Mr. Yang Lei | Energy Network Security | Best Researcher Award
School of Foreign Languages and Business, Shenzhen Polytechnic University, China
Yang Lei is a skilled data scientist and educator with substantial expertise in computer technology, applied mathematics, and big data systems. He has held several significant roles in both academia and industry, where he applied advanced data analytics and machine learning to a broad range of practical applications. Currently, he serves as a full-time computer science instructor at Shenzhen Polytechnic, where he also leads student innovation and entrepreneurship initiatives.
Education
Yang Lei completed his higher education at Hunan University, earning a Master’s degree in Computer Technology from 2014 to 2016, following a Bachelor of Science degree in Applied Mathematics from 2010 to 2014. His academic foundation integrates mathematical modeling with computational problem-solving, which laid the groundwork for his later work in algorithmic design and data-driven systems.
Experience
Yang’s professional career reflects a steady progression through increasingly complex roles in technology and analytics. At Shenzhen Polytechnic, he teaches subjects including data structures, big data, cloud computing, and Python programming. Before this, he worked as a Big Data Engineer at Shenzhen Hualang Education Investment Co., analyzing patterns in school performance data to guide educational investment strategies. From 2019 to 2021, he served as Project Manager at Shenzhen Medical Information Center, where he led the construction of a regional medical big data platform. Earlier roles include positions as a quantitative trading strategist at GF Securities (under China Unicom), where he employed neural networks and Bayesian models for stock and currency analysis, and as a cloud service operations manager for Guangzhou Unicom, managing innovation projects totaling over 60 million RMB.
Research Interest
Yang Lei’s research interests lie at the intersection of data mining, deep learning, and domain-specific applications such as healthcare, energy systems, and financial markets. He focuses on integrating structured and unstructured data through advanced machine learning frameworks to optimize decision-making and prediction accuracy. His work often incorporates neural networks, adversarial learning models, and multimodal data processing techniques, with particular attention to applications in electronic medical records, renewable energy, and cybersecurity.
Awards and Funding
His research initiatives have garnered recognition and funding from several prestigious programs. These include the Special Foreign Languages Research Project under the Guangdong Provincial Philosophical and Social Sciences Program (GD23WZXC02-17), the Shenzhen Philosophy and Social Science Planning Project (SZ2022D057), and internal research grants from Shenzhen Polytechnic University. These grants have supported his studies on intelligent systems and secure infrastructures in various industrial domains.
Publications
Yang Lei has authored multiple peer-reviewed articles in international scientific journals. His selected publications include:
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“Adversarial false data injection attacks on deep learning-based short-term wind speed forecasting”, IET Renewable Power Generation, 2024, Vol. 16(7), pp. 1370–1379. [Cited by: multiple studies in renewable forecasting security frameworks.]
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“Cybersecurity Challenges in PV-Hydrogen Transport Networks: Leveraging Recursive Neural Networks for Resilient Operation”, Energies, 2025, Vol. 18(9), Article 2262. [Cited by: interdisciplinary research on smart grid protection.]
These works emphasize his contribution to developing robust AI systems capable of withstanding adversarial inputs and ensuring stability in critical infrastructure.
Conclusion
Yang Lei exemplifies the modern multidisciplinary researcher and educator, combining strong theoretical training in mathematics and computer science with practical deployment of complex data systems. His cross-sector experience—from healthcare and finance to renewable energy—demonstrates a consistent commitment to solving real-world challenges using data-driven methods. With a trajectory marked by academic contribution, industry innovation, and educational leadership, he remains a vital contributor to the evolving landscape of applied artificial intelligence and big data analytics.