Awarded in 2016
Home Department: Computer Science
Faculty Advisors: Anshul Kundaje (Computer Science, Genetics) and Helen Blau (Microbiology & Immunology)
Research Title: Interpretable deep learning approaches for regulatory genomics
Research Description: All cells in our body have essentially the same DNA sequence, but cells from different tissues have distinct behaviour because different genes are turned on in each cell type. Understanding this gene regulation is of key importance - in fact, the majority of disease-associated mutations occur not in genes but in regions responsible for regulating gene activity. Prevailing computational tools cannot satisfactorily model gene regulation. Deep learning, a powerful new computational technique, could provide a solution, but the resulting models are difficult to interpret. By developing novel approaches to make the models interpretable, Avanti hopes to effectively apply deep learning to gain valuable insights into gene regulation.
WHERE IS SHE NOW?
Avanti is a Stanford Data Science postdoctoral fellow applying machine learning to study oceanic nutrient cycling with Professor Karen Casciotti.