Ramesh Sridharan

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These notes give a short introduction to Gaussian mixture models (GMMs) and the Expectation-Maximization (EM) algorithm, first for the specific case of GMMs, and then more generally. These notes assume you’re familiar with basic probability and basic calculus. If you’re interested in the full derivation (Section 3), some familiarity with entropy and KL(More)
We have incorporated cluster computing fundamentals into the introductory computer science curriculum at UC Berkeley. For the first course, we have developed coursework and programming problems in Scheme centered around Google’s MapReduce. To allow students only familiar with Scheme to write and run MapReduce programs, we designed a functional interface in(More)
We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over(More)
Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional(More)
We present an analysis framework for large studies of multimodal clinical quality brain image collections. Processing and analysis of such datasets is challenging due to low resolution, poor contrast, mis-aligned images, and restricted field of view. We adapt existing registration and segmentation methods and build a computational pipeline for spatial(More)
We present a model that describes the structure in the responses of different brain areas to a set of stimuli in terms of stimulus categories (clusters of stimuli) and functional units (clusters of voxels). We assume that voxels within a unit respond similarly to all stimuli from the same category, and design a nonparametric hierarchical model to capture(More)
The effective translation of data into novel insights, discoveries, and solutions, also known as data science, has enormous potential to bring about positive social change. In this paper, we propose ways to “democratize data science”: that is, to allocate the power of data science to society’s greatest needs. Two underlying challenges are 1) the(More)
Medical image analysis often requires developing elaborate algorithms that are implemented as computational pipelines. A growing number of large medical imaging studies necessitate development of robust and flexible pipelines. In this thesis, we present contributions of two kinds: (1) an open source framework for building pipelines to analyze large scale(More)
EPILEPSY is a disorder characterized by recurrent seizures of cerebral origin, presenting with episodes of sensory, motor or autonomic phenomenon with or without loss of consciousness. Epilepsy is the second most common chronic neurological condition seen by neurologists. It is estimated that there are 55,00,000 persons with epilepsy in India, 20,00,000 in(More)