Nanotechnology Engineering
University of Waterloo
AIreasoning · learningMaterialsstructure · discoveryMarketsinformation · incentivesSystemsarchitecture · controlMove to perturb the system
01 / APPROACH
From first principles to real-world systems.
My work moves between scientific research and consequential deployment: reducing complex problems to their fundamentals, building rigorous models, then engineering systems that create durable value.
02 / RESEARCH
Selected research
Machine learning and data science applied to materials discovery, semiconductors, and complex physical systems.
20222023OPEN
DARWIN
Interpretable discovery of semiconductors with machine learning · Nature Computational Materials
Accelerating Semiconductor R&D
Turning fragmented scientific workflows into connected decision systems · Palantir
Luminescence Profile Generator
Ideal luminescence curve generation for optical materials research · GitHub
03 / ENGINEERING
Engineering complex systems
The work is not finished at the model. It moves from problem definition through architecture, implementation, evaluation, and iteration.
?Problem
△Model
⌘Build
↻Learn
04 / SELECTED WORK
Systems in practice
At Palantir, I work where research, software, and consequential operations meet—turning complex data into systems that people can trust and use.
View the semiconductor whitepaper ↗Data selected · Secure semiconductor data ecosystem
05 / LEADERSHIP
Technical depth. Organizational clarity.
Strong technical leadership connects rigorous thinking to the people, incentives, and operating systems required to make change last.
- Think in systems
- Empower people closest to the problem
- Align incentives with outcomes
- Build for long-term value
06 / FOUNDATION
Academic foundation
Electrical & Computer Engineering
University of Toronto
Applied AI & Quantitative Systems
Research translated into real-world deployment.
The most interesting problems are still unsolved.
Let’s build what’s next.