Satyam Tripathi

Designed and developed an innovative approach to optimize patient follow-up visits in the cataract department. Leveraged advanced data analytics and machine learning methodologies to streamline visit schedules, reducing unnecessary appointments while ensuring patients receive optimal care. Roles included gathering patient and healthcare data from Electronic Medical Records (EMR), employing Python for data preprocessing, feature engineering, and model development, and implementing classification and regression algorithms. Successfully reduced unnecessary patient visits to hospitals by 15%.

Led a project aimed at implementing Transformer networks for text generation, leveraging state-of-the-art deep learning techniques. Implemented the Transformer architecture using PyTorch for text generation tasks, curating and pre-processing a large corpus of text data, specifically utilizing "A Brief History of Time" by Stephen Hawking.

Implemented Convolutional Neural Networks (CNNs) for image classification using a Flower dataset (multi-class). Enhanced model accuracy through hyperparameter optimization and data augmentation techniques, achieving over 95% accuracy.

Implemented LSTM neural networks for stock analysis and prediction on NSE-TATAGLOBAL STOCK, leveraging historical data for forecasting next 30 days' stock prices. Led project, optimizing model performance and validating results for actionable insights.