Incoming @ Stanford. Searching for the patterns that connect society, technology, and the observable world.
The stars have always asked humanity the same questions. Our answers have evolved — from philosophy to mathematics — but the wonder that drives them remains unchanged. There are endless "constellations" around us that shape the way we understand intelligence, nature, and our place in the world.
Beneath the same sky that inspired philosophers and scientists, I grew up in Iowa, where my mind wandered under vast horizons and fields. Some landscapes teach you how to look farther than the horizon itself.
☆ Age 10: engineered a computer with my dad.
☆ Age 11: earned a Harvard CS certification.
☆ Middle school: built and launched H-motor model rockets in fields; experimented with robotics and DL.
→ Designed a photocatalytic emissions system
→ Conducted topological DL research on Earth systems
→ Forecasted global methane emissions using advanced satellite retrievals
☆ Graduated from West High one month ago.
☆ Studying Symbolic Systems @ Stanford, combining CS and philosophy.
Streamlining innovation to better understand and harness the natural world surrounding us.
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Explore my research publications and ongoing projects below.
Developed a topology-driven atmospheric analysis framework that applies the Poincaré-Hopf theorem and Hairy Ball Theorem to detect stagnation structures, accumulation hotspots, and generalize transport dynamics across planetary atmospheres.
TopoFlow is a computational topology framework that applies the Poincaré-Hopf theorem and Hairy Ball Theorem to identify and classify atmospheric stagnation points from global wind fields. By combining sub-grid root-finding algorithms with high-resolution ERA5 reanalysis and Sentinel-5P methane observations, it reveals persistent flow structures that suppress atmospheric transport and drive methane accumulation. The framework validates topological conservation in real atmospheric data, quantifies the relationship between stagnation dynamics and greenhouse gas hotspots, and provides a novel mathematical approach to atmospheric transport analysis. Because the methodology depends only on the topology of flow on a spherical manifold, it naturally extends to the study of planetary atmospheres beyond Earth.
↓ RESEARCH ABSTRACT · PDF

Engineered a quantum-enhanced atmospheric intelligence platform that couples spatiotemporal methane forecasting with sonophotoelectrochemical oxidation to identify emission hotspots and transform methane into renewable energy products.
SPECO is a dual-stage methane forecasting and mitigation platform that combines quantum ML with advanced catalytic oxidation. The system employs a hybrid Quantum Long Short-Term Memory (QLSTM) neural network trained on multi-source satellite, atmospheric, and emissions datasets to predict methane hotspot formation with significantly improved accuracy over classical deep learning models. These forecasts are coupled with a sonophotoelectrochemical oxidation (SPECO) reactor, optimized through computational fluid dynamics, molecular dynamics, and vibroacoustic simulations, to enhance methane oxidation via ultrasonic cavitation. Together, the computational and experimental framework enables both proactive methane monitoring and catalytic conversion of atmospheric methane into energy feedstocks.
Developed a computationally-optimized, multi-stage catalytic emissions system that integrates nanomaterial engineering, photoelectrochemical oxidation, and multiphysics simulation to reduce particulate matter, carbon monoxide, and carbon dioxide emissions from internal combustion engines.
Pura Aerem is a modular, multi-stage extension to standard three-way catalytic converters designed to enhance the removal of particulate matter and greenhouse gas emissions from internal combustion engines. The system combines diffusion-interception particulate capture, photoelectrochemical oxidation for carbon monoxide degradation, and C60 buckminsterfullerene nanomaterial for carbon dioxide encapsulation, with each stage optimized through molecular dynamics, computational fluid dynamics, and AI image segmentation analysis of scanning electron microscopy data. Computational modeling informed the design of a manufacturable 3D-printed prototype, enabling optimization of airflow, filtration efficiency, and structural performance before physical validation. Experimental testing demonstrated near-complete removal of particulate matter and carbon monoxide alongside substantial carbon dioxide reduction, highlighting the potential of computationally engineered emissions control technologies to improve air quality.

Designing the social app for curious grocery eaters. Chia is a taste-matching platform where food obsessives rank local products, build lists, and discover what they'll love based on whose taste matches theirs.
Founded STEM for Youth, a nonprofit that develops and delivers scalable, hands-on STEM education programs in partnership with schools, libraries, and community organizations to expand access to science and technology education.
I'm interested in building planetary intelligence — developing technologies that can model, optimize, and ultimately operationalize Earth's atmospheric and energy systems, while laying the foundations for resilient civilizations beyond our planet.
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Awards and recognition are not endpoints. They are positions along an orbit that constantly stay in motion.
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Open a drawer, or search the whole archive. Each entry carries a note in the margin.
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