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 […]
CIF: Medium: Discovering Changes in Networks: Fundamental Limits, Efficient Algorithms, and Large-Scale Neuroscience
Modern technological advances have made it possible to collect extremely large datasets across a wide range of disciplines, spanning from social science to neuroscience. These datasets often consist of the activity patterns of many nodes that together form a network. For instance, in a social network, each node can represent a person while a connection […]
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: Collaborative Research: A Unifying Approach for Identification of Sparse Interactions in Large Datasets
More than 2.5 quintillion bytes of data are created daily in the form of sensor measurements, web posts and clicks, surveillance videos, purchase transactions, and health-care records. However, not all data collected is informative and not all features are relevant to the outcomes of interest. While several researchers have focused attention on compressive sampling for […]
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 […]