Networks, and Battery Life Estimation for New Energy Vehicles 🔋🚗. His academic involvement includes both teaching and guiding research projects, fostering innovation in intelligent energy systems 💡. Through his position, Dr. Sun continues to advance sustainable technologies and smart mobility solutions, helping shape the future of eco-friendly transportation and battery diagnostics 🌱🔧

📚 Academic Contributions

Dr. Rongli Sun has made notable contributions to the field of battery health diagnostics through his extensive research and publications 📖. He has authored several peer-reviewed journal articles and international conference papers, demonstrating expertise in data-driven approaches and intelligent algorithms 🔍🧠. His works are featured in high-impact journals like Energy, Journal of Power Sources, and Journal of Energy Storage 📑. Notably, his 2025 article in Energy introduced the BiGRU-Attention model, showcasing advanced deep learning applications in real-world lithium-ion battery State of Health (SOH) estimation 🔋📊. His research supports smarter, more sustainable energy systems 🌱

🔬 Research Focus

Dr. Rongli Sun focuses his research on Big Data Mining and Life Estimation Algorithms for New Energy Vehicles 🚗🔋, addressing critical challenges in energy efficiency and battery longevity. His work primarily centers on the State of Health (SOH) estimation of lithium-ion and lead-acid batteries, aiming to improve predictive maintenance and operational safety ⚙️📊. By leveraging large-scale data and intelligent models, Dr. Sun contributes to the advancement of sustainable energy and smart mobility technologies 🌱🚀. His innovative methods play a key role in enhancing the reliability and performance of electric vehicle power systems worldwide 🌍

Conclusion

Dr. Rongli Sun is highly suitable for the Research for Best Researcher Award. His cutting-edge contributions to battery health estimation in new energy vehicles, solid publication record, and alignment with global sustainability goals make him a compelling nominee

Publication Top Notes

  • 📘 Sun R, Chen J, Li B, et al. State of health estimation for Lithium-ion batteries based on novel feature extraction and BiGRU-Attention model. Energy, 2025

  • 📘 Sun R, Chen J, Piao C. Battery health features extraction and state of health estimation based on real-vehicle operation data. Journal of Power Sources, 2024

  • 📘 Piao C, Sun R, Chen J, et al. A feature extraction approach for state-of-health estimation of lithium-ion battery. Journal of Energy Storage, 2023

  • 📘 Sun R, Xie J, Piao C. A multi-scenario driving range prediction method for electric vehicles in low temperature. Proceedings of the 16th International Conference on Computer Science and its Applications (CSA), 2024

  • 📘 Sun R, Liu Q. Research on Electric Vehicle State of Health Estimation Based on Multi-Feature Attribute Data Mining. Proceedings of the 4th International Conference on Electronics Technology and Artificial Intelligence (ETAI), 2025

  • 📘 Sun R, Hu P, Wang R, et al. A new method for charging and repairing Lead-acid batteries. IOP Conference Series: Earth and Environmental Science, 2020

 

Mohammad Alipour-Vaezi | Data-Driven Optimization | Best Researcher Award

Published on by Global Academic Awards

Mr. Mohammad Alipour-Vaezi | Data-Driven Optimization | Best Researcher Award

Mr. Mohammad Alipour-Vaezi, Virginia Polytechnic Institute and State University, United States

Mr. Mohammad Alipour-Vaezi is pursuing a Ph.D. in Industrial & Systems Engineering with a focus on Operations Research at Virginia Tech, USA. He holds an M.Sc. in Systems Optimization from the University of Tehran and a B.Sc. in Industrial Safety from Shahid Beheshti University of Medical Sciences. His research spans data-driven optimization, healthcare excellence, and supply chain management. He has industry experience as the CEO of ELSA Group and internships in healthcare systems. Notable accolades include a patent for an automatic guitar tuner and recognition as an elite student researcher at UT. 🎓🔬

 

Publication profile

Google Scholar


Educational Background

Mr. Mohammad Alipour-Vaezi is currently pursuing his Ph.D. in Industrial & Systems Engineering at Virginia Tech, with a concentration in Operations Research, under the supervision of Prof. Kwok L. Tsui. He earned his M.Sc. in Industrial Engineering-Systems Optimization from the University of Tehran, where he graduated with a GPA of 3.82 and completed a thesis on disruption management using data mining in the insurance industry. His academic journey began with a B.Sc. in Industrial Safety from Shahid Beheshti University of Medical Sciences, where he worked on improving hospital processes. 📊✨

