Carried out several key projects such as modeling the effects of non-coding variants, developing a foundation model for single-cell transcriptomics in cancer, discovering shared transcriptional states, among others. Published research findings in journals like Cell Reports Medicine, Genome Biology, JNCI, and Bioinformatics.
Research Experience
Focused on multiscale models for DNA, investigating the effects of non-coding variants in cancer, AI-driven integration of heterogeneous data for biomarker discovery, deconvolution of tumor signals from bulk data, and multi-omics survival modeling to predict patient outcomes.
Background
Research interests include developing machine learning approaches to explore transcriptional heterogeneity and plasticity in cancer, focusing on chromatin, transcription, and cancer evolution under treatment.
Miscellany
Offers Bachelor's and Master's student project opportunities in machine learning, bioinformatics, and cancer research.