PhD student | Shandong University | China
Mr. Daojun Liang is a dedicated PhD student at Shandong University with a solid academic background in computer science. He earned his BS from Taishan University in 2016 and his MS from Shandong Normal University in 2019. Currently pursuing his doctoral studies, Daojun has established himself as a researcher with expertise in uncertainty quantification, time series analysis, and large language models (LLM). Recognized for his independent research skills, Daojun has published several high-level papers in prestigious journals and serves as a reviewer for reputable organizations like IEEE, ACM, Elsevier, and Springer.
Profile
Scholar
Education
Daojun Liang began his academic journey with a Bachelor’s degree in Computer Science from Taishan University in 2016. Driven by a passion for innovation, he pursued a Master’s degree in Information Science and Engineering at Shandong Normal University, which he completed in 2019. His commitment to academic excellence led him to Shandong University, where he is currently advancing his research as a PhD candidate. His educational foundation has equipped him with the skills necessary for cutting-edge research and practical problem-solving in the fields of artificial intelligence and computational sciences.
Experience
Daojun’s research and professional experience demonstrate his versatility and expertise. He has contributed to several impactful projects, such as the development of intelligent vehicle networking technologies and the creation of advanced forecasting methods for 6G communication systems. His work with data-driven analysis and artificial intelligence for industrial applications highlights his ability to address complex challenges. Additionally, his role as an SCI reviewer for leading journals and collaborations with esteemed institutions like Fortiss GmbH and Shanghai Jiao Tong University reflect his strong academic and professional network.
Research Interests
Daojun’s research interests encompass long-term time series forecasting, uncertainty quantification, and the development of probabilistic inference methods. He focuses on analyzing intrinsic patterns in data to propose efficient and lightweight solutions. His work has implications for a variety of industries, including energy, manufacturing, and telecommunications. Daojun is also exploring the intersection of deep learning, natural language processing, and computer vision, ensuring his research remains at the forefront of innovation.
Awards and Recognitions
Daojun has been nominated for the Best Researcher Award in recognition of his outstanding contributions to academia and industry. His innovative methods for time series analysis and uncertainty quantification have not only been published in high-impact journals but have also been widely adopted in industrial applications. He has been honored as a reviewer for leading journals and conferences, which underscores his influence in the research community.
Publications
Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Progressive Supervision via Label Decomposition: A Long-Term and Large-Scale Wireless Traffic Forecasting Method. Knowledge-Based Systems, 305, p.112622. (SCI Q1, IF = 7.2). Cited by 10.
Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Periodformer: An Efficient Long-Term Time Series Forecasting Method Based on Periodic Attention. Knowledge-Based Systems, 304, p.112556. (SCI Q1, IF = 7.2). Cited by 8.
D. Liang, H. Zhang, D. Yuan, M. Zhang. (2024). Multi-Head Encoding for Extreme Label Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. (SCI Q1, IF = 20.8). Cited by 15.
Liang, D., Yang, F., Wang, X., et al. (2019). Multi-Sample Inference Network. IET Computer Vision, 13(6), 605-613. (SCI Q1, IF = 1.7). Cited by 12.
Liang, D., Zhang, H., Yuan, D., et al. (2025). DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting. ACM SigKDD 2025. Cited by 5.
Conclusion
Daojun Liang exemplifies the qualities of a modern researcher: innovative, dedicated, and collaborative. His contributions to uncertainty quantification, time series analysis, and large language models are reshaping academic and industrial practices. With numerous publications, collaborative projects, and a commitment to advancing knowledge, Daojun stands as a promising figure in his field.