Sakyajit Bhattacharya

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The dynamic nature of crowd platforms poses an interesting problem for users who wish to schedule a large set of tasks on a given platform. Although crowd platforms vary in their performance characteristics, certain temporal patterns can be discerned and statistically modeled. Methods that can learn these patterns and adapt as the patterns change can(More)
A rank clustering system, CloudRank, is proposed that takes into account cloud user preference data to characterize cloud user behaviour and also identify (an initially unknown set of) groups of users with similar behaviour in an unsupervised manner. The user groups are determined based on fitting mixture models on the cloud user preference observations. A(More)
Stroke is a major cause of mortality and long-term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. We design a new multi-class classification model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our(More)
Heterogeneous data with complex feature dependencies is common in real-world applications. Clustering algorithms for mixed – continuous and discrete valued – features often do not adequately model dependencies and are limited to modeling meta–Gaussian distributions. Copulas, that provide a modular parameterization of joint distributions, can model a variety(More)
An Acute Hypotensive Episode (AHE) is the sudden onset of a period of sustained low blood pressure and is one of the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to irreversible organ damage and death. By identifying patients at risk for this complication, adequate medical intervention can save lives and(More)
We propose an online scheduling algorithm for posting crowdsourcing tasks which maximizes a novel metric called task viewership. This metric is computed using stochastic model based on coverage process and it measures the likelihood that a task is viewed by multiple crowd workers, which is correlated to the likelihood that it will be selected and completed.
Crowd workers exhibit varying work patterns, expertise, and quality leading to wide variability in the performance of crowdsourcing platforms. The onus of choosing a suitable platform to post tasks is mostly with the requester, often leading to poor guarantees and unmet requirements due to the dynamism in performance of crowd platforms. Towards this end, we(More)