I'm an aspiring AI/ML Engineer with skills spanning across scientific programming in Python, software development in Java and C++, and a keen interest in the intersections of AI/ML, peformance analysis, robotics, and aerospace.
From January - August 2025, I worked as an AI Intern in the System Performance Architecture group within Hardware Engineering at Apple Inc. My opinions, repos and other content here are not a reflection of my employers, unless otherwise specified or agreed. I am making my contributions/submissions to the projects in my personal capacity and am not conveying any rights to any intellectual property of any third parties.
📐 Shap-E
APPLE: Enabling Shap-E to run on Apple Silicon GPUs via Metal Performance Shaders (MPS) Acceleration
- Overview: Shap-E falls back to CPU on Apple M-series machines because certain indexing ops are not yet supported by PyTorch-MPS. This PR removes that blocker, giving native-GPU performance on macOS without sacrificing CUDA/CPU compatibility.
- Technologies: Python, PyTorch MPS
- Features: Significant performance increase while running Shap-E locally on Mac M-series, for example, on a Mac mini (M4 Pro), default image-to-3D generation time drops from 4 hours to just under 4 minutes when switching from CPU to GPU via MPS.
- 📄 View Pull Request
Using Frequentist and Bayesian Regression Models to Optimize Surprise Gift Strategies
- Overview: Developed a Bayesian Regression Discontinuity Design to evaluate and optimize the return on investment of surprise gift campaigns.
- Technologies: Python, NumPyro, Pandas, Jax, StatsModels, Matplotlib
- Features: Data Visualization & Actionable Insights.
- 📄 View Project README
Predicting Airbnb listing prices in New York City using various regression models.
- Overview: Developed models to predict Airbnb prices, with the Random Forest model showing the best performance.
- Technologies: Python, Jupyter Notebook, Scikit-learn, Pandas, Numpy, Seaborn
- Features: Data preprocessing, model training, evaluation metrics.
- **📄 View Project README
Modern and responsive website for Naacho Dance Company at Princeton.
- Overview: Features event details, member bios, and integrated Google Maps for location information.
- Technologies: HTML, CSS, Bootstrap
- Features: Responsive design, interactive elements, user-friendly interface.
- **📄 View Project README
Implementing CNNs and RNNs to analyze and predict trends in cognitive function.
- Overview: Proof of concept utilizing deep learning models for cognitive trend analysis.
- Technologies: Python, TensorFlow, Keras
- Features: Model implementation, data analysis, prediction visualization.
- **📄 View Project README
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