Zhijian Fan

fanzj0523@bupt.edu.cn  ·  +86 177 2351 9127  ·  Beijing, China / Available for internship
Email: mailto:fanzj0523@bupt.edu.cn
Phone: tel:+8617723519127

EDUCATION
=========

Beijing University of Posts and Telecommunications (BUPT) | Queen Mary University of London   (2023 - 2027 Expected)
B.Eng, Telecommunication Engineering and Management

EXPERIENCE
==========

Faculty of Science, The University of Hong Kong — Research Project Member - Substrate-Level Failure Prediction   (2025 - Present)
  - Joined a faculty research project on semiconductor manufacturing analytics and substrate-level failure prediction.
  - Translated sparse wafer defect patterns into modeling objectives and experiment plans for predictive analysis.
  - Contributed to iterative research discussions on model robustness across different wafer specifications and manufacturing conditions.

International School, Beijing University of Posts and Telecommunications — Developer - Student Information Management and Personal Planning System   (2025 - Present)
  - Helped build and launch a student data platform that reached 2,000+ users after school-wide promotion.
  - Owned end-to-end product iteration across user workflows, data analysis logic, backend services, and deployment reliability.
  - Worked on a data flywheel loop that connected real user activity with continuous improvements to prediction and recommendation features.

Innovation Centre, Queen Mary University of London — Developer - QMUL WinterHack Challenge   (2026 - 2026)
  - Developed a ROS2-based robotic arm vehicle on Raspberry Pi, focusing on software and algorithm design.
  - Implemented SLAM for localization and mapping and A* search for path planning.
  - Integrated perception and control modules to enable autonomous navigation and object grasping.

OxCam Programme, University of Cambridge — Participant - Large Language Model & Generative AI Summer Camp   (2024 - 2024)
  - Completed three weeks of coursework and interdisciplinary research under the supervision of Prof. Pietro Lio.
  - Explored generative AI and computer vision research directions through a final project on sign language recognition.
  - Delivered the final presentation and received a Distinction grade.

PROJECTS
========

Substrate-Level Failure Prediction Based on Deep Learning — Data Science / Deep Learning Research   (2025 - Present)
  - Prepared substrate-level defect data for modeling, including sparse-pattern preprocessing and feature extraction.
  - Built neural network experiments to predict die failure from manufacturing defect signals.
  - Evaluated model behavior across wafers with different specifications to improve robustness and generalization.
  - Connected prediction outputs with production-efficiency goals in the semiconductor manufacturing context.

Student Information Management and Personal Planning System — AI / Data Platform   (2025 - Present)
  - Modeled long-term learning and career trajectories with a Transformer-based framework on student data.
  - Designed recommendation and prediction logic for personal planning scenarios.
  - Implemented lightweight database, relay, and authentication services to support cross-network usage.
  - Built feedback pathways from user activity to platform iteration, supporting a data flywheel after launch.

QMUL WinterHack Challenge — Robotics / Embedded AI Project   (2026 - 2026)
  - Built a ROS2-based robotic arm vehicle on Raspberry Pi with a focus on software and algorithm design.
  - Implemented SLAM for localization and mapping, plus A* search for path planning.
  - Integrated perception and control modules to enable autonomous navigation and object grasping.

Self-Supervised Hand Pose Estimation and Recognition — Generative AI / Computer Vision Research   (2024 - 2024)
Distinction
  - Studied self-supervised learning methods for hand pose estimation.
  - Explored how hand-pose representations can support sign language recognition.
  - Produced the final presentation titled "Self-Supervised Hand Pose Estimation and Recognition" and received a Distinction grade.

SKILLS
======

Programming: Python, PyTorch, C
AI & Data: Machine Learning, Deep Learning, Transformer Models, Predictive Modeling, Data Analysis
Robotics & Algorithms: ROS2, SLAM, A* Path Planning, Raspberry Pi
Backend & Systems: Backend Services, Database Services, Authentication Services, API Integration, Lightweight Backend Architecture
Languages: Chinese (Native), English (IELTS 7.0)
Coursework: Machine Learning and Data Analysis, Data Structures, Signals and Systems Theory, Digital Signal Processing, Digital Circuit Design, Electronic and Circuit Foundation
Interests: AI4S, Agentic AI

cv.ha7ch.com/hodor