Motivated
to be
Designing Algorithms
Wrangling Data
Building Models
Working for You
About Me
As both a Computer Science and Mathematics student at UC Davis, I have developed a strong foundation for which I have built many skills in algorithm development, Machine Learning, Computational Genetics, and Data Science. These skills include low level C/C++ skills, algorithm methodologies such as dynamic programming or greedy algorithms, and a familiarity with many languages inluding R, Python, C++, Java, and Matlab. I have developed an understanding of Machine Learning methods such as model selection, active learning through uncertainty queries, hyper-parameter tuning, and experience with Neural Network, SVM, GLM, and Logistic Regression models. Through my work at Forage Genetics, I have become comfortable analyzing genetics data from qPCR and genetic sequence alignments and built models for auto tetraploid dosage calling. In my journey into Data Science, I have developed skills in SQL, Web-Scraping, and Statistical Analysis methods while gaining experience with the visualization software and cloud computing servies. My continued education is focusing on Machine Learning models and continuing to build my skill base for Data Science techniques. Learn more at About Me
My Projects
Some of my work has been chronicled on My Projects. Humidor is a microbial classifer that classified microbes by their 16S gene using Tensorflow's Convolutional Neural Network. Quadrophenia is an auto tetraploid dosage calling algorithm that used a mixed model approach including a Semi-Supervised K-Mean Clustering algorithm, a Generalized Linear Model, and a Multinomial Classification Model. MathNet is a study on the similarity between Mathematics theorems and seperate fields of mathematics including a field classification algorithm. The Virtual Machine is a full virtual operating system impliments in C for machine level code, such as system calls and context switches, and C++ for thread scheduling and the file system.