Welcome to my webpage!  I’m currently a Ph.D student at the School of Computer Science, Peking University, honorably under the supervision of Prof. Yinjun Wu. Before that, I obtained my master’s degree from the Politecnico di Milano (Polytechnic University of Milan (polimi)) in 2025, and worked honorably under supervision of Prof. Giacomo Boracchi during my Master Thesis. I am also actively involved honorably in research in collaboration with Prof. Hossein Abbasimehr.

Primary Research Interests: My research interests primarily lie at the intersections of database systems, data science and AI. My goal is to apply AI techniques to optimize core performance aspects of database systems—such as indexing and retrieval—in order to enhance system efficiency and reduce the operational workload of DBAs. Beyond database performance optimization, I also study information retrieval with AI models, focusing on how they can improve semantic search, query interpretation, and relevance ranking across diverse structured and unstructured data. In addition, I further investigate similarity search in large databases, with time series as a key application domain. Here, my focus is on developing improved representation learning techniques that enable more effective pattern discovery, clustering, and analysis of large-scale diverse temporal data.

Recent News

• September 2025 one paper accepted by Information Processing & Management (IP&M)
• September 2025 Started as PhD student at Peking University in the School of Computer Science
• August 2025 one paper accepted by Neurocomputing
• August 2025 one paper accepted by Cluster Computing
• March 2025 one paper accepted by Journal of Forecasting
• January 2025 one paper accepted by International Journal of Data Science and Analytics
• January 2024 one paper accepted by Expert Systems with Applications

Selected Publications

• Deep time-series clustering via evolutionary learning and graph-based manifold learning (IP&M 2025)
• Multi-View Clustering for Localized Global Time Series Forecasting (Neurocomputing 2025)
• Big time series data forecasting based on deep autoencoding and clustering (Cluster Computing 2025)
• Localized Global Time Series Forecasting Models Using Evolutionary Neighbor-Aided Deep Clustering Method (Journal of Forecasting 2025)
• A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniques (Expert Systems with Applications 2024)

Awards

• Awarded the Study in China on Chinese government scholarships, (CGS) CSC Scholarship for continuing PhD in Peking University.
• Awarded the IYT (Invest Your Talent) Scholarship by Italy for continuing Master in Italy.
• Ranked 3rd in the Machine Learning competition held by Politecnico di Milano and Reply company.
• Ranked 1st in the National RoboCup competition, line follower track.