Computational scientist · Excited-state dynamics · Ultrafast spectroscopy · Scientific machine learning

I build scalable methods and scientific software for understanding how molecules respond to light—from electronic-structure calculations and nonadiabatic dynamics to time-resolved spectroscopy and machine-learned Hamiltonians.

My work combines physical chemistry, high-performance computing, and reproducible software development. I have authored more than 40 peer-reviewed publications and contributed methods and tools for NEXMD- and NWChem-based research workflows.

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• Machine-learned quasi-diabatic Hamiltonians for excited-state molecular dynamics
• Nonadiabatic dynamics and X-ray, UV–visible, and photoelectron spectroscopies
• Molecular chirality analysis and real-space visualization
• Scalable, reproducible workflows for quantum chemistry and molecular simulation

• NQDH beating demo — A pre-trained neural quasi-diabatic Hamiltonian drives Libra/Ehrenfest dynamics for a 122-atom chromophore heterodimer and reproduces its S1/S2 vibronic quantum beating across a 300-trajectory ensemble.
• tr-XPES — Python and MATLAB tools for calculating time-resolved X-ray photoelectron spectroscopy signals from NWChem electronic-structure calculations, accompanying the JCTC method paper.
• Plotting chiral response — Tools to compute and visualize chiral population orbitals, accompanying the 2025 Chemical Science method paper on real-space visualization of X-ray circular dichroism.
• Euler–Lagrange Solutions — A tested Python package that derives equations of motion symbolically with SymPy and integrates them numerically with SciPy.
• STO–Gaussian overlaps — Orbital-overlap, density-matrix, and transition-density analysis connecting Slater- and Gaussian-type orbital representations.
• Libra tutorials — Tutorials for quantum-classical dynamics, including nonadiabatic dynamics driven by a machine-learned Hamiltonian.

• Developed excited-state simulation algorithms for systems with more than 200 atoms, with applications to dendrimers and organic photovoltaics.
• Built and scaled molecular-dynamics and quantum-mechanical workflows on national high-performance-computing facilities.
• Collaborated across physics, chemistry, computation, and experiment to turn new methods into reproducible research software.

• Languages: Python, Fortran, MATLAB
• Scientific computing: NumPy, SciPy, SymPy, Jupyter
• Scientific ML: PyTorch, hippynn, learned molecular Hamiltonians
• Electronic structure and dynamics: NWChem, PySCF, NEXMD, Libra, GROMACS
• HPC and software: MPI, OpenMP, Slurm, Linux, Git, continuous integration

I am open to research collaborations and opportunities in computational chemistry, scientific machine learning, and scientific software engineering. The best way to reach me is by email or LinkedIn.