•Learned probabilistic and statistical techniques such as Markov Processes, principal component analysis, and time lagged component analysis and their implementations in Python 3 (Scikit-Learn, Numpy).
•Learned Latent NeuralODEs, autoencoder, and variational autoencoders (PyTorch).
•Learned and implemented (Python 3) the numerical method known as the Nudged Elastic Band (NEB) method which helped find minimum energy paths.
•Developed multiple NeuralODE based models with PyTorch (Python 3, conda environment) for the problem of finding minimum energy paths.
•Ran various experiments on remote GPUs in a bash environment showing that NeuralODE based models which were able to perform better than the traditional NEB method.
Technologies: Scikit-Learn, Numpy, Python, Jupyter, Git, Linux, bash, PyTorch, conda