With four years of molecular science experience and a Master's in Molecular Science and Software Engineering, I bring a powerful combination of deep scientific knowledge and robust computational skills. Since graduating in May 2024, I've actively pursued personal coding projects focused on machine learning for life sciences. I'm currently seeking roles as a Computational Biologist, Software Engineer, or positions where I can leverage my full, interdisciplinary skillset.
I've worked on several projects, both individual and collaborative. Below are descriptions and links to GitHub repositories of each one.
Feel free to reach out to me if you'd like to learn more about my work!
This repository features machine learning algorithms designed for life sciences, combining my experience in molecular science and software engineering. Most of the code here is independent work that reflects my interests in the uses of machine learning in life sciences, from medical imaging to drug discovery. I have also included some of my work from a machine learning course I took during my masters.
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In this individual project, I developed two deep learning models, an artificial neural network (ANN) and a residual neural network (ResNet), for predicting molecular energies within the ANI1 dataset. To encode molecular structures into Numpy arrays, I used the DScribe package, which transforms atomic structures into fixed-size vectors or matrices. I also used Scikit-learn and PyTorch.
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During my internship at Pivotal Life Sciences, I worked with DeepFRI, a deep learning model that combines LSTM networks with graph convolutional networks to predict protein function. My role involved using Python libraries like Tensorflow and Biopython to develop complementary functions that increased DeepFRI’s utility to Pivotal Life Sciences. At the end of my internship, all code was handed over to Pivotal’s computational biology team.
To delve into the dynamics of changing allele frequencies in a population, I worked on a collaborative project where my team designed and implemented a C++ cellular automata model. I used Python libraries seaborn and matplotlib to create insightful visualizations depicting our results. This project not only deepened my understanding of population genetics and computational modeling techniques but also honed my programming skills in C++ and Python and strengthened my ability to collaborate.
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Masters in Molecular Science and Software Engineering
Bachelor of Science in Biomedical Science
After School Science Teacher
Computational Biology Intern
Molecular Technologist I