Research in this topical area will focus on various techniques in prediction interpretation for large-scale, deep learning using multi-source integrated data sets. Some goals include:

  1. Statistical Learning – Random Forests (RF) for Recurrent Event Analytics
  2. Statistical Learning – Marked Temporal Point Process Enhancements via Long Short-Term Memory Networks
  3. Deep Learning – Novel Approaches
  4. Deep Learning – Efficiency and Specification
  5. Harnessing Transaction Data through Feature Engineering

Advancing the State of Knowledge

A major challenge in building secure and widely adopted deep learning systems is that they sometimes make wrong, unexplainable, and/or unpredictable misclassifications. In addition to confusing examples of very different classes, they are also vulnerable to adversarial examples. These systems are often trained as large feed-forward error-back propagating black boxes and thus we have no way of interpreting the meanings of their features and understanding the causes of misclassifications, a situation that can be exploited by attackers. Research in this theme will focus on applying statistical learning techniques alongside more advanced deep learning techniques to address three major challenges.

  1. Violation of fundamental statistics principles
  2. Mode specification and interpretation
  3. Computing in big data environments

We will investigate these challenges surrounding high-dimensional, dynamic, and unstructured data sets and explore solutions in the domains of genomics, transaction scenarios in eCommerce, and supply chain logistics.