I’m a computational researcher, with experience working in interdisciplinary projects (biochemistry, computational biology, bioinformatics, physics, machine learning).
Much of my past work has focused on the sequence–structure–function relationships of intrinsically disordered proteins (IDPs), though my interests also extend more broadly across computational biology and bioinformatics.
The selected publications below reflect some of my computational work. I’m eager to continue exploring research questions in computational biology and related fields.
- Resolving Local and Global Conformational Heterogeneity of the Human Intrinsically Disordered Proteome
- A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis
- Map Conformational Landscapes of Intrinsically Disordered Proteins with Polymer Physics Quantities
- Exploring Structures and Dynamics of Protamine Molecules through Molecular Dynamics Simulations
- PyHeteroMap: A python package for analysis of intrinsically disordered proteins simulations, directly from trajectory.
- PyConforMap: A python module that plots a map of the conformational landscapes of intrinsically disordered proteins and polymers, using shape and size metrics.
Python · R · BEAST · IQ-TREE · Nextclade · HPC
numpy · machine-learning
mdtraj · pandas · ggtree · geopandas