Toronto · Open to research & opportunities
I build systems that think with evidence.
I’m Kai Zang — a University of Toronto Computer Science student exploring how retrieval, reasoning, and thoughtful product design can turn AI into something people can actually trust.
The best AI doesn’t just sound intelligent.
It knows where its answer came from.
That belief shapes how I build: start with evidence, design for clarity, and make every technical choice serve a human need.
Ask Kai · RAG AI Agent
An agent built to retrieve before it replies.
Most AI demos begin with a model. Mine begins with a question of trust: how can an answer remain useful, private, and grounded in real evidence?
What makes Kai’s AI work more than a chatbot?
It doesn’t rely on a model’s memory alone. It finds relevant evidence, ranks it, and uses that context to produce an answer you can trace.
Grounded by design.
RAG connects every answer to a controlled knowledge base instead of trusting fluent guesses.
Built as a system.
FastAPI, PostgreSQL, retrieval logic, and deployment come together as one end-to-end product.
Made for people.
The goal isn’t AI for its own sake—it is faster access to clear, relevant, verifiable information.
From algorithm
to experience.
I like building across the stack— understanding the algorithm, shaping the product, and carrying it all the way to a usable interface.
Travel,
orchestrated.
One app from inspiration to itinerary.
An in-progress iOS travel concept exploring how destination discovery, AI-assisted itineraries, maps, and trip details can become one calm, connected experience.
Open Travel case study ↗
Your detail
screenshot
worth going.
screenshot here
Teaching a machine to think ahead.
A 10 × 10 Gomoku engine that searches adversarial possibilities efficiently, using alpha–beta pruning and evaluation heuristics to turn strategy into code.
View Gomoku on GitHub ↗
Add your BIT research photo here.
Beijing Institute of Technology
Giving a robotic arm the ability to see.
I explored how image processing and object detection can translate visual input into physical action for a biomimetic robotic arm—an early experience that made the connection between algorithms and the real world tangible.
Built on strong
foundations.
Computer Science Specialist with interests in AI, computer vision, and dependable software systems.
PDF document Open full résumé ↓Education
2025 — 2029
University of Toronto
St. George
Computer Science Specialist
A rigorous foundation across programming, proofs, linear algebra, probability, and software design.
Experience & Leadership
Library & Help-Desk
Helped students and staff solve everyday technology problems—building patience, clarity, and a service mindset.
Student Events
Organized activities and coordinated peers, turning ideas into shared, well-run experiences.
Computer Vision & Robotics
Applied image processing and object detection to a biomimetic robotic arm at Beijing Institute of Technology.
Technical Toolkit
Core skills, built to ship.
A focused stack for turning grounded AI ideas into reliable, thoughtful software.
AI & Data
Grounded intelligence, retrieval, and perception systems built around evidence.
Backend
Clean APIs and dependable services designed for real products, not just demos.
Languages
A practical foundation spanning intelligent systems, product interfaces, and data.
Product Engineering
From interface decisions to payment and API integration, with the user in view.
Infrastructure
The tools and deployment habits that keep projects collaborative and shippable.
Next Frontiers
Expanding toward larger systems and richer visual intelligence.