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Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. During her Ph.D., she quantified the potential pan-genomic scope of microRNA targeting and combinatorial regulation of protein expression and co-discovered the first microRNA in a virus. As a postdoc, she and her colleagues cracked the classic, unsolved problem of ab initio 3D structure prediction of proteins using a maximum entropy probability model for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her new lab at Harvard is interested in developing methods in deep learning to address a wide range of biological challenges including predicting the effects of genetic variation and sequence design for biosynthetic applications.