I study artificial intelligence and I've worked in real estate since I was eighteen. For a while those were just two separate things. Lately, they've started to become one.
My current research sits at the intersection of machine learning and real estate — an area I find particularly compelling because it combines rigorous quantitative methods with a domain I understand from the inside out.
Real estate data is messy, spatially dependent, and deeply shaped by human behaviour. That makes it an interesting problem. I'm working with a supervisor on a specific direction I'm not ready to detail publicly yet — but the short version is: there's a lot the market hasn't priced in that a well-trained model can see.
This research is the project I'm most excited about right now.
Most people my age were figuring out university. I was already working the public-facing side of real estate with a RECO license. That experience — reading markets, understanding clients, sitting across from people making the biggest financial decisions of their lives — informs the way I think about the research I'm doing now.
The data is only interesting if you understand what it's measuring.
Focused on machine learning methods and their application to real-world domains. Active research with academic supervision.
Licensed to practice public-facing real estate in Ontario since turning eighteen. A grounding in markets, negotiation, and the human side of property.
Competed as catcher through high school, a role built on reading situations, managing tempo, and keeping a quiet eye on everything.
Simplicity is the ultimate sophistication.— Leonardo da Vinci