Anupama is an Assistant Professor in the Department of Genetics at Yale University. Her lab develops predictive machine learning methods to understand 3D genome architecture and downstream gene regulation across healthy tissues and cancers. By building models that connect DNA sequence, chromatin organization, and functional readouts, her work aims to uncover the principles that govern genome regulation across biological contexts and to translate large-scale genomic data into interpretable models of cellular function. In addition to developing new computational methods, a core theme of her research is making these approaches useful for answering fundamental biological questions in gene regulation and disease. Before joining Yale, Anupama was a postdoctoral scholar at the University of Washington with William Stafford Noble, where she developed TwinC, an early sequence-to-function model for trans-chromosomal DNA contacts, and Fibertools for m6A calling in long-read Fiber-seq data. She completed her Ph.D. in Computer Science at the University of Pennsylvania with Yoseph Barash, where she developed interpretable machine learning methods to study tissue-specific splicing and RNA-binding protein regulatory networks. The overarching theme of her work is the development of interpretable computational models that connect genome sequence to higher-order regulation and function.