I'm a fourth-year PhD student in the Department of Statistics & Data Science at UCLA, co-advised by Professors Ying Nian Wu and Shantanu H. Joshi. My research focuses on developing and applying generative models, representation learning techniques, and statistical inference methods across diverse scientific domains including medical imaging (fMRI, DTI), astrophysics (galaxy morphology modeling), and robotics (action-conditioned sequence models).

I work at the intersection of machine learning, high-performance computing, and applied science. My recent projects involve:

* Generative modeling using diffusion models, variational inference, and transformer-based architectures.

* Efficient training and inference, including quantized models (int8, binary), LoRA tuning, and multi-GPU deployment.

* Scientific applications of deep learning to neuroimaging, medical time-series alignment, and redshift-conditioned galaxy synthesis.

* Systems-level design, including CUDA kernels, PyTorch internals, and model compression techniques.