Graduate student instructor for CS 227 (Foundation Models for Healthcare). Responsibilities include designing and grading assignments and holding weekly office hours.

• Honors senior thesis: developed a graph convolutional neural network trained on gene expression data from patients with Glioblastoma to predict cell state energy and learn the underlying graph structure of the gene-gene interactions
• Co-first author on ENCODE Consortium project, CoRNN, to predict three-dimensional organization of the genome (A/B compartments) from one-dimensional data (histone modification signals) using deep learning methods

Head TA for Deep Learning Spring ‘23. TA for Deep Learning (Fall ‘22, Spring ‘22), Introduction to Computer Systems (Fall ‘21), and Linear Algebra (Spring ‘21). Responsibilities included course development, grading problem sets/projects, and holding weekly office hours and labs.

Advised by Kevin Yang, Ava Amini, and Sarah Alamdari. Contributions during internship led to the publication of EvoDiff, a diffusion framework for generating proteins from sequence information. Assisted in project conceptualization, creation of generation models for 2D alignments, and analysis of the generations.

Advised by Neriman Tokcan through the Broad Summer Research Program (BSRP). Applied machine learning methods to spatial transcriptomic data. The project goal was to understand how Classical Hodgkin’s Lymphoma tumor cells survive based on interactions with nonmalignant immune cells in the cancerous environment. Presented work at Annual Biomedical Research Conference for Minority Students in November 2021.