I am a biophysicist and data scientist specializing in the modeling of chromatin structure and dynamics. My research integrates stochastic simulations, molecular dynamics, and machine learning, all implemented in Python—my primary programming language and favorite scientific tool.

My work centers on unraveling the complexities of chromatin architecture, particularly how 3D genome organization arises from linear DNA sequence. To achieve this, I develop computational frameworks that combine:

• Sequence-based predictive models for chromatin interactions and epigenetic states.
• Physics-driven simulations to translate these predictions into dynamic structural models.

The ultimate goal is to construct a hybrid pipeline that enables in silico reconstruction of chromatin architecture purely from sequence data—a step toward understanding how genome structure influences function in health and disease. Until now I have implemented 3 user-friendly pipe-lines for the modeling of chromatin dynamics from experimental data.

A minimal yet powerful simulator integrating replication dynamics, loop extrusion, and epigenetic spreading.

• Models DNA replication under both physiological and stress conditions.
• Enables exploration of how chromatin structure and epigenetic state influence replication.
• Currently applied to study replication stress in cancer.

“Simplicity is the ultimate sophistication.” — My guiding principle in design and code.

A multiscale simulation platform for modeling chromatin across all levels of organization.

• Supports modeling from nucleosomal arrays to chromosome-scale domains.
• Designed to be user-friendly and extensible for both research and educational purposes.

A stochastic model of loop extrusion capable of reproducing experimental Hi-C heatmaps.

• Captures dynamic interactions mediated by loop extruding factors.
• Useful for testing hypotheses about CTCF-cohesin-mediated chromatin folding.

All three tools contribute to a unified vision:

Decoding the 3D genome from its 1D sequence.

This interdisciplinary effort merges machine learning and polymer physics to tackle one of the most fundamental challenges in genome biology.