Zhixiao Wang

Major Details: School of Computer Science and Technology, majoring in Computer Science, expected to graduate in June 2025.

Main Courses: Linear Algebra; Advanced Mathematics; Algorithm Design and Analysis; Operating Systems; Compiler Principles; Digital Logic Design; etc.

Elective Courses: Artificial Intelligence; Network Security; etc.

I led our vision group in developing algorithms for the RMUC, focusing on advanced deep learning models for real-time object recognition and tracking. Our efforts significantly boosted our robots' operational capabilities, leading to our winning the first prize in this prestigious competition that challenges a broad spectrum of engineering skills.

During this period, I self-studied "Dive into Deep Learning" and made use of what I learned by modifying and optimizing the YOLOv5-face model for our RoboMaster competition. This project was crucial for improving the real-time object recognition capabilities of our robots.

I led the team to a championship victory in the BOTEC2022, where we developed advanced visual navigation systems for humanoid robots equipped with infrared structured light depth cameras. The competition focused on autonomous navigation and task execution.

I designed and implemented a pipelined CPU based on the RISC-V instruction set using Verilog. This project enhanced my skills in computer architecture and hardware design. Also my source code is available on GitHub.

I participated in the design and development of a high-performance fault-tolerant storage system based on EVENODD encoding. I contributed to the initial code writing and subsequent algorithm optimization and parallelization, focusing on enhancing system reliability and performance.

I independently completed 11 labs as part of the MIT 6.S081 course, implementing various system-level features within the xv6 operating system. These tasks involved advanced areas such as memory management, concurrency synchronization, and file systems.

As a teaching assistant for the Digital Logic Design course, I was responsible for designing and implementing a new serial communication experiment. This initiative was aimed at enhancing the practical understanding of digital logic among students.

In the ASC24 Supercomputer Competition, I played a key role in optimizing LLM inference and managing power consumption during the preliminary round. Our team's efforts led to high-performance results in the finals, tackling complex tasks in the GoMars and Group Competition events, achieving the first prize.