Third-year CS & Statistics student at the University of Toronto.
Previously an ML Engineer at Whatnot (YC W20) & Shopify.
About me - background, interests, goals
I'm passionate about machine learning engineering and research - particularly in model alignment and physical AI. I'm always looking for opportunties in these areas.
Overall, I am driven by a long term goal of making a large-scale positive impact. This is exactly my criteria when looking for new opportunities and would love to connect if you think we can work together!
Experience
Alongside university, I've had the opportunity to gain 12 months of industry experience in machine learning, backend development, and data engineering through three previous internships.
Machine Learning Engineer Intern @ Whanot
Recommendation systems for Whatnot's livestream feeds.
Applied ML Engineer Intern @ Shopify
ML models for optimizing ad bidding spend.
Data Engineering Intern @ theScore
Worked on real-time activity streaming and data integration systems to power DS/ML work.
Software Technology Intern @ Veolia
Architected and deployed monitoring and alerting infrastructure for ETL pipelines.
Research - Physical AI & ML compilers
Undergraduate Researcher @ Toronto Intelligent Systems Lab
Working with a team of researchers to improve on current methods for cross-embodiment learning from humans to robots.
ML Compilers Researcher @ ParaMathics Lab
Contributed to research on accelerator agnostic compilers. Worked with CUDA, Triton, and MLIR to optimize kernels for A100s.
Featured
Vigil
Security & Observability for AI computer-use agents
Agents that can control a computer introduce new security, compliance, and privacy risks. Vigil provides a runtime layer that watches your agent's actions to block malicious behaviour and shielding your sensitive content.
- Select restricted application
- Select your agents
- Launch them through Vigil and let them run loose
Vigil monitors their activity and blocks any attempt to interact with or access restricted content on the screen.