Prof. Joydeep Ghosh receives $496K from NSF

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Published:
August 18, 2014

Much of life involves setting priorities. In the age of information overload, the need to select relevant information and prioritize is greater than ever. Ranking product choices, identifying items of greatest relevance and interest and setting priorities are widespread issues in economics, health informatics and online networks. 

WNCG Prof. Joydeep Ghosh and his research team plan to develop scalable ordering and ranking algorithms for complex data using the concept of Monotonic Retargeting (MR). Using tools from convex optimization, function approximation and stochastic learning theory, Prof. Ghosh will create a framework to establish efficient solutions for different classes of associated learning problems. This framework will affect ordering, ranking and determine top choices for recommendation, multi-label classification and multi-dimensional isotonic regression.

To further his research in this field, the National Science Foundation awarded Prof. Ghosh a grant worth over $496,000.

This research has widespread applications in the study of diseases, where only a small number of genes associated with certain illnesses are known. Yet several sources of information on gene-to-gene relations exist, such as co-expression and protein interactions. The question becomes how to prioritize, simultaneously and for each disease, a small number of additional genes associated with the illness.

According to Prof. Ghosh, the central idea behind his MR research is to avoid the complexity of ordering by efficiently optimizing over all possible monotonic transformations of numeric preference scores. The approach exploits the full power of available partial order information while maintaining the simplicity of regression like approaches to the problem.  Prof. Ghosh’s framework will also incorporate supplementary information, unlabeled data and heterogeneities in order to provide comprehensive solutions.