Published in Scientific Data (2025): long-term cattle recognition dataset and baseline verification framework.
My contribution: participated in full data pipeline design, detection/pose/ReID evaluation protocol construction, and baseline modeling experiments.
Measurable output: co-built dataset subsets reported in the paper: 16,889 images covering 5,661 cattle and 12,172 labeled images for long-term tracking of 103 cattle.
Engineering value: established reusable evaluation workflow for long-cycle recognition tasks, including cross-dataset validation settings.
Published in Computers and Electronics in Agriculture (2025): contactless top-view rotated cattle detection framework.
My contribution: built top-view cattle data processing and rotated object detection workflow, and implemented lightweight vision modeling modules.
Measurable output: paper-reported system-level results include about 70% parameter reduction and about 50% FLOPs reduction with at least 3% AP gain versus compared bottom-up approaches.
Engineering value: completed cascaded pipeline of detection, keypoint localization, and alignment for robust top-view scenarios.
Deliverables: production-oriented algorithm module package and verifiable technical documentation for recruitment review.
EEG Hidden-frequency System Based on Microtexture and Closed-loop Control
2025.10 · EEG software copyright project
My contribution: implemented EEG microtexture feature extraction and closed-loop control algorithm modules.
Optimization focus: tuned preprocessing and feature-flow stability for repeated closed-loop triggering experiments.
Deliverables: reusable algorithm implementation and validation scripts for iterative BCI experimentation.
Resume
I position my portfolio to show how I create practical AI and software value.
Target roles: LLM Application Engineer Intern, AI Developer Intern, and AI Product Engineering Intern. This page summarizes my technical scope, project ownership, and measurable outcomes for fast hiring evaluation.
5 End-to-End Projects2 Published Papers + 1 Under Review2 Software Copyrights
What This Portfolio Demonstrates
I am deeply interested in software development, large language models, and applied AI systems. In project practice, I focus on turning concepts into usable products with reliable engineering decisions.
I have built strong programming fundamentals and problem-solving ability through implementation-heavy work. I pay close attention to code quality, maintainability, and system design, and I adapt quickly in collaborative environments.
Engineering Direction: AI product development with strong software discipline.
Execution Style: clear scope, measurable progress, and practical delivery.
Project Execution Evidence: independently completed multi-project engineering runs across Shapeville implementation, MiniMind full pipeline reproduction, and WeClone end-to-end deployment validation.
Long-Term Goal: grow on the LLM/AI track and build products with real-world value.