Venkatesh Saligrama

Foundations of Data Science Institute

The Foundations of Data Science Institute (FODSI) brings together a large and diverse team of researchers and educators from UC Berkeley, MIT, Boston University, Bryn Mawr College, Harvard University, Howard University, and Northeastern University, with the aim of advancing the theoretical foundations for the field of data science. Data science has emerged as a central […]

Collaborative Research: CIF: Small: Learning from Multiple Biased Sources

The field of artificial intelligence, and especially machine learning, is concerned with automating the performance of a task by learning from past performances of that task. Examples include classifying images and successfully navigating a maze. Classical machine learning methods assume that past occurrences of a task, or ?training data,? accurately represent future occurrences of the […]

CIF: Small: Learning Mixed Membership Models with a Separable Latent Structure: Theory, Provably Efficient Algorithms, and Applications

In a wide spectrum of problems in science and engineering that includes hyperspectral imaging, gene expression analysis, and metabolic networks, the observed data is high-dimensional and arises from an unknown random mixture of a small set of unknown shared latent (hidden) causes. Being able to successfully and efficiently identify the latent causes from the observed […]

CIF: Small: Sensing-Aware Decision Making for High-Dimensional Signals

There has been an explosion in our ability to sense and record the world around us. This has led to new discoveries and allowed us to consider new paradigms in nearly every walk of life. While the promise of these developments is significant, the explosion of sensing has also created substantial challenges. These challenges include […]