NumPy

Physics-Informed Neural Network Mathematical Implementation: Developed sophisticated physics equation implementations using NumPy for Physics-Informed Neural Networks at Momentum Technologies, integrating fundamental physics principles directly into neural network loss functions. The challenge involved translating complex chemical engineering equations into efficient NumPy array operations while maintaining mathematical accuracy and computational performance. Successfully implemented custom loss functions that incorporate physics constraints, mass balance equations, and thermodynamic principles using NumPy's mathematical functions, enabling the neural network to respect physical laws while optimizing chemical process parameters.

Financial Modeling Mathematical Engine: Architected a comprehensive financial mathematics engine using NumPy that calculates complex financial forecasts, statistical models, and quantitative analysis for the financial modeling tool. The project required implementing sophisticated mathematical operations including time series analysis, statistical distributions, risk calculations, and portfolio optimization algorithms. Successfully created a robust numerical computing foundation that handles diverse financial calculations, from basic statistical measures to complex derivative pricing models, demonstrating mastery of NumPy's mathematical function library for real-world financial applications.

Autonomous Vehicle Sensor Data Processing System: Designed and implemented a sophisticated sensor data processing system for the autonomous vehicle simulator that combines NumPy matrix operations with Pandas data manipulation. The challenge involved processing real-time sensor data from multiple sources (LiDAR, cameras, GPS) and performing complex matrix transformations for spatial analysis and object detection. Successfully created efficient numerical processing pipelines that handle high-frequency sensor data, coordinate transformations, and real-time mathematical analysis essential for autonomous vehicle navigation and safety systems.

Environmental Data Analysis & Mathematical Modeling: Developed comprehensive environmental data analysis systems at INTERA using NumPy for statistical analysis and mathematical modeling of environmental monitoring data. The projects required implementing complex statistical operations, time series analysis, and mathematical models for regulatory compliance reporting. Successfully created robust numerical analysis workflows that process large environmental datasets, perform statistical validation, and generate mathematical models that meet strict regulatory requirements for environmental consulting applications.