Discusses challenges in comparing non-Euclidean data, proposing a Laplacian-based solution for graph alignment and exploring optimal transport for graph distance computation.
Explores protein structure determination using X-ray crystallography and NMR spectroscopy, covering historical significance, crystal formation, diffraction patterns, and challenges in crystallization.
Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.
Explores the importance of protein-ligand interactions, focusing on binding affinities and energetic landscapes, with implications for drug development and specificity.
