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About Me:
Hi, I'm Xinyi Li, a passionate software engineer with a strong background in full-stack development and a keen interest in creating innovative solutions that make a positive impact.
I specialize in building scalable web applications using modern technologies like React, Node.js, and cloud platforms. My experience spans across frontend development, backend architecture, and database design.
Currently, I'm exploring the exciting fields of machine learning and artificial intelligence, looking for ways to integrate these technologies into practical applications.
Education:
University of Michigan-Ann Arbor
Ann Arbor, MI
B.S in Computer Science📅 Aug 2023 - Dec 2025📊 GPA: 3.86/4.0
Macalester College
Saint Paul, MN
B.S in Economics📅 Aug 2021 - May 2023📊 GPA: 4.0/4.0
Relevant Courses:
Data Structure & AlgorithmObject-Oriented ProgrammingComputer OrganizationAdvanced RegressionWeb SystemsNatural Language ProcessingMachine LearningDatabase Management Systems
Experience:
U-M Information and Technology Services - Data Integration
DevOps Intern📍 Ann Arbor, MN📅 May 2025 - Dec 2025 (extended)
- •Automated OpenAPI validation using Schemathesis across 42+ APIs, reducing swagger debugging from manual inspection to seconds-long validation and enabling rapid error fixes
- •Built Python-based Apigee and Denodo CI/CD pipelines with GitHub Actions, reducing 5-step deployment workflows to 2 script executions and automating 53 API endpoint deployments with 90%+ time savings
- •Developed Oracle-based API performance monitoring system to analyze response times across multiple partitions, proactively identifying performance issues and generating executive reports for upstream departments
Glimpse Diagnostic LLC
Data Engineer Intern📍 Arden Hills, MN📅 May 2023 - August 2023
- •Built generative adversarial networks model using Tensorflow and PyTorch in AWS Sagemaker to augment synthetic children tympanic membrane images from 726 to 2000 for training abnormal tympanic membrane recognition
- •Optimized quality metrics including Fréchet Inception Distance(FID) derived during model training process to classify images under cross validation to enable an effective diagnostic source for medical practitioners
- •Pre-processed image samples using NumPy and TensorFlow (keras) for image transformation (resizing, color uniforming) and utilized CUDA for GPU acceleration to optimize model training efficiency
Skills & Technologies:
Programming Languages
PythonJavaScriptTypeScriptJavaC++SQLR
Frontend Technologies
React.jsVue.jsNext.jsHTMLCSSTailwind CSSBootstrapSass
Backend & Database
Node.jsExpress.jsDjangoFlaskSpring BootPostgreSQLMongoDBRedis
Tools & Platforms
GitDockerAWSFigmaJiraPostmanVS Code