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Now (2026)
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Selected Work

Software Version Migration Assistant (RAG) GitHub ↗

Version-aware RAG system for Pydantic 1.x→2.x migration — hybrid retrieval (dense + BM25 + RRF) with version filtering, reaching 0.958 Recall@5 on change retrieval and 0.948 on supporting evidence.

Fraud Risk Modeling & Third-Party Signal Valuation GitHub ↗

Leakage-safe fraud modeling framework with TreeSHAP + temporal holdout evaluation; TabICLv2 hit 0.951 ROC-AUC, used to quantify the marginal value of third-party fraud signals.

About Me

Kiana Shi

M.S. student in Data Analytics Engineering at Northeastern University (Seattle), building on a B.S. in Computer Science & Environmental and Sustainability Sciences from Northeastern (Boston). I've spent my internships engineering LLM-based agents and RAG systems — from multi-agent orchestration layers to retrieval pipelines — and I like turning research-grade AI into things people can actually use.

Skills

Programming

PythonSQLJavaR

AI & LLM Systems

RAGMulti-Agent SystemsAgentScope OpenAI APIFunction Calling

Machine Learning

XGBoostCatBoostScikit-learn TensorFlow/KerasResNet50OptunaSHAP

Retrieval & Data

BM25RRFPandasNumPy PostgreSQLMySQLMilvusRedisChromaDB

Engineering & Visualization

GitpytestStreamlitTableau PlotlyMatplotlib
Now (2026)

Currently an AI Engineer Intern at Wuhan Survey & Design Institute (remote), building a Plan-and-Execute multi-agent orchestration layer — while working through my M.S. in Data Analytics Engineering at Northeastern.

Experience

AI Engineer Intern — Wuhan Survey & Design Institute

Apr 2026 – Sep 2026 · Remote. LLM-based intent router + Plan-and-Execute multi-agent orchestration; pushed routing accuracy from 65% to 90%+ and cut workflow latency 50%.

Software Development Engineer Intern — HireBeat

Apr 2025 – Jul 2025 · Remote. LLM-driven document-to-presentation pipeline with ViT-based layout understanding; improved content/design/structure quality 13–29%+ over baselines.

Software Development Engineer Intern — Microsoft Research Asia

Jun 2023 – Aug 2023 · Beijing. Cross-dataset facial analytics pipeline (FER2013, KDEF) with ResNet50 transfer learning — 82.3% emotion accuracy, 99.0% gender accuracy.

Playlist

Add your build/study playlist here — track names, links, whatever fits your vibe.

Contact

Email: shi.jiaqi1@northeastern.edu

Phone: (857) 654-7075

LinkedIn: linkedin.com/in/jiaqis

GitHub: github.com/KianaShi

Résumé.pdf

Open résumé.pdf →

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