Earthquake Dashboard
plannedUSGS live data feeding an interactive map of recent seismic activity.
USGSmaps
Available for new-grad software roles
full-stack engineer · ml for earth systems
Building software at the edge of code and the planet
I'm a recent computer science graduate with an earth science background, exploring physics-informed neural networks (PINNs) and the place where rigorous software meets the systems that shape our planet.
- 4+
- shipped & systems projects
- 6
- person team led (Plotline)
- 2026
- B.S. Computer Science
About
I'm a recent computer science graduate with hands-on experience across full-stack development, relational database design, and AI prototyping. I enjoy building things end to end — from schema and API to the interface people actually use.
An earth science minor pulled me toward physics-informed machine learning and geophysical modeling. I'm now working toward physics-informed neural networks (PINNs) that combine numerical methods with deep learning to model real-world earth systems.
Deep dives
Two long-form, interactive write-ups that go well beyond a project card — built in the visual language of each subject.
Software engineering
Engineering terminal
An interactive terminal walking through Plotline, a multi-org academic database, Kinoko, and a RAG prototype — with roles, stacks, and delivery signals.
4 projects · interactiveExplore
ResearchQuantum computing journal
A hand-drawn technical journal on quantum error correction, complete with circuit diagrams, a syndrome table, and a live QEC simulation.
journal · live simulationExplore
Projects
A mix of shipped work, systems projects, and an earth-science research track I'm actively building toward.
01
Full-stack & web apps
04
Earth science × CS — building toward PINNs
Climate/Weather Trend Analyzer
plannedNOAA datasets analyzed for regional comparisons and long-term trend visualization.
NOAAanalysis
Numerical PDE Simulation
plannedA finite-difference solver for the heat and wave equations as a numerical baseline.
finite-differencePDE
Groundwater/Soil ML Forecast
plannedForecasting using NASA SMAP or USGS groundwater data to predict soil moisture trends.
NASA SMAPforecasting
PINN Capstone
flagship · not startedA physics-informed neural network solving a geophysical PDE, benchmarked against the numerical solver.
PINNsgeophysics
Skills
Color-coded by area — languages, cloud & databases, and AI/ML.
Languages
JavaPythonJavaScriptSQLC++
Cloud & Databases
MySQLAWSLinuxGit
AI / ML
RAGembeddingsPINNs
Notes
Notes on PINNs
Working notes documenting the journey toward physics-informed neural networks. More coming soon.
What is a physics-informed neural network?
coming soonFrom finite-difference solvers to learned PDEs
coming soonBenchmarking a PINN against a numerical baseline
coming soonContact
Get in touch
Open to new-grad software roles and research collaborations at the intersection of ML and earth systems.