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:
- Statistical Learning – Random Forests (RF) for Recurrent Event Analytics
- Statistical Learning – Marked Temporal Point Process Enhancements via Long Short-Term Memory Networks
- Deep Learning – Novel Approaches
- Deep Learning – Efficiency and Specification
- 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.
- Violation of fundamental statistics principles
- Mode specification and interpretation
- 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.