• Applied quantum chemical computation methods (DFT, Hartree Fock, Post Hartree Fock) to inform the design and characterization of battery materials, solvents, and solid-liquid interfaces.• Conducted molecular dynamics simulations (ab-initio, empirical, and machine-learned potentials) to simulate the properties of novel battery solvents, aiming to enhance safety and performance.• Proficient in the usage of DFT codes and methods such as Gaussian, VASP, Quantum Espresso, NWChem, and COSMO.• Developed and implemented novel neural network architectures as well as graph equivariant networks tailored to specific property predictions of solvated systems• Utilized machine learning techniques, including active learning and transfer learning, to improve the prediction of thermophysical properties.• Proficient in high and low-level programming languages, including Python, C++, FORTRAN, PyTorch, JAX, TensorFlow, and scikit-learn.• Developed a multi-objective genetic algorithm for discovering materials and tailored molecular solvents, contributing to advanced material discovery initiatives.• Implemented a non-linear solvation model coupled with adsorption potentials, integrated into the VASPsol code using Fortran, to improve simulation accuracy for battery materials.• Created and managed extensive datasets (> 1 million entries) of quantum calculations, computing solvated properties of molecules for use in battery design.• Regularly presented research findings in internal seminars and proposals, and disseminated research at conferences such as the Sanibel Symposium, AIChE, and APS.

• I developed software and algorithms for materials discovery and materials characterization using an extensive toolset combining machine learning, physics, and custom software development. I am proficient in using VASP, Gaussian, Quantum Espresso, and NWChem for quantum calculations coupled with common packages such as RDKit, Pymatgen, PyIron, PySCF and more. I am proficient in using Fortran, Python, PyTorch, Tensorflow, JAX, C++, OpenACC, Linux/bash scripting, and MATLAB. • I am the lead developer of the VASPsol solvation package (https://github.com/henniggroup/VASPsol) used by thousands of researchers globally. • My work has extended the VASPsol parameterization beyond water to 19 different solvents, opening up a diverse range of applications from catalysis to studying materials for batteries. • I have developed a GPU-accelerated version of the VASPsol code using OpenACC• I have used Machine Learning and artificial intelligence to improve prediction accuracies for solvated systems. Through the use of transfer learning and active learning, I have improved intensive property prediction using molecular structures, as well as improved solvation free energy and thermodynamic activity coefficients. • We have been awarded the EAGER: SSMCDAT2023: Database generation with Dr. Megan Butala to identify trends in inter- and intrapolyhedral connectivity and energy storage behavior (DMR-2334240). Our collaboration has led to the successful implementation of artificial intelligence algorithms to predict the properties of transition metal oxide battery materials. • Member of the Molecular Magnetism for Quantum Materials (M2QM) Energy Research Frontier through the Department of Energy. I have developed methods for using artificial intelligence to discover novel spin-crossover molecules for quantum information devices.

• Operated instruments such as UV/Vis, SEM, TGA, EDS, and FTIR to characterize compounds and verify experimental results • Modeled heterogeneous catalytic decomposition of species within adsorption column processes to assist in the design process of aircraft filtration systems • Built and ran test stands measuring quantities such as VOC concentrations, pressure drops, and conversion of species• Characterized materials and degradation to inform design processes and assist with specific client needs• Functionalized surface chemistries of materials to improve VOC uptake• Conducted extensive literature reviews and meta-analyses to inform experimentation and modeling techniques to target client needs

Assisted in improvement of the manufacturing process and drove scrap reduction efforts in the production of high-pressure turbine blades for jet engines. • Provided support to engineers in the process of producing high pressure turbine blades• Conducted a root cause analysis on material handling damage and grinding operation failures using quality inspection and statistical process control data in tangent with a historical study on program parameters• Designed methods to collect process data and analyzed it using statistical methods• Developed a program that can dynamically analyze process defect databases and produces reports given user inputs

• Designed, prototyped, and 3d printed various auxiliary fixtures, such as an integrating sphere• Synthesized two dimensional nanomaterials with varying morphologies• Characterized products using Raman spectroscopy, powder X-ray diffraction, spectrofluorimetry, and scanning electron microscope images• Reported findings weekly to mentoring professors and peers

• Designed products and produced 3D CAD models using SolidWorks• Designed and implemented fixtures to support manufacturing processes• Manufactured parts utilizing manual mills and lathes in accordance with technical drawings• Performed quality assurance and control functions to ensure compliance with technical drawings

• Designed and scheduled lesson plans for supplemental learning sessions to help students succeed in classes such as pre-calculus and college algebra• Students who attended sessions received a letter grade higher than their average grade in similar classes