I’m a computational scientist and scientific programmer integrating data-driven modeling, deep learning, and soft-matter physics to advance research at the intersection of chemistry, physics, and biology. I specialize in developing reproducible, open-source pipelines that connect experimental and computational science — from molecular dynamics and statistical modeling to machine learning–based analysis.

My technical expertise spans Python software development, Bayesian optimization, and molecular-scale modeling, with experience designing performant algorithms, APIs, and research frameworks that bridge disciplines. I’m passionate about making complex scientific systems understandable and usable through efficient, well-documented code.

Over the past five years, I’ve:

• Developed and released multiple open-source Python packages — including VorPy, Foamify, and Coarsify — for molecular partitioning, coarse-graining, and stochastic structure generation.
• Published first-author research in Physical Chemistry Chemical Physics (PCCP) advancing geometric modeling of molecular systems.
• Collaborated across teams in chemistry, physics, biology, and applied mathematics to ensure reproducibility, scalability, and scientific rigor.
• Integrated statistical, geometric, and AI methods into workflows that accelerate material and molecular discovery.

My goal is to bridge scientific insight with computational precision, enabling tools that help researchers and engineers design the next generation of molecular systems and materials.

• Python (NumPy, Pandas, SciPy, scikit-learn, Matplotlib, Seaborn, Plotly)
• Statistical modeling, regression and classification, clustering (K-means, hierarchical)
• Dimensionality reduction, biological data integration, and exploratory visualization

• Object-oriented programming, algorithm optimization, and API development
• Version control (Git/GitHub), testing and debugging (pytest), continuous integration (CI/CD)
• Technical documentation, package deployment (PyPI, Conda), and reproducible research workflows

• GROMACS, OpenMM, and coarse-graining frameworks
• Surface and binding interaction analysis, thermodynamic modeling
• Monte Carlo and stochastic simulations, force-field parameterization

• Cross-disciplinary collaboration across chemistry, physics, biology, and data science
• Experimental design and validation, open-source development, and team leadership
• Technical translation for publication and presentation

VorPy is a Python framework for spatial partitioning and geometric analysis of molecular systems.
It computes Additively Weighted, Power (Laguerre), and Primitive (Delaunay) Voronoi diagrams for 3D spheres, supporting atomic coordinate files across major formats.

Features:

• Solves and exports weighted Voronoi and Delaunay tessellations
• Generates visualizations and geometric statistics
• Includes both a GUI for intuitive workflows and a CLI for automation and scripting

Coarsify is a Python package for molecular structure reduction and geometric analysis, transforming atomic ensembles (.pdb, .gro, .mol, .cif, .xyz) into simplified coarse-grained models.
It supports multiple schemes for grouping and averaging atoms — ideal for MD post-processing and visualization.

Foamify (formerly foam_gen) generates randomized 3D sphere ensembles for porous media and microstructure simulation.
Users can control sphere count, radius distribution, and packing behavior to produce datasets for modeling aerated media, granular systems, and porous materials.

This exploratory project simulates physical predictions from the Grand Unified Theory of Classical Physics (GUTCP) proposed by Dr. Randall Mills, using Python-based numerical solvers and field visualizations.

Example: electromagnetic current field vectors forming a “classical electron” orbitsphere, merging magnetic and electric domains to illustrate a unified classical representation.

December 2024

• Thesis: “The Geometry of Spatial Decomposition: Evaluating Partitioning Schemes in Physical Chemical Systems.”
  Developed computational methods for statistical and geometric analysis of simulation data.
• First-author publication in Physical Chemistry Chemical Physics (2025): “Evaluation of Weighted Voronoi Decompositions of Physicochemical Ensembles.”
• Awards: Research Excellence Award, Department of Chemistry, 2023.
• Coursework emphasized computational chemistry, scientific programming, and data analysis.

August 2019

• Undergraduate research in optics and magneto-optical effects, studying Faraday rotation in dielectric materials under strong magnetic fields.
• Graduated early with a focus on theoretical and computational problem-solving.
• Coursework emphasized computational physics, advanced mathematics, and data analysis.

Website: ericsonlabs.com
Email: jackericson98@gmail.com · jericson1@gsu.edu
LinkedIn: linkedin.com/in/jackericson98

Thanks for visiting — feel free to reach out for collaboration or opportunities!