Incoming Software Engineer Intern (Summer 2026) at Cloudflare, where I will be working with the UI Platform Team to build scalable React and TypeScript frontend infrastructure for the Cloudflare Dashboard, developing platform systems that integrate backend services, APIs, and data pipelines, and contributing to reusable component libraries, Redux state management, and enterprise-grade software powering millions of users.

Constructed a scalable, high-throughput ML inference pipeline for wildlife monitoring across 4000+ hours of ARU field recordings, integrating CNNs, BiLSTMs, ResNet18, and PANNs with attention-based fusion on multi-resolution spectrograms for temporal-spectral feature modeling, achieving 94% accuracy at 81.5% recall for automated gunshot detection.

Systematized model evaluation and experiment management by automating 30+ training and inference runs using PyTorch and Hydra configuration management, reducing evaluation time by 85% and lowering false positives by 70% through rigorous ablation studies and architecture comparisons enabling deployment-ready performance.

Designed end-to-end data preprocessing workflows including noise filtering, spectrogram generation, class balancing with SMOTE, and multi-resolution feature extraction, building reproducible training scripts with comprehensive validation dashboards tracking precision, recall, F1, ROC curves, and domain-specific wildlife detection metrics.

Containerized the full ML pipeline with Docker for deployment on HPC clusters and cloud environments, improving reproducibility and enabling cross-institutional collaboration between UC Davis and Louisiana State University researchers on bioacoustics and conservation analytics.

(Federally Funded Research Collaboration)

(Advisor: Prof. Kevin Ringelman (Wildlife, Fish, and Conservation Biology Department of UC Davis))

(Co-Advisor: Prof. McKenzie Fowler (School of Renewable Natural Resources, College of Agriculture, Louisiana State University, LSU))

(Lab: Avian Ecology Lab)

Engineered Charitap, a production-grade blockchain-based micro-donation platform at UC Davis ExpoLab under Prof. Mohammad Sadoghi. Solved donor fatigue and opacity via an automated decentralized transaction round-up system and user-driven charity nominations with admin approvals.

Architected a microservices AWS EC2 infrastructure with GitHub Actions CI/CD and Vercel frontend deployment. Utilized Terraform IaC to provision AWS networking and security. Containerized the stack using Docker and an Nginx reverse proxy, integrating Kubernetes manifests for future auto-scaling. Tracked system health using Prom-Client, Prometheus, and custom Grafana dashboards to monitor API latencies and Stripe success rates.

Built a NodeJS and ExpressJS backend with MongoDB for relational data and Redis caching to optimize database load. Integrated Apache Kafka event streaming to buffer real-time transaction data and ensure zero data loss. Managed recurring donations using Stripe Connect and Node-Cron for automated threshold and monthly fiat roll-ups. Secured the platform with Google OAuth, JWT, bcryptjs, and middleware like helmet, cors, rate-limit, express-mongo-sanitize, and xss-clean.

Designed a React dashboard with Tailwind CSS and a custom Chrome Extension. The extension securely captures real-time round-ups and pushes events to Kafka. Built an interactive UI with ChartJS, framer-motion, and react-countup. Ensured reliability using Jest for unit testing and Playwright for E2E user flow simulation. Integrated Stripe React SDK for secure client-side payment tokenization and user preference management.

Pioneered Apache ResilientDB integration to guarantee immutable transaction transparency. Deployed custom Solidity smart contracts via a WSL sub-process and ResContract to execute various functions. Leveraged a GraphQL API to hash sensitive data and log receipts onto the ResilientDB key-value mainnet, generating cryptographic hashes for every micro-donation.

Served as Teaching Assistant for ECS 130: Scientific Computation under Prof. Zhaojun Bai, mentoring 70+ students through numerical analysis and computational methods by leading weekly discussion sections with live coding demonstrations on linear algebra algorithms, iterative methods, sparse matrix computations, and MATLAB/Python implementations while holding regular office hours to provide personalized debugging assistance and academic support.

Managed course infrastructure across Piazza, Gradescope, and Canvas platforms, monitoring Q&A forums for timely student support, facilitating peer-to-peer learning, and maintaining consistent communication channels while evaluating student performance by grading midterms, final projects, and weekly assignments with detailed rubrics and constructive feedback.

Developed strong communication, mentorship, and organizational skills by adapting teaching approaches to diverse learning styles, fostering an inclusive collaborative environment, coordinating with course staff on curriculum delivery, and maintaining accurate grade records with Excel and Google Sheets to ensure assessment consistency and academic integrity.

Engineered CATF, a production-grade Python ML framework for context-aware multivariate time-series forecasting, implementing a manager–worker architecture with modular system design, unified ETL pipelines, and end-to-end workflows spanning feature engineering, model training, hyperparameter optimization, validation, and deployment across seven attention- and optimal-transport-based models.

Designed and implemented MLOps infrastructure including direct CSV-to-tensor ingestion, lazy loading for GPU memory optimization, automated model validation workflows, Optuna-based hyperparameter tuning, multi-dataset CLI support, and CI/CD-ready pipelines to enable reproducible experimentation and scalable model development.

Achieved state-of-the-art performance on 5 of 7 benchmark datasets, improving 26 of 35 experimental runs with CATF-TimesNet and CATF-iTransformer reducing MSE by 49.3% and 32.8% respectively through systematic architecture optimization, performance benchmarking, and data transformation efficiency improvements that reduced computation overhead by 9%.

Submitted research findings to ACM SIGKDD 2026 Research Track (flagship knowledge discovery and data mining conference), documenting novel context-aware prediction methods and architectural innovations for production ML systems.

(Advisor: Prof. Dongyu Liu)

(Team Lead: Yueqiao Chen, PhD)

(Lab: Visualization and Intelligence Augmentation (VIA) Lab, Computer Science Department of UC Davis)