Xu Wangjie, class of 2025 in Electrical Engineering of ZJUI, has earned multiple academic honors, including “Outstanding Academic Model Student” “Innovation and Entrepreneurship Model Student” and “Excellent Student” as well as a Third-Class Scholarship from Zhejiang University. He has also been recognized as an Outstanding Graduate of Zhejiang University.
He is a co–first author of Personalized Local Differentially Private Federated Learning with Adaptive Client Sampling, accepted by Institute of Electrical and Electronics Engineers International Conference on Acoustics, Speech, and Signal Processing(IEEE ICASSP), and a co-author of Intelligent Topology Control of Distribution Power Network for Increased Clean Energy Absorption Using Reinforcement Learning, published in Institute of Electrical and Electronics Engineers Asia Conference on Power and Electrical Engineering (IEEE ACPEE). As a member of a 2022 Zhejiang University National SRTP project, he conducted interdisciplinary research on federated learning and differential privacy, successfully completing the project defense and earning the award of Outstanding Performance in 2022 Undergraduate Summer Research Program.
In competitions, he was a Finalist Winner in the 2024 Mathematical Contest in Modeling/Interdisciplinary Contest in Modeling (MCM/ICM) and received the “Eaton Cup” Best Design Award in the ECE385 course. He has obtained exemption from examinations for master’s program of ZJUI.
ZJUI offers hands-on courses that bridge theory and practice, with ECE385 standing out for its focus on SystemVerilog programming and FPGA design. In this course, Xu Wangjie and his team won the prestigious “Eaton Cup” for creating FPGA-Minecraft, a 3D game built on FPGA. Inspired by Minecraft, the team tackled challenges in combining 3D rendering with FPGA programming, leveraging knowledge from SystemVerilog and linear algebra to overcome technical barriers and complete the award-winning project.
Xu also gained early research experience as a freshman in Assistant Professor Zhang Meng’s project on federated learning and differential privacy, where he worked on theoretical derivations, optimization problems, coding, and building a multi-Raspberry Pi platform. This work sharpened his research skills and prepared him for independent exploration.
In his junior year, Xu focused on using reinforcement learning to optimize power distribution networks, joining Associate Professor Diao Ruisheng’s multi-objective optimization project. His work on designing and tuning reinforcement learning agents led to a publication at the IEEE ACPEE conference, where presented the team’s findings.
Currently, Xu is developing a multi-agent reinforcement learning system with masking mechanisms for power system optimization, aiming to advance smart grid technologies and support the sustainable transformation of energy systems.
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