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Ni is a machine learning engineer at Cisco with an academic background in information engineering. SPECIALITIES: ✅Video Generation Models: VEO-3, seedance, wan, I2V, Stable Video Diffusion; ✅Generative AI Tools: VEO, GANs, Stable Diffusion (SD inpaint, SDXL), Flux, Fine-Tuning (DreamBooth, LoRA); ✅Programming Languages: C++, Python, JavaScript, Java; ✅Quality Control: LLM Evaluation & Benchmarking, Metrics-Driven Validation, AI-Powered QA Tools; ✅Deep Learning Tools & Libraries: PyTorch, TensorFlow, Hugging Face Transformers, ComfyUI; ✅Data Processing: NumPy, Pandas, OpenCV, SQL; ✅Model Training & Deployment: CUDA, Distributed Training, TensorBoard, Data Cleaning, AWS, Azure, Docker; ✅Certifications: AWS Certified Machine Learning – Specialty; ✅Awards: Silver Medal at the Kaggle Competition (Kaggle-LLM Science Exam); ✅Languages: Chinese (Native), English (Full Professional Proficiency, IELTS 6.5); Five years of experience in AI and software development represent a proven track record of delivering innovative AI-powered software applications. Through leading and participating in numerous projects, Ni is adept at leveraging data-driven insights to optimize algorithms and enhance product performance, also developed her expertise in cutting-edge technology solutions. With a mission to solve complex real-world problems and drive business success by pushing the boundaries of AI and machine learning, Ni continues further honing her skillsets to create innovative solutions, and is seeking new opportunities and challenges.

I build machine learning pipelines that actually work in production — not just notebooks that look good in demos. Most of my work revolves around tabular data: gradient boosting (LightGBM, XGBoost, CatBoost), tabular deep learning (TabNet, TabPFN), and causal inference. I've spent a lot of time on the unglamorous but critical parts — feature engineering, handling imbalanced datasets, model calibration, and making sure results are interpretable and reproducible. A few things I've shipped: A cancer risk prediction system for a medical research team (AUC > 0.84), with full feature importance analysis and threshold strategy reports. A 3D reconstruction pipeline based on Structure-from-Motion, robust enough to handle blurry and low-texture inputs. That work led to 2 software copyrights and a patent under review. A causal boosting framework (CBDT) for heterogeneous treatment effect estimation — benchmarked on IHDP, ACIC, and MIMIC-III. Paper currently under submission. Quantitative models for financial risk — credit scoring with XGBoost and TabNet, A/B testing on simulated datasets, ended up improving accuracy by 15% and cutting false positives by 20%. I'm comfortable working with financial data, backtesting pipelines, and building risk models from scratch. I also work with OpenClaw for robotic manipulation tasks and have experience integrating it into simulation and control workflows. On the tools side: Python is my daily driver, along with PyTorch, Transformers, Optuna, and Git. I write clean, documented code — not the kind you need to reverse-engineer to understand. I'm finishing up a B.Eng. in Software Engineering (Beijing University of Technology × UCD joint program, class of 2026). Kaggle Gold and Silver medalist. If any of this sounds relevant to what you're working on, drop me a message. I usually reply within a few hours.

I build production RAG chatbots, AI agents, and LLM-powered backends — currently at AUS Shenzhen AI, open-source maintainer of xiaozhi-esp32-server (ESP32 voice assistant, active community). Daily stack: Python, FastAPI, Claude Code + MCP, Pinecone, LangChain, n8n. 5+ years across Chinese AI startups.

