Multi-disciplinary Engineer specializing in the intersection of High-Performance Software Engineering and Data Science.
Expert at bridging the gap between low-level system efficiency and high-level analytics by building robust technical infrastructure.
Re-implemented a carbon-box model incorporating the latest IPCC emission scenarios. Predicted global temperatures (2022–2100) based on varying CO2 levels using Python and SciPy.
Developed a Hybrid Deep Learning model combining LSTM and GRU to predict atmospheric CO2. Achieved accuracy on par with traditional physics-based simulations.
Implemented Active Learning, Self-training, and Co-training from scratch. Evaluated their performance on Sentiment Analysis tasks to determine the most computationally efficient method.
Built a custom library for statistical analysis, including hypothesis testing, frequency distributions, and probability visualization tools. Optimized for performance.
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