 

Teaching Experience

Mr. Mohammad Alipour-Vaezi has significant teaching experience as a Graduate Teaching Assistant at Virginia Tech and the University of Tehran. At Virginia Tech, he assisted in the Manufacturing Process Lab in Fall 2023 and supported the Introduction to ISE course in Spring 2023 and 2024 under Prof. M. Earnest. Previously, at Shahid Beheshti University of Medical Sciences, he served as an Instructor for Deterministic Operations Research in Fall 2022. He also worked as a Graduate Teaching Assistant for Scheduling Theory, Queuing Systems, and Design of Industrial Systems at the University of Tehran from Fall 2020 to Spring 2022. 🎓✏️

 

Industry Experience

Mr. Mohammad Alipour-Vaezi brings valuable industry experience as the CEO of ELSA Group in Tehran, where he led international trading operations from September 2019 to August 2021. His practical insights were further enriched during his internships at Loghman Hakim Hospital. From April to August 2019, he focused on reviewing and modifying the Health, Safety, and Environment (HSE) system, ensuring compliance and safety standards. Earlier, from July to October 2018, he investigated and enhanced the hospital’s structural processes, contributing to operational efficiency. This blend of leadership and hands-on experience underscores his strong foundation in industrial engineering. 🔍📈

 

Honors & Awards

Mr. Mohammad Alipour-Vaezi has received notable recognition for his academic and creative achievements. In 2022, he was a candidate for the Elite Student Researcher award at the University of Tehran, showcasing his commitment to research excellence. He ranked 27th in Iran’s nationwide university entrance exam for his M.Sc. in 2019. His innovative spirit was highlighted when he became a finalist in the Khwarizmi Youth Awards in both 2013 and 2014, for his contributions to Persian classic literature and mechatronics, respectively. Notably, he holds a patent for his invention, the Automatic Guitar Tuner. 🎓🎶

 

Research Experience

Mr. Mohammad Alipour-Vaezi has extensive research experience as a Graduate Research Assistant at Virginia Tech, focusing on Data-Driven Optimization in Prof. K.L. Tsui’s lab since May 2023 and Healthcare Excellence in Dr. H. Zhong’s lab. Prior to this, he contributed to multiple projects at the University of Tehran, where he worked from September 2020 to January 2023. His research there included Supply Chain Management and Healthcare Systems under Prof. R. Tavakkoli-Moghaddam, Data-Driven Queuing Systems with Prof. F. Jolai, and Data-Driven Coordination Contracts in Prof. M. Rabbani’s lab. 📊💡

 

Conclusion 🏆

Mr. Mohammad Alipour-Vaezi’s diverse academic background, strong research focus in operations research, healthcare systems, and optimization, coupled with teaching experience and practical industry contributions, make him a strong candidate for the Best Researcher Award. His achievements and research impact, particularly in supply chain management and healthcare systems, align well with the award’s criteria for innovation and academic excellence.

 

Publication Top Notes

  • Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study – 44 citations (2022) 🏥
  • Prioritizing post-disaster reconstruction projects using an integrated multi-criteria decision-making approach: A case study – 42 citations (2022) 🏗️
  • Introducing a novel revenue-sharing contract in media supply chain management using data mining and multi-criteria decision-making methods – 28 citations (2022) 📈
  • An M/M/C/K queueing system in an inventory routing problem considering congestion and response time for post-disaster humanitarian relief: a case study – 26 citations (2022) 🚚
  • Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques – 16 citations (2024) 🌍
  • Scheduling the COVID-19 vaccine distribution based on data-driven decision-making methods – 16 citations (2021) 💉
  • A new methodology for COVID-19 preparedness centers based on a location-allocation platform – 13 citations (2020) 🏥
  • A new application of coordination contracts for supplier selection in a cloud environment – 11 citations (2020) ☁️
  • A diagnostic analytics model for managing post-disaster symptoms of depression and anxiety among students using a novel data-driven optimization approach – 9 citations (2023) 📊
  • Optimization of a television advertisement scheduling problem by multi-criteria decision making and dispatching rules – 9 citations (2022) 📺