I am a Software Engineer at Bosch (Fortune 500). Previously at the Federal Reserve Bank of St. Louis, where I built RAG AI platforms used by 1000+ internal users, turning messy documents into structured data and insights. I was also a backend engineer at Siemens (Fortune 500), and MSAI at Carnegie Mellon University (number one in AI). At the Fed: • I indexed 1700+ documents and integrated 800k+ economic time series from the FRED and FRASER Database • Implemented hybrid RAG combines vector and keyword search (BM25&Trigrams) with Reciprocal Rank Fusion (RRF), • Designed data extraction pipelines to convert unstructured FOMC PDFs into structured defined schemas for querying, • Built multi-step workflows using LangChain/LangGraph, traceable and auditable actions with guardrails and citations, • Set up MCP server + API integrations, consistent access to structured data from internal systems with audit logs, • Delivered significant efficiency improvement, saving users up to 15 minutes per query. Currently at Bosch: • Developing Tessera, a conversation-driven data extraction system that converts unstructured documents into structured datasets via dynamic schema generation and iterative refinement At Siemens: • I developed Java backend services for a graph visualization platform, integrating with APIs and improving data processing throughput to support real-time visualization needs, matched performance of Neo4j on optimized workflows, • Built automated testing pipelines using GitHub Actions, enabling continuous integration for faster, more reliable releases, • Containerized services using Docker and deployed to AWS EKS, provisioning infrastructure with Terraform to ensure consistent, reproducible deployments At Institute of Automation, Chinese Academy of Sciences: • Deployed a computer vision traffic security system in Python and Pytorch supporting over 100k inferences per day through real-time data streams in a production environment. • Trained RepVGG, Yolo v5 models on 500k+ images for vehicle attribute detection, enhanced multi-task accuracy by 35% PUBLICATIONS Qufei Zhang, Yunshuang Wang, Gengsheng Li, Barry Cardiff, Pasika Ranaweera "Optimizing Federated Learning on Non-IID Data with Clustering and Model Sharing". EuCNC 2025 Jiahui Han, Qufei Zhang, Xiaoying Yang, and Jinyi Wang. "MIAE: A Mobile Application Recommendation Method Based on a Neural Tangent Kernel Model." IEEE BigData 2023 SKILLS Languages: Python, JavaScript, Java ML/AI: LangChain, LangGraph, RAG, RAGAS, Pinecone, OpenSearch, PyTorch DevOps: AWS, Docker, Kubernetes, Apprunner, Terraform, GitHub Actions, Supabase, Vercel Backend & Data: PostgreSQL, Kafka, MongoDB, Redis Frameworks: Flask, Node.js, Next.js, React, Django PS: The video on my page is for an AI lead follow-up app project I used to work on; you can get a sense of what I'm like, feel free to reach out if you wanna chat!

As part of a team, I bring extensive experience in problem-solving and critical thinking to the table. As a certified machine learning engineer and data scientist, I have worked on computer vision and deep learning applications and have been instrumental in creating architectures, training models, and turning data science prototypes into production-ready solutions. Additionally, I have a solid foundation in traditional machine learning approaches, conventional image processing techniques, and statistical analysis. Working with a team, I can contribute my skills in: • Deep Learning • Computer Vision • Image processing with OpenCV and Pillow • TensorFlow 2.x and PyTorch • CAD and Simulations • SolidWorks • Autodesk Fusion 360 • ANSYS • Data analytics • Data analysis • Data visualization using Tableau • Python • R • SQL • Excel • Powe BI • Spreadsheets • Machine learning algorithms • Statistical modeling • Hypothesis testing • Experimental design • Data preprocessing and cleaning • Feature selection and engineering • Model evaluation and selection Furthermore, I am adept at communicating technical concepts to both technical and non-technical audiences and believe in open communication, constructive feedback, and collaboration to achieve the team's goals.

🚀 Looking to integrate AI into your product but don't know where to start? Have you been told that AI can automate workflows, improve customer experience, and unlock new revenue streams—but you're unsure what is actually feasible for your business? Do you have valuable data but lack the expertise to turn it into actionable insights, predictions, or intelligent automation? Are you building a startup and need an engineer who can take your idea from concept to a production-ready web application without hiring an entire team? If any of these sound familiar, I can help. 💡 I specialize in building AI-powered software solutions that solve real business problems—not just research projects that never leave the lab. What I can help you build: ✅ AI Chatbots & LLM Applications - Customer support assistants - AI-powered search and retrieval systems - Workflow automation agents ✅ Machine Learning Solutions - Custom computer vision models - Recommendation systems - Predictive analytics ✅ Full-Stack Websites - React, Next.js, Typescript in frontend - Python, Django, FastAPI backend - MySQL, Postgres, MongoDB for database - End-to-end product development Whether you need to add AI to an existing product, outsource part of your machine learning pipeline, build an MVP for investors, or develop a complete AI-powered platform, I can help turn your idea into a scalable solution. 📞Get a feel for yourself by sending me an invite or scheduling a call with me.📞