QL Qiwei Liang
A bright university study desk with a laptop, notebook sketches, and computer vision project materials.

Undergraduate E-Portfolio

Computer Vision and Agentic AI coursework in practice.

I am Qiwei Liang, a Computer Science undergraduate with academic experience across the University of Idaho and Suzhou City University. This dual background has shaped how I approach technical work: I value rigorous engineering fundamentals, clear collaboration, and systems that solve practical problems.

My strongest interests are Computer Vision and Agentic AI. In my undergraduate projects, I have built YOLOv8-based detection pipelines, OpenCV data processing workflows, and LLM-supported reasoning loops that turn raw visual or environmental data into actionable decisions.

This portfolio collects selected course outcomes from capstone design, software engineering, database systems, and AI research, with emphasis on how each project was implemented and evaluated.

View My Research Read CV
University of Idaho B.S. Computer Science · GPA 3.76/4.0
Suzhou City University B.E. Computer Science · Academic Excellence Award
Research Direction Computer Vision · Agentic AI · Applied ML Systems

Portfolio

Artificial Intelligence & Research

Drone monitoring crops with computer vision detection overlays.

Agentic AI · Autonomous monitoring

Agentic-PestGuard

I designed an autonomous drone monitoring workflow for agricultural pest detection. The project integrates YOLOv8 for real-time visual recognition, OpenCV for frame processing, RTSP for low-latency video streaming, and an LLM-based reasoning loop to evaluate weather, crop stage, and treatment context.

The agentic layer was built to move beyond detection alone: it can synthesize field observations with retrieved agricultural knowledge and produce structured audit reports for decision support.

#Python #PyTorch #YOLOv8 #OpenCV #LLM #RAG #ComputerVision
Killer yeast computer vision detection result.

Capstone · Biological image analysis

Killer Yeast Strain Identification

I developed a computer vision pipeline to automate recognition of killer yeast strains in microscopic datasets. The workflow combines YOLOv8 model training, OpenCV-based augmentation, and K-Means clustering to analyze morphology and sample distribution.

The project focused on robustness under real imaging conditions: augmentation scripts improved tolerance to noise, lighting variation, and inconsistent microscope captures.

Raw microscope input
Detection result 1
Detection result 2
#Python #PyTorch #YOLOv8 #OpenCV #KMeans #ComputerVision #Capstone
Software Engineering

Intelligent Game Agent: Boss Fight Module

Engineered complex boss behaviors in Unity using finite state machines and behavior trees, then managed asset integration and collaboration through Git.

#Unity2D #CSharpConcepts #FSM #BehaviorTree #Git
Database Systems

Graduate Student Admission Management System

Designed a responsive Vue.js frontend, PHP backend, and MySQL relational database to manage applicant records and application status tracking.

#VueJS #PHP #MySQL #Bootstrap #FullStack

Resume / CV

Application documents

Public preview version: phone number, home address, and street-level personal details are intentionally removed for submission privacy.

Curriculum Vitae

Qiwei Liang

Sanitized preview

Education

University of Idaho, Moscow, ID
Bachelor of Science in Computer Science · Aug 2025 - Jul 2026
GPA: 3.76/4.0

Suzhou City University, Suzhou, China
Bachelor of Engineering in Computer Science · Sep 2022 - Jun 2025
GPA: 3.5/4.0 · Second-Class Academic Excellence Award

Selected Projects

  • Agentic-PestGuard: autonomous drone monitoring and AI decision support using YOLOv8, RTSP, OpenCV, LLM reasoning, and RAG.
  • Killer Yeast Strain Identification: capstone vision pipeline using YOLOv8, OpenCV augmentation, and K-Means clustering.
  • Intelligent Game Agent: Unity boss fight module using finite state machines and behavior trees.
  • Graduate Admission Management System: Vue.js, PHP, and MySQL full-stack applicant management system.

Skills

Python, C++, PHP, JavaScript, SQL, HTML/CSS, PyTorch, YOLOv8, OpenCV, Unity 2D, Bootstrap, Git/GitHub, VS Code, MySQL, Linux/Unix.

Contact

For professional and academic inquiry.

Admissions reviewers, faculty, and collaborators can use the form or the links below to reach me.

2908375382@qq.com GitHub LinkedIn