Priyatham Gangapatnam

Employed Python and SQL to extract and process data from encrypted DAT files. Conducted comprehensive feature engineering and data preprocessing. Utilized scikit-learn and TensorFlow for data training, testing, and cross-validation. Employed MLflow for efficient hyperparameter tuning. Developed software applications for data analysis and visualization using Tkinter in Python. Created web applications leveraging Streamlit and Microsoft Azure. Containerized Python applications using Docker.

- Apply Deep Learning techniques to predict the Advance rate of Bauer Cutters. - Data Preparation for the raw data obtained from the machines. - Data Analysis, Feature Selection and Cross validation.

Predictive Modelling of Tunnel Boring Machine (TBM) data - Data Extraction of Tunnel Boring Machine data in Python. - Principal Component Analysis using Tensorflow - Data clustering using K-means and DBSCAN in RapidMiner - Cross-Validation of the data using machine learning algorithms - Comparative study of Random Forests, Neural Networks and Polynomial Regression

- Conducted Grid Independent studies, Meshing, Simulations and post processing for heat transfer in metal foams. - Carried out Experiments under identical conditions to validate the numerical results in ANSYS FLUENT - Research Paper titled "Numerical Simulation of Heat Transfer in Metal Foams" published in the Heat and Mass Transfer Springer Journal (DOI 10.1007/s00231-017-2149-6)

- Linear Finite Element Analysis - Applied FEM ( Abaqus) - Modern Programming Concepts in Engineering (Java) - Finite Element Analysis

Finite Element Methods Fluid Mechanics Solid Mechanics