- Research Program Mentor
PhD Doctor of Philosophy candidate
Neuroscience, Computational Neuroscience, RNA-SEQ, Bioinformatics
In this project, students will learn about different types of neural networks and what problems motivated their creation from a historical perspective. Students will then select a network model to train to perform a task using a publicly available dataset. For example, students could train a convolutional neural network to categorize images in the CIFAR-10 dataset. Depending on familiarity with python, students will have the option of building their own model or using a publicly available one.
In this project, students will learn about RNA-seq and how to analyze and interpret data from these experiments. This includes providing a foundational introduction to quality control, alignment, and other data processing steps found in bioinformatics pipelines. Students will then use publicly available gene expression data (e.g. Allen Cell Types Database) to generate plots and analyze gene expression in the assayed cells.