Skills

Capabilities

Technical depth across AI, cheminformatics, and scientific computing

I work comfortably across modeling, simulation, engineering, and research tooling, which makes it easier to connect exploratory science with production-facing workflows.

Deep learning and graph models Molecular simulation platforms Cheminformatics and APIs HPC and workflow automation

AI and Data

Modeling toolkit

Equivariant Graph Neural Networks Deep Learning GANs Diffusion Models LLMs CNNs RNNs Transformers Clustering SVM Random Forests

Python Stack

Libraries I use often

PyTorch TensorFlow Keras Pandas NumPy SciPy Scikit-learn Matplotlib Seaborn

Scientific Computing

Engineering and systems

  • Programming in Python, Bash, and Tcl/Tk with day-to-day HPC scripting and workflow automation.
  • Comfortable with AWS, Linux systems, Git, DVC, SLURM, and PBS environments.
  • Built and administered LDAP-based client-server infrastructure for a 400 TB file server setup during doctoral research.

Molecular Tooling

Simulation and analysis platforms

OpenMM AMBER NAMD Gromacs Desmond Psi4 Gaussian16 Orca VASP Quantum Espresso OEDock Smina AutoDock Vina Glide GOLD DiffDock

Cheminformatics and APIs

Production-friendly interfaces

RDKit OpenEye Toolkit Chemaxon OpenBabel PaDel ProLIF ASE MDTraj MDAnalysis BioPython

Structural Biology

Adjacent biophysics expertise

  • X-ray crystallography and cryo-EM structure analysis for protein-ligand complexes.
  • Homology modeling and protein preparation.
  • Binding site analysis and structure-activity relationships.