Learned traditional methods of generating synthetic data through statistical techniques such as ARIMA.

Ran experiments on the TimeGAN (Tensorflow 2.0) and Copulas methods in Vertex AI (GCP).

Applied statistical learning schemes on deep learning models to capture time-series trend/seasonality.

Created a user interface to easily generate synthetic data based on real data or a desired distribution.

Presented results of synthetic time-series data generation algorithms in a presentation fair.

Created a pipeline which supplied sampled images to ProgressiveGAN or StyleGAN algorithms.

Ran various experiments on remote GPUs in a bash environment.

Developed Python scripts and notebooks that used analysis tools such as PCA to create GIFs and figure which visualized the latent space of generative models of 3D space.

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.

Developed a feedforward Deep Equilibrium Model in PyTorch (Python 3).

Compared the performance of the developed Deep Equilibrium model with Securiti.ai's own model and a state-of-the-art Transformer by performing experiments in Amazon's SageMaker services.