About Me
I am a Computational Scientist at Genetesis, where I develop advanced algorithms and multiscale finite element models to simulate complex real-world phenomena, with a focus on cardiac electrophysiology for diagnostic tools like CardioFlux.
With a PhD in Mechanical Engineering from the University of Kentucky, I specialize in translating complex mathematical models into scalable computational frameworks. My expertise spans algorithm development, finite element analysis (FEA), programming (Python, MATLAB, C++), machine learning, data analysis, signal processing, and image processing. While my recent work focuses on cardiac simulations, my skills are broadly applicable to engineering, biomedical, and scientific challenges.
I’m passionate about driving innovation through computational modeling and collaborating with cross-functional teams. In my spare time, I enjoy hiking, exploring new technologies, and watching documentaries. I’m always open to discussing potential collaborations or opportunities—feel free to reach out!
Key Skills
Algorithm Development
Expert in designing algorithms for multiphysics simulations, including FEM-based solvers for cardiac and engineering applications.
Programming & Data Analysis
Proficient in Python (NumPy, SciPy, pandas, scikit-learn), MATLAB, and C++ for automation, visualization, and large-scale data processing.
Machine Learning & Signal Processing
Skilled in deep learning (Keras, PyTorch), image processing (OpenCV), and signal analysis for biomedical and engineering datasets.