Dr. David Gifford

Biography

David Gifford develops new machine learning techniques and algorithms to model transcriptional regulatory networks that control gene expression in living cells. His research group employs combined computational and experimental approaches for discovering novel biology related to human therapeutics. They focus on creating interpretable computational models trained and validated with experimental evidence. Gifford's collaborators apply these models to problems in experimental design, developmental biology, gene regulation, immunology, and genomics. The group typically evaluates their models through studies that produce data from both populations of cells and single cells. An ongoing challenge in their work is the incomplete knowledge of biological systems, leading to model uncertainty. Therefore, an active area of study in his group is generating appropriate uncertainty metrics for models to guide experiment design and improve model accuracy. The lab utilizes both conventional large-scale linear and non-linear models as well as Bayesian methods and deep learning approaches. Current specific research areas include motor neuron development, single-cell perturbation studies, chromatin regulation, genome regulation, antibody design, and peptide presentation to major histocompatibility complex (MHC) proteins.

Research Interests

Experience