I'm currently a researcher at the NerDS Lab at UPenn, where I work on learning transferable representations from large-scale neural datasets across subjects, recording sessions, and modalities. My work involves building PyTorch training and evaluation pipelines, studying cross-domain generalization, and analyzing how learned representations change across contexts and behavioral states.

I'm broadly interested in understanding what neural networks learn internally and how those representations generalize.

Some of the areas I'm currently interested in include:

• Representation learning and self-supervised learning
• Model interpretability and representation analysis
• Cross-domain and zero-shot generalization
• Evaluation of learned representations and model behavior
• Foundation models and reusable computational structure

My recent work has included zero-shot neural decoding, cross-subject representation learning, and analysis of latent representation dynamics.