Resume

Dissertation on visualizing and interpreting high-dimensional data: theory and applications.

• Mathematical & engineering principles for training foundation models
• Geometric deep learning and manifold learning techniques
• Human-computer interaction and visualization

Fellowship focused on advancing AI safety and alignment research in collaboration with industry leaders.

• Collaborating on an explorable article on LLM interpretability using Sparse Autoencoders (SAEs)
• Developed research direction and initial analyses on novel SAE phenomena like shrinkage and feature splitting

• Developing embedding visualization tools and interpretable techniques for high-dimensional representations
• Used by collaborators for new insights in ICU healthcare settings, interpretability, and physics
• 2 conference presentations (IEEE VIS 2023–24), 2 workshop presentations (NeurIPS 2023, ICLR 2025), 3 articles under review

• Leading multiple projects on AI Interpretability, Safety, and Alignment as Research Scientist / Mentor
• Collaboration has led to several publications and ongoing research

• Mentored undergrad and early grad students on explainable AI, visualization, and LLM interpretability
• Results: three accepted workshop submissions and four papers in preparation
• Organised student activities and outdoor trips aimed at improving mental and physical health

Frontiers in NeuroAI, Kempner Institute Symposium

Presenting research on understanding and interpreting large-scale activations in language models during reasoning tasks.

IEEE VIS 2024

Best short paper award winner on novel techniques for visualizing and identifying anomalous distortion in visual projections of high-dimensional data.