01 / 05Semiconductor R&D
Secure data ecosystem
Designed and implemented a module for secure data sharing, machine-learning workflows, and materials insight.
4-person teamWhitepaper ↗
Scientist / Builder / Applied AI
From scientific complexity to trusted systems.
I design and lead AI systems where research, software, and consequential operations meet, turning complex scientific and industrial data into decisions people can use.
Move to perturb the system
01. Selected Systems
Three systems where scientific rigor became operational infrastructure.
01 / 05
02 / 05Agentic infrastructure
Agentic R&D system
Architected an autonomous code-generation and execution framework used across active internal deployments.
30 deployments300+ users
03 / 05Renewable technology
Root-cause intelligence
Applied LLM-driven reasoning to diagnose failures across complex, multi-stage solar-cell fabrication.
LLM reasoningDevCon ↗
Systems in practice
Built for use, not demonstration.
My work spans the full path from ambiguous technical problem to durable system: shaping the question, structuring the data, designing the intelligence layer, and earning adoption.
04
3 engineers mentored ↗ 05
Official platform capability ↗
Supply chain management
Enterprise transformation
Owned five workflows from a bridge-financed pilot through a two-year enterprise adoption, working across technical and executive stakeholders.
5 workflows3 engineers mentored ↗ 05
Product strategy
Model Studio
Translated customer needs into a reusable modeling capability and carried the product vision through engineering into production.
Production microserviceOfficial platform capability ↗
02 / Research
A path from discovery to practice.
Published research, industry work, and open tools across interpretable machine learning, semiconductors, and optical materials.
Published research
npj Computational Materials
Industry whitepaper
Palantir
Open-source tool
GitHub
npj Computational Materials
DARWIN
Interpretable discovery of semiconductors with machine learning.
Palantir
Accelerating Semiconductor R&D
Connected data and workflows for semiconductor research and development.
GitHub
Luminescence Profile Generator
Ideal luminescence curve generation for optical materials research.
03 / Experience
Technical depth, made operational.
I lead at the intersection of scientific rigor, executive priorities, and the daily work required to move complex systems into practice.
5,000+Research citations
25+Publications
30Deployments
300+Users served
Deployment Strategist / Senior Data Scientist
Palantir Technologies. Applied AI, scientific systems, technical delivery, and product strategy.
Electrical Engineering
University of Toronto. Perovskite nanomaterials and interpretable scientific machine learning.
Nanotechnology Engineering
University of Waterloo. Dean's Honours List.
04 / About
First principles. Real-world consequence.
I move between scientific research and consequential deployment: reducing complex problems to fundamentals, encoding domain insight, then engineering systems that create durable value.
Find the consequential question.
Begin with the operational decision, the constraints around it, and the evidence required to act with confidence.
Encode the domain, not just the data.
Combine scientific models, machine learning, and software architecture so the system reflects how the work actually happens.
Design for trust and adoption.
Measure the system by whether people can interrogate it, rely on it, and improve their decisions with it.
05 / Contact
The most interesting problems are still unsolved.
For conversations about applied AI, scientific R&D, and complex decision systems, LinkedIn is the most direct path.