Dave Gifford






David Gifford develops new machine learning techniques and algorithms to model the transcriptional regulatory networks that control gene expression programs in living cells.

Our group has a very productive interdisciplinary collaboration with leading biologists that has allowed us to tackle extraordinarily difficult and interesting problems that underlie cellular function and development.

Current work in our laboratory is examining how we can computationally model chromatin modifying complexes that are associated with the genome of living yeast cells. New kinds of mechanistic computational models are necessary to capture how chromatin structure encodes cellular memory, and how the state of this memory is used to control gene expression.

In particular, we are investigating new modular graphical models that use mechanistic constraints to describe biological mechanism. A new focus is an interdisciplinary project that seeks to build computational models of the transcriptional regulatory networks that control the differentiation of specific cell types.

Elucidating these regulatory networks will enable us to define the regulatory processes that determine a cell's progress to its terminally differentiated state, and position us to differentiate embryonic stem (ES) cells for the treatment of debilitating human diseases.

New computational techniques for elucidating transcriptional regulatory networks based on the integration of diverse high-throughput experimental data (genome sequence, chromatin structure, transcription factor-DNA binding, gene expression) provide a powerful foundation for discovering the detailed mechanisms of regulatory network control of cell differentiation during development.



Learning Optimal Interventions

We develop statistical models that are prescriptive rather than predictive/descriptive. From an observational dataset, our methods learn to automatically identify beneficial actions that will improve outcomes, rather than requiring human-made decisions.
Tommi Jaakkola



Community of Research

Applied Machine Learning Community of Research

This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.