- 10/24/2018 New paper: Kallumadi et al., RecSys Challenge 2018
- 10/24/2018 New paper: De La Torre et al., KDIR 2018
- 10/24/2018 New paper: Yates et al., IJCAI AffComp 2018
- 10/24/2018 New paper: De La Torre et al., IJCAI CogVis 2018
- 6/23/2018 New paper: Kallumadi et al., UMAP 2018 Adjunct
- 4/14/2018 New paper: IUI ESIDA 2018, Aguirre et al.
- 3/11/2018 New paper: MLDS 2017, Aguirre et al.
- 10/17/2017 Alumni Dissertations and Theses Online
- 9/24/2017 New to the Group? Welcome! Go here first:
- 8/11/2017 Congratulations to spring, 2017 graduates
The Laboratory for Knowledge Discovery in Databases (KDD) is a research group in the Computing and Information Sciences (CIS) Department at Kansas State University. Its research emphasis is in the areas of applied artificial intelligence (AI) and knowledge-based software engineering (KBSE) for decision support systems.
More specifically, we are interested in machine learning, data mining and knowledge discovery from large spatial and temporal databases, human-computer intelligent interaction (HCII), and high-performance computation in learning and optimization. In our research, we look for ways to systematically decompose analytical learning problems based upon information theoretic and probabilistic criteria, so that the most appropriate machine learning methods may be applied to the resulting transformed problems.
One of the major challenges in this area is the design of unsupervised learning and bias (or hyperparameter) optimization methods to produce an effective decomposition of learning tasks. An interesting opportunity presented by this problem is that, by addressing the high-level control of inductive learning in a statistically sound fashion, we can improve our techniques for both model selection and model integration (as practiced in multimodal sensor fusion). We have developed and applied such approaches to multistrategy learning, which are potentially computation-intensive, to interesting analytical problems in the areas of decision support (uncertain reasoning) and control automation.
The goal of our work is to gain insight into the interaction between artifacts that adapt or learn - whether by Bayesian, neural, or genetic computation - and their users. Important examples of this interaction include data visualization in intelligent displays, software agents for distributed high-performance computation and information retrieval, and virtual environments for simulation and computer-assisted instruction.