University of Ottawa · ECE
Current work moves through AI systems, sensing pipelines, and hardware-aware engineering rather than staying only at the UI or product layer.
Kair Wang · software + AI systems engineer · Ottawa
Kair Wang
I build software where machine learning, sensor data, and real hardware meet.
M.A.Sc. ECE at the University of Ottawa, with 4+ years shipping full-stack product systems before that. Focus: AI & recommendation systems, sensing pipelines, and the operator-facing software that makes them work in production.
Research log
The throughline is not “product thinking.” It is systems work moving closer to the metal.
This replaces the usual slogan section. The useful story is the actual track: software engineering, ECE graduate work, IEEE papers, and deeper semiconductor-facing curiosity.
AI water resource systems
The research thread is about tying data collection, modeling, and decision support into one system that can survive real operational use.
Research that stays accountable to reality
The second IEEE paper reinforced the same standard: the interesting part is not a model in isolation, but whether the full loop holds up when someone has to use it.
Silicon-based III-V, heterogeneous integration, lower-layer constraints
That longer arc matters because it points toward the part of engineering where semiconductor realities start shaping software decisions.
Selected work
Three things I'd want a recruiter, professor, or peer to read first.
- RESEARCH Python · XGBoost · LLM · TF-IDF · SMOTE
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SYSTEM
kAir · chat-first iOS super-app Solo · on-device ML + tool routing · 2026-04 → present View on GitHubSwift · SwiftUI · MLX-LM · LoRA · Core ML
- TOOL Python · Ultralytics YOLO11 · OpenCV · PyTorch
Featured system
Revo, stripped down to the moving parts.
I still keep one shipped product in the foreground, but the page shows its request flow, state handling, and admin control surface instead of retail copy.
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Problem
Recommerce intake was unstructured: customers described devices in free text, valuation took manual review on every order, and the operator team had no audit trail when an exception slipped through.
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System
Guided intake → rules + condition handling → state machine for checkout/payment/tracking → operator console with review flags. One pipeline, four cooperating layers, full request-to-fulfillment traceability.
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Result
Most quotes resolve instantly; only edge cases reach a human, and every state transition is logged for the operator. The same data shape powers reporting and dispute resolution.
Shipped product, read as architecture
Same data shape powers checkout, fulfillment, and dispute resolution.
The public storefront is only the surface. The hard engineering work sits in request shaping, valuation logic, state transitions, and the operator-facing layer that cleans up edge cases.
- Why keep it on the site
- Because it proves I can ship full-stack product systems, not only research-facing work.
- What matters technically
- Request shaping, valuation logic, state transitions, admin traceability, and the handoff between automation and human review.
Representative request
200 OK · sample
POST /api/trade-in/quote{ "device_model": "iPhone 14 Pro", "condition": "screen_minor_wear", "storage_gb": 256, "region": "CA-ON"}200 OK{ "quote_id": "qt_1840", "valuation_band": [410, 465], "review_required": true, "next_step": "checkout"}
Quote intake
A guided intake turns messy device details into a structured request before payment ever starts.
Valuation engine
Rules, condition handling, and review flags decide whether the result can be priced instantly or needs human review.
Order state
Checkout, payment, tracking, and after-order updates are treated as one stateful system rather than disconnected pages.
Admin review
The hard part is the operator-facing layer: exceptions, fulfillment, and traceability after the happy path ends.
Core toolkit
The stack is layered by how deep it runs, not by how nice it looks on a resume.
That is the visual difference between a generic full-stack profile and someone moving across research, software systems, and hardware context.
Core depth
Research / system substrate
- AI systems and model-to-interface thinking
- Sensing, telemetry, and monitoring pipelines
- Hardware-facing software and embedded-adjacent work
- Semiconductor reading track: silicon-based III-V and heterogeneous integration
Applied systems
ML, data, and operational software
- Python for ML and data-facing systems
- IoT dashboards, reporting, and operator surfaces
- SQL and decision-support data models
- MQTT, automation, and service integration
Delivery layer
Shipped product surfaces
- JavaScript and React interfaces
- PHP services and internal tooling
- Admin workflows, dashboards, and customer-facing flows
- LLM integrations when they solve a real interface problem
Field notes
I only publish notes when a system taught something worth keeping.
笔记｜游戏运营方向课程总结
TL;DR： 这门课最大的收获不是学到了几个公式，而是从技术视角转向运营视角——游戏能不能长期活下去，靠的不是单点功能，而是产品、用户、市场、商业化、数据之间的持续配合。花了一天时间，终于上完了课程，以下是我的总结笔记。这次的游戏运营方向的课对我来说收获很大，接下来会花很多的时间对大作业进行完成以及打磨，也希望能和你们...
Woohoo!! 我正式出生了！！
记录一下网站上线的第一条博客内容！！以后会网站会上线一些黑科技，如果想要看到什么内容可以在下方留言！！！
Contact
For research, systems roles, or hard technical conversations, email works fastest.
A short note about the role, problem space, or system is enough.
Set in Inter Tight & SF Mono. Composed by hand in Ottawa, ON. © 2026 Kair Wang · technical archive
