Computing

Every structural biology course eventually hits the same wall: students can look at a folded protein in PyMOL, they can dock a ligand with AutoDock Vina, but they never actually see the physics doing the work. PyMOL doesn’t visualize forces. Vina’s scoring function is a black box unless you go digging through source code most students will never open. The intuition that molecular dynamics is just Newton’s second law, applied a few million times in a row, gets lost somewhere between the lecture slides and the exercise sheet.

Dataframe operations form the foundation of modern data science workflows, yet researchers often face vendor lock-in when choosing between different dataframe frameworks. My work addresses this by developing a unified operations library that abstracts away framework-specific implementations. The challenge lies in handling the fundamental differences between these systems—some pandas-like dataframes use row-by-row operations, others employ lazy evaluation with native expressions, some require distributed execution patterns, while others focus on parallelization.