Data Sciences

Modeling and Algorithms for Aggregated Data

Databases in domains such as healthcare are routinely released to the public in aggregated form to preserve privacy. However, naive application of existing modeling techniques on aggregated data is affected by ecological fallacy that can drastically reduce the accuracy of results and often lead to misleading inferences at the individual level. The project by Prof.

Generalization of Standard Matrix Completion

Joydeep Ghosh and student Suriya Gunasekar work on the generalization of standard matrix completion in various aspects. In previous work, we have proposed tractable estimators for matrix completion with observations arising from heterogeneous datatypes and heterogeneous noise models. In a more recent work, we focus on consistency results for the collective matrix completion problem of jointly recovering a collection of matrices with shared structure.

Bayesian Sparse Principal Component Analysis

Several real-life high dimension datasets can be reasonably represented as a
linear combination of a few sparse vectors. Succinct representation of such data with a few selected variables is highly desirable for such cases. A Bayesian setup is useful because the limitation of knowing a limited number of high dimensional data points can be alleviated by well-designed domain-specific priors.

WNCG Welcomes New Director for Three-Year Term

Every few years, WNCG welcomes a new Director and Associate Director from among its faculty ranks. With an academic culture that encourages openness and research collaborations among equals, the rotation of Directors provides each faculty member with the opportunity to lead WNCG.

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