Intelligent Systems Lab
ISL research is focused on learning high quality models from data. A focus is building models that generalize to unseen sources. Deep learning with adaptations for smaller datasets, especially medical image sets, is explored. Unlabeled data is modeled with clustering algorithms. Mixtures of labeled an unlabeled data are addressed with semi-supervised learning approaches. Of particular interest is big data for which Deep Neural Networks are often useful. Ensembles of different (and the same) types of models are considered, imbalanced data is continually addressed. Imprecision in intelligent systems is also a research topic. Some recent work focuses on learning prognostic models from medical images and clinical data, learning models of activity in very large information networks and clustering data in a network environment. An overall goal is to be able to group large sets of unlabeled data in useful ways, uncovering small, but important groups where they exist. Another goal is to be able to make accurate predictions from potentially large (at least partially) labeled data sets.
Some select recent
paper titles are below. You can find a full list of papers at
Hall Google Scholar Page
If you do not find a paper, please just ask.
Discovery of a generalization gap of convolutional neural networks on COVID-19 X-rays classification KB Ahmed, GM Goldgof, R Paul, DB Goldgof, LO Hall IEEE Access, 72970-72979, 2021
A radiogenomics ensemble to predict EGFR and KRAS mutations in NSCLC S Moreno, M Bonfante, E Zurek, D Cherezov, Dmitry Goldgof, LO Hall, M. Schabath - Tomography, 2021
VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media Networks F Mubang, LO Hall - IEEE Transactions on Computational Social Systems, 2022
Challenges for the repeatability of deep learning models SS Alahmari, DB Goldgof, PR Mouton, LO Hall - IEEE Access, 2020
Lab Director: Lawrence O. Hall