Category: News

Congratulations to our labmates and collaborators: Ricardo Augusto Borsoi, Tales Imbiriba, Jose Carlos Moreira Bermudez, Cedric Richard, Jocelyn Chanussot, Lucas Drumets, Jean-Yves Tourneret, Alina Zare and Christian Jutten!  Their publication, “Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review” was recently accepted to the IEEE Geoscience and Remote Sensing Magezine. In their paper, the authors […]

The Machine Learning and Sensing Lab is excited to welcome Ritesh Chowdhry as a new Ph.D. student! Ritesh will receive his MS degree in Electrical & Computer Engineering from the University of Florida in May 2021. In our lab, Ritesh will be working on machine learning techniques for use in agronomic applications. Welcome to our […]

Congratulations to our labmate, Sarah Walker! Sarah presented her work, titled “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification” at UF’s 2021 Undergraduate Research Virtual Symposium.  The virtual symposium featured outstanding undergraduate researchers across all colleges at UF. You can get more information about Sarah’s talk here and can check out the paper […]

Abstract: In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples in a low-dimensional embedding space. We compare the learned sample embeddings against coordinates found by […]

Congratulations to Gatorsense alumnus, Daniel Suen!   Daniel was recently awarded a Graduate Research Fellowship by the National Science Foundation to fund his Ph.D. work in Statistics at the University of Washington.    We are so proud of you, Daniel.  Keep up the great work!  

Congratulations to our labmates and collaborators: Diandra Prioleau, Kiana Alikhademi, Armisha Roberts, Joshua Peeples, Alina Zare and Juan Gilbert!  Their paper, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data” was recently accepted to the the 2021 International Conference on Machine Learning and Data Mining (MLDM). In their paper, the authors propose the use […]

Abstract: A lack of diversity and representativeness within training data causes bias in the machine learning pipeline by influencing the performance of many machine learning models to favor the majority of samples that are most similar. It is necessary to have diverse and representative training data, especially for application domains in which people of varying […]

Congratulations to Dr. Sheng Zou for graduating with his Ph.D.! Sheng’s dissertation is titled “Classification with Multi-Imprecise Labels.” His research focused on developing classification approaches under the multiple instance learning framework. The goal of Sheng’s work was to model class variability using discriminative probabilistic distributions and multiple types of imprecise labels. Read about more Sheng’s […]

Congratulations to our labmates: Sarah Walker, Joshua Peeples, Jeff Dale, James Keller and Alina Zare!  Their paper, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their paper, the authors provide an in-depth analysis to the factors that affect performance of texture […]

Congratulations to our labmates: Dylan Stewart, Anna Hampton, Alina Zare, Jeff Dale and James Keller!  Their paper, “The Weakly-Labeled Rand Index” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their paper, the authors introduce an approach to quantify superpixel segmentation performance. Whereas traditional evaluation approaches require crisp segmentations, the […]