Academic Research Interests
My academic research focuses on understanding how complex collective behavior emerges in polymeric and biomolecular systems from relatively simple physical principles. I am particularly interested in developing and applying coarse-grained models that retain essential physics while remaining computationally efficient and conceptually transparent.
Many of these interests are reflected directly in my ongoing and completed research projects, including studies on polymer topology under crowding, salt-mediated self-assembly, nanopore translocation, and surface adsorption.
Coarse-Grained Modeling and Polymer Physics
A central theme of my work is coarse graining—systematically reducing molecular complexity to minimal models that capture the dominant interactions and length scales. I primarily use bead–spring polymer models, grounded in polymer physics and statistical mechanics, to study conformational, dynamical, and transport properties of long-chain molecules. These approaches underpin my work on polymer compaction, adsorption, and translocation under confinement and crowding.
Polyelectrolytes and Charged Polymer Systems
I am interested in the physics of polyelectrolytes, where long-range electrostatic interactions, salt effects, and ion correlations strongly influence behavior. My research examines how salt concentration, multivalent ions, and charge distribution regulate polymer conformations, self-assembly, and effective interactions. These interests are directly connected to my published work on salt-induced self-assembly and the anti-polyelectrolyte effect.
Polymer Translocation and Transport
Another major research direction is polymer translocation through nanopores and confined geometries. Using numerical simulations, I study how electric fields, salt concentration gradients, polymer–pore interactions, and crowding effects control translocation pathways and timescales. This work links fundamental polymer dynamics with biologically relevant transport processes and nanopore-based technologies.
Self-Assembly and Biomolecular Condensates
I am interested in self-assembly phenomena in polymeric and biomolecular systems, including clustering, aggregation, and re-entrant condensation driven by electrostatics and crowding. These ideas naturally connect to the physical understanding of biomolecular condensates, where weak, multivalent interactions give rise to mesoscale organization inside cells.
Chromatin Compaction and Crowding
Motivated by biological organization, I am increasingly interested in chromatin compaction and genome organization from a polymer-physics perspective. I aim to understand how crowding, confinement, topology, and effective interactions collectively regulate large-scale chromatin structure using minimal, physically grounded models.
Numerical Simulations and Modeling
Methodologically, my work relies on numerical simulations, particularly coarse-grained molecular dynamics and stochastic simulations. Beyond running simulations, I focus on extracting physical insight through careful analysis, scaling arguments, and free-energy-based interpretations, with an emphasis on connecting numerical results to simplified theoretical pictures.
Non-Academic and Cross-Disciplinary Research Interests
Beyond traditional academic problems, I am deeply interested in modeling as a general framework for understanding complex systems, particularly where stochasticity and emergent behavior play a central role.
Cellular Automata and Emergent Computation
I am fascinated by cellular automata, including Conway’s Game of Life, as minimal models of emergence, pattern formation, and self-organization. These systems provide intuition for how complex global behavior can arise from simple local rules—ideas that resonate strongly with modern ML systems and agent-based modeling.
Future Directions
Looking ahead, I aim to expand my research toward integrative modeling of chromatin and nuclear organization, combining coarse-grained polymer models with data-driven analysis. I am particularly interested in incorporating machine learning techniques to analyze large simulation datasets, identify emergent patterns, and build reduced models that connect microscopic interactions to mesoscale organization.
More broadly, I am motivated by research directions that connect polymer physics, biophysics, and modern computational methods, with an emphasis on interpretability, minimal modeling, and physical insight.