Chaitanya Gogineni

• Design of hybrid modeling for the prediction of dynamic systems using the combination of Deep learning models and Physics-based models. • Design hyperparameter optimization techniques for various neural networks such as FeedForward NN, LSTM, GRU, CNN-RNN

• Developed automatic Machine learning Regression, Classification and Clustering pipelines to process Bosch specific data. • Developed automatic hyper parameter optimization scripts using keras-tuner, Optuna and Bayesian optimization. • Created python and conda packages for the developed toolkit using PyPi and conda. • Created documentation using Sphinx. Created python and conda packages for the developed toolkit using PyPi and conda. Created documentation using Sphinx.

Experience in the field of software development using Java, Python and software testing using Manual Testing, developing Automation Scripting of test cases using Selenium for various clients including Microsoft and Ericsson.

Focus areas: • Data Science concepts, Machine learning, Natural Language Processing, Preference based Information Retrieval, Big data and Software Process Engineering.