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hansu650/README.md
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About

I am Qin Tian, a Software Engineering undergraduate at Hubei University. I build practical systems around Python, AI Agent workflows, backend services, and machine learning, and I use open-source contribution as an engineering discipline: reproduce the issue, make the smallest defensible patch, add regression coverage, and follow the change through review.

  • Major rank: 1st · Academic average: 91.54 / 100
  • Lanqiao Cup Python: Provincial First Prize · National First Prize
  • Focus: AI Agent / Python SDK / backend engineering / applied AI
  • Three first-author manuscripts currently under anonymous review
  • 10 external technical pull requests merged across OpenAI Agents SDK, AnyIO, pydantic-settings, isort, Pydash, Hutool, PR-Agent, charset-normalizer, H2 Database, and Matplotlib
  • Open to AI engineering, Python backend, and software engineering internships

Open Source Contributions

Merged upstream

Project Contribution
OpenAI Agents SDK #4372 Added boundary validation and regression tests for non-positive audio channel counts.
AnyIO #1279 Fixed a worker-result delivery race during asyncio event-loop shutdown and added a deterministic end-to-end regression test.
pydantic-settings #994 Fixed empty JSON array parsing in CLI list arguments, with regression tests and documentation; refined through maintainer review to preserve mapping-union behavior.
isort #2683 Preserved CRLF blank lines during float-to-top preprocessing and fixed false-positive --check failures, with regression coverage.
Pydash #351 Fixed single-array intersection deduplication while honoring custom comparison rules and preserving the first representative; added 29 regression/control cases.
Hutool #4321 Fixed CSV comment handling with CRLF line endings, preserving original line numbers and header/range behavior; added 17 passing regression cases.
PR-Agent #2636 Closed a long-standing GitLab documentation gap by clarifying pipeline/webhook behavior and dependency-bot filtering workflows.
charset-normalizer #794 Restored source and wheel builds with setuptools 84.x; verified packaging, tests, lint, and coverage.
H2 Database #4344 Fixed SCRIPT tool option placement and added command-line regression coverage.
Matplotlib #31664 Clarified markevery float-spacing semantics in the public API documentation.

Selected work in review

Project Current status Contribution
browser-use #5457 Open · CLA pending Reported missing browser elements as action errors and added real-browser regression tests.
SQLAlchemy #13503 Open · Gerrit review pending Added per-statement asyncpg_timeout support with unit, integration, documentation, and changelog coverage.

Status snapshot: 2026-10-21. Merged and in-review contributions are separated deliberately. Closed, superseded, or duplicate work is excluded from the public portfolio.

Selected Engineering & Research Work

AI-assisted learning and focus platform built with FastAPI, MySQL, SQLAlchemy, Electron, JavaScript, and OpenAI API. I coordinated the team and worked across requirements, API design, database modeling, frontend/backend integration, focus-guard behavior, and deployment.

Research Manuscripts

First-author work spanning RGB-D semantic segmentation and visual time-series anomaly detection. I built reproducible PyTorch/Lightning and frozen-ViT evaluation pipelines, controlled ablations, robustness analyses, and claim-to-result evidence tracking.

All three manuscripts are under anonymous review. Titles, venue assignments, metrics, and repositories remain private until disclosure is permitted.

Technical Focus

Area Stack and practice
Python / AI Agent Python, OpenAI Agents SDK, LiteLLM, browser-use, tool-call workflows, pytest
Applied AI PyTorch, PyTorch Lightning, OpenCLIP ViT-B/16, DFormerV2, RGB-D segmentation, visual time-series anomaly detection
Backend / Data FastAPI, SQLAlchemy, MySQL, REST APIs, JWT, schema design, regression testing
Engineering Git, GitHub Actions, issue triage, minimal patches, CI review, Windows/Linux workflows

Current Direction

  • Deepen AI Agent and Python SDK engineering through reviewable open-source work.
  • Maintain reproducible research artifacts during anonymous review, then publish code and evidence when disclosure is permitted.
  • Build stronger backend foundations around testing, observability, databases, and API design.

Contact

Building practical AI systems, one verifiable change at a time.

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