Prof. Joydeep Ghosh of UT ECE was the keynote speaker at the inaugural Workshop on Divergences and Divergence Learning (WDDl), held in Atlanta, June 2013. In his talk, entitled "Learning Bregman Divergences for Prediction with Generalized Linear Models," which reflects joint work with ECE and WNCG student Sreangsu Acharrya, an efficient approach to learning a broad class of predictive models was introduced. What is most remarkable about this approach is that model parameters can be estimated even when the loss function is unknown. This breakthrough enables one to construct predictive models in both online and batch settings, for certain complex problems for which standard costs such as squared loss are inappropriate.
Prof. Ghosh will also present the keynote address at the International Workshop on Data Mining for Healthcare (DMH), Philadelphia, Sept 11, 2013. The talk, entitled "Predictive Modeling of Large Healthcare Data under Privacy Constraints," will address the fundamental tension between the need to extract value from large quantities of health-related data and the desire to maintain privacy of patients and caregivers. He will discuss two approaches that provide privacy-aware predictive modeling with little degradation in model quality despite restrictions on what can be shared or analyzed. The first approach focuses on extracting predictive value from data that has been aggregated at various levels due to privacy concerns, while the second introduces a novel, non-parametric Gibbs sampler that can generate "realistic but not real" data given a dataset that cannot be shared as is. This is joint work in conjunction with ECE and WNCG student Yu Bin Park.