{
"name": "Daniel Hu",
"occupation": "Software Engineer",
"likes": ["Math", "Coding", "Badminton"],
}
About Me
I’m a Computer Science student at the University of Waterloo, passionate about problem-solving and applying computational concepts to real-world challenges. Having completed the IB Program and driven by self-learning, I’ve expanded my expertise through Stanford University & DeepLearning.AI's Algorithms, Machine Learning, and Deep Learning Specializations. I'm passionate and versatile, leveraging technologies like Java, Python, C, C++, SQL, React, and TypeScript to build impactful and efficient solutions.
Currently, I’m a Software Engineer Intern at Imperial Capital, building algorithms and tools to improve the valuation of portfolio companies. Previously, as a Software Engineer Intern at Tramona, I developed web scrapers and full-stack platform features. On campus, I lead and contribute to the CS Club and Data Science Club, working on AI apps and judging hackathons, while honing my technical and leadership skills.
Outside of work, I’m a national badminton player, with an all-time highest junior ranking of fourth in Canada. The sport has instilled in me resilience, discipline, and teamwork. I also promote inclusivity through Bridge to Badminton. Balancing my passions for math, software engineering, data science, and sports, I’m dedicated to making a positive impact in the world and exploring new ways to innovate.
Experience
Research Assistant
Applying meta-analytics to quantify discrimination, stability, and independence across seasons, validating which volleyball metrics best reflect true player ability.
Implementing systematic Python-based research pipelines for metric modelling and validation, integrating literature synthesis directly into methodological and writing decisions.
September 2025 - Present
Data Scientist Intern
Waterloo, Ontario, Canada
Toronto, Ontario, Canada
Software Engineer Intern
Software Engineer Intern
University of Waterloo
Seattle, Washington, United States
July 2024 - August 2024
September 2024 - December 2024
May 2025 - August 2025
ESDC
Tramona
GE Vernova
Performed multivariate regression in Python and R to identify bottlenecks in EI approvals, improving outcomes for over 5000 daily applicants and informing corrective action.
Built 25+ Power BI features processing more than 40 million data points, and collaborated with 20+ stakeholders to reduce Azure data validation time by 77% in Agile sprints.
Contributed to full-stack development using Next.js, tRPC, Drizzle ORM, and Tailwind CSS, focusing on scalability while maintaining a large codebase with over 1000 files.
Implemented a dynamic scraper to automate data retrieval for client listings and automated email systems to enable real-time reminders for thousands of users.
Automated data managing for Bills of Materials using VBA, reducing execution time from 5 hours to 5 minutes and eliminating previous manual approach errors.
Developed Smartsheet automation scripts for factory-wide data management and proposed optimization ideas for machine operations based on data analysis.
Hangzhou, Zhejiang, China
Software Engineer Intern
Imperial Capital Limited
January 2026 - Present
Toronto, Ontario, Canada
Creating routing and screening algorithms that help the deals team find and evaluate new acquisition targets, distilling complex inputs into clear, repeatable signals.
Shipping full-stack features and maintaining infrastructure using Docker, Terraform, and AWS services, enabling smooth deployments and improving internal efficiency.
AI Engineer Intern
Outlier
May 2024 - August 2024
San Francisco, California, United States
Utilized Java, Python, C, JavaScript, and TypeScript to develop, test, and fine-tune scalable AI model training pipelines, focusing on efficient and robust architecture.
Conducted performance evaluations, troubleshooting issues for deployment, and maintaining documentation to ensure model reproducibility across 10+ models.
Education
University of Waterloo
St. Robert Catholic High School
Stanford University, DeepLearning.AI
Bachelor of Computer Science, Double Major in Statistics
Ontario Secondary School Diploma, International Baccalaureate Diploma
Expected April 2028
Completed December 2024
Graduated June 2024
Projects
Backdrop Zap
I developed Backdrop Zap, an autoscaling SaaS that removes image backgrounds and generates clean, high-quality cutouts. I designed the full-stack system with a Java Spring Boot backend and OpenFeign-based service communication. The platform integrates Clerk for authentication and MySQL for persistence. Designed around scalable API workflows, Backdrop Zap delivers fast processing and a smooth user experience.
I engineered a high-performance object detection algorithm for autonomous vehicles, achieving a 0.99 confidence score. I tested convolutional neural networks and transfer learning approaches before selecting a YOLO pipeline using the Darknet framework for the best speed–accuracy tradeoff. Leveraging TensorFlow, Keras, and PyTorch, the model identifies and classifies key road objects such as vehicles, pedestrians, and traffic elements to support safer, more efficient navigation.
Object Detection Algorithm
Formula 1 Strategy Master
ShopAR
I built F1 Strategy Master, a dynamic application that allows users to explore historical Formula 1 race data and simulate race strategies. Users can customize tire choices, stint lengths, weather conditions, and tracks to generate optimal strategies. The application adapts to the number of pit stops, providing tailored options for each stint. Built using Python, Node.js, JavaScript, EJS, CSS, and PostgreSQL, the application leverages the random forest classifier machine learning model to predict finishing positions, bringing the thrill of real-time strategy decision-making to life.
I created ShopAR, an end-to-end AR commerce demo that brings Shopify product listings to Snapchat Spectacles for interactive “try-before-you-buy” visualization. Using the Shopify Storefront API, I fetch product metadata and dimensions, then generate 3D assets by converting 2D renders into models through a diffusion-based Meshy pipeline and scaling them to real size. I import models into Lens Studio for GLSL texturing plus physics and cloth simulation, then deploy to Spectacles. A Chrome extension tracks pipeline status and progress.
Marketwire
I built Marketwire, a production-grade, end-to-end platform to simplify smart investing. Marketwire analyzes news-driven sentiment in real time to send instant alerts, advising users when to buy or sell for maximum profit. With user consent, transactions can be automated for optimal execution. Combining personalized real-time trends, stock insights, and company research, Marketwire keeps users ahead of market dynamics. Built with Python’s FastAPI, Beautiful Soup for web scraping, the FinBERT NLP sentiment classifier, Supabase, Redis, Celery, and a Next.js frontend using React, TypeScript, and Tailwind CSS.
Sleep Trigger
I developed Sleep Trigger, a privacy-first iOS and watchOS system that detects sleep onset on-device and triggers user-defined iOS shortcuts in real time. The app processes Apple Watch heart rate and motion data locally using a lightweight sensor pipeline and a high-performance DSP core implemented with Swift, C/C++, Objective-C++, ARM NEON, and Metal. I implemented robust cross-device synchronization via WatchConnectivity and iCloud KVS, with history, widgets, and a macOS menu-bar companion to ensure low latency, efficiency, and zero cloud dependency.
Technical Skills
Jupyter
C/C++
JavaScript/Node.js
HTML/CSS
TensorFlow/PyTorch
Svelte/TypeScript
Terraform
Python
SQL
Java
Docker
React/Next.js
Git/GitHub
AWS/Azure
Power BI
