Physics-trained. Biology-driven. Built for computation.

Biography

From molecular mechanisms to methods other researchers can use.

My work begins with a scientific question: how molecular structure, motion, and interaction produce larger-scale biological behavior. I translate those questions into geometric algorithms, stochastic models, and computational experiments, then build the software needed to validate and reuse the resulting methods.

At Inria, I develop inverse-kinematics methods for protein-backbone conformational sampling and a framework for generating consistent scientific interfaces across molecular-visualization platforms. Previously at Johns Hopkins University, I developed mechanistic models of biomolecular self-assembly, co-developed ioNERDSS, and led the MPI parallelization of the NERDSS reaction–diffusion simulator.

My long-term research direction is to make biomolecular conformational exploration faster, more scalable, and more accessible by connecting geometric sampling, statistical mechanics, molecular simulation, structural data, and high-performance computing.