Scientific software
Designing systems that respect the complexity of scientific data and the people who use it.
Scientific software · cloud platforms · AI/ML
Engineering systems for the work that matters.
I build resilient, practical software for complex scientific work—from cloud-native foundations to the models and workflows that make research move.
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About
Building dependable paths from science to software.
At Schrödinger, I work at the intersection of scientific computing and production engineering. My focus is clear: give scientists and teams robust tools that make complicated work feel more direct, connected, and useful.
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Expertise
The tools change. The responsibility doesn’t.
Cloud-native platforms
Creating reliable foundations that scale with the needs of a team and its work.
Multi-cloud infrastructure
AWS, GCP, Kubernetes, Terraform, CloudFormation, and the practical automation that keeps platforms reliable.
AI/ML systems
Bringing models into useful, durable workflows—where they can earn trust over time.
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Current focus
Complex systems, made human.
I’m drawn to the connective tissue: the platform choices, data flows, and engineering decisions that let research teams spend less time fighting their tools and more time making progress.
Multi-cloud platforms
Building reliable foundations across AWS, GCP, Kubernetes, and the services that support them.
Infrastructure as code
Using Terraform, CloudFormation, and automation to make environments repeatable and easier to operate.
Research-ready AI/ML
Connecting production AI/ML workflows to the dependable data systems research teams need.
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