Hello! I'm Gassan, a driven Data Scientist currently pursuing a Master's in Data Science. Passionate about data science and machine learning, my background in physics and research has fueled my specialization in advanced machine learning models.
Throughout my academic journey from the University of Massachusetts Amherst to Harvard, I've consistently engaged in projects that emphasize data-driven decision-making and statistical analysis. My work includes developing predictive models for dynamic systems, creating intuitive visualizations for complex data, and applying machine learning techniques to solve real-world challenges.
Professionally, I served as an AI Research Engineer at AIMdyn Inc., where I built Koopman-based machine learning models from scratch to production for autonomous system learning inputs, enhancing model accuracy and efficiency. I actively participate in data science competitions on Kaggle and run a YouTube channel, 'Math & Physics Fun with Gus', where I share insights on complex mathematical theories and data science concepts.
-
NeuralForecast | Nixtla NeuralForecast - Features comprehensive neural forecasting models including NBEATS, enhancing stock market analysis, prediction accuracy, and model training time.
-
High-Accuracy Brain Tumor Classification using CNN - A CNN developed with TensorFlow 2.16 and GPU acceleration achieves a 99.7% accuracy rate in classifying brain tumors, incorporating advanced techniques like data augmentation and adaptive learning rate adjustments.
-
Stock Forecasting with Multi-Step Stacked LSTM - Utilizes stacked LSTM models for detailed Tesla stock price forecasting, with extensive documentation on LSTM network theory and practical application.
-
Ozone (O3) AQI Trends in High-Impact U.S. Regions - Applies SARIMAX and Holt-Winters models to analyze and predict ground-level ozone fluctuations, contributing to better environmental policy making.
