I'm a sophomore at American High School exploring how machine learning can be used to study scientific problems and build real-world AI systems.
My projects span astrophysics, space weather, remote sensing, time-series forecasting, and AI-powered automation. I enjoy working with real datasets, building end-to-end ML pipelines, and going beyond model accuracy to investigate interpretability, robustness, uncertainty, and the scientific meaning behind predictions.
I use machine learning to investigate problems in astronomy, space science, and Earth observation.
stellar-scope Exploring stellar and exoplanetary data through computational analysis and machine learning.
geomagnetic-storm-forecasting-ml A physics-informed LSTM time-series model for forecasting geomagnetic storms from solar-wind data. The project combines temporal deep learning with space-weather feature engineering.
exoplanet-classification-ml A scientific ML study investigating whether host-star properties can predict gas-giant classification in observed exoplanets, with interpretability, uncertainty, and robustness analysis.
satellite-landcover-knn A K-Nearest Neighbors model for classifying EuroSAT satellite imagery into land-cover categories, exploring classical ML for remote sensing and computer vision.
I also build AI systems that connect machine-learning models and language models to practical workflows.
vapi-appointment-reminders A voice-AI automation project using Vapi to build automated appointment reminder and calling workflows.
I'm interested in the engineering side of AI as well—from model and API integration to automation, data pipelines, and usable interfaces.
I like projects that go beyond simply training a model.
My work often involves:
- Real-world scientific datasets
- Data cleaning and preprocessing
- Feature engineering based on domain knowledge
- Classical and deep learning approaches
- Time-series modeling
- Model evaluation and robustness testing
- Explainable ML and SHAP
- Uncertainty analysis
- Data visualization
- Reproducible computational workflows
I'm particularly interested in the question:
Can machine learning help us understand something about the real world—not just predict it?
| Area | Technologies |
|---|---|
| Languages | Python, JavaScript, HTML/CSS |
| Machine Learning | Scikit-learn, XGBoost, LSTM, KNN, Random Forest |
| Scientific Computing | NumPy, Pandas, SciPy |
| ML Analysis | Feature Engineering, SHAP, Model Evaluation, Uncertainty & Robustness Analysis |
| Visualization | Matplotlib, Seaborn, Plotly |
| AI / Automation | Vapi, LLM APIs, Voice AI |
| Applications | Streamlit, React, Firebase |
| Tools | Git, GitHub, Jupyter, VS Code, Google Colab |
- Computational Astrophysics
- Exoplanet & Stellar Science
- Space Weather
- Geomagnetic Storm Forecasting
- Remote Sensing & Earth Observation
- Scientific Machine Learning
- Time-Series Modeling
- Explainable & Physics-Informed ML
- AI Systems & Automation
| Project | Focus |
|---|---|
| stellar-scope | Stellar & exoplanet computational analysis |
| geomagnetic-storm-forecasting-ml | Physics-informed LSTM space-weather forecasting |
| exoplanet-classification-ml | Scientific ML + astrophysics + interpretability |
| satellite-landcover-knn | KNN + satellite imagery + remote sensing |
| vapi-appointment-reminders | Voice AI + workflow automation |
I'm interested in connecting with students, researchers, and developers working in machine learning, computational science, astronomy, space science, and AI systems.
Explore my repositories to see what I'm building at the intersection of science, machine learning, and AI engineering.