Who am I?

I am a PhD candidate in the Bioinformatics and Integrative Genomics program at Harvard Medical School, and a National Defense Science and Engineering Graduate (NDSEG) fellow, co-advised by Dr. Maha Farhat and Dr. Marinka Zitnik. At Harvard, I am excited to design novel machine learning methods to predict phenotypes from genotypes. The overarching objective of my PhD research is to design multimodal machine learning methods that integrate whole genome sequencing and gene to gene interaction data to predict phenotypes such as antibiotic resistance in Tuberculosis or vaccine escape in Covid and Influenza. I also aim to understand how well these models can generalize to new and unseen mutations. I have also worked on designing convolutional neural networks to predict breast cancer presence and subtypes from histopathology slides with Dr. Kun-Hsing Yu. Previously, I received a B.S. in Electrical Engineering and Computer Science (EECS) and another BS in BioEngineering (BioE) from UC Berkeley, where I developed computational methods to understand microbial communities with Dr. Adam Arkin and Dr. Lauren Lui. Beyond academia, I have worked at Dascena (now CirrusDx, Inc) designing machine learning models to use EHR data to predict ischemic stroke occurrence in patients and at Verily gathering metrics for a convolutional neural network predicting diabetic retinopathy to facilitate FDA consideration of the algorithm.