About

I am a computational biologist/bioinformatician. My primary research interests lie in using machine learning models to decipher how gene regulation is encoded in our genomes. I’ve also worked on variant effect and complex disease risk prediction.

Currently, I am a postdoc with Anshul Kundaje at Stanford. Previously, I did my PhD with Charles Danko at Cornell.

During my PhD, I developed CLIPNET, a deep learning model that predicts transcription initiation with single nucleotide resolution from DNA sequence. Interpretation of CLIPNET revealed a complex regulatory syntax governing how core promoter and transcriptional activator motifs work together to control when and where transcription begins. This work was published in Nature Genetics.

I’ve also worked on improving variant effect prediction by training deep learning models on personalized genomic sequences, and have contributed to studies using functional genomics to identify disease-driving variants in juvenile idiopathic arthritis.