Category: News

Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data. We examine the sensitivity to factors in the fine tuning process such as […]

Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there are many approaches to quantify performance in standard crisp segmentation schemes, drawing hard boundaries in […]

Congratulations to our labmate, Connor McCurley, for passing his oral qualifying exam and becoming a PhD candidate!  For the remainder of his Ph.D. work, Connor plans to investigate “Discriminative Manifold Embedding with Imprecise, Uncertain and Ambiguous Data.” Great work, Connor!

Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in […]

Congratulations to our labmates: Susan K. Meerdink, James Bocinsky, Alina Zare, Nick Kroeger, Connor H. McCurley, Daniel Shats and Paul D. Gader!  Their paper, “Multi-Target Multiple Instance Learning for Hyperspectral Target Detection” was recently accepted to IEEE Transactions on Geoscience and Remote Sensing (TGRS).   In their paper, the authors introduce an approach to estimate multiple […]

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Daniel Bertak!  Daniel is majoring in Computer Engineering at the University of Florida and will be investigating machine learning methods for plant root analysis. Welcome to our lab, Daniel!

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Hayden Menge!  Hayden is majoring in Computer Engineering at the University of Florida and will be investigating machine learning methods for agricultural applications. Welcome to our lab, Hayden!

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Aaron Zygala!  Aaron is pursuing a Master’s degree in Computer Science at the University of Florida and will be working on a new project in collaboration with Dr. Steven Weisberg in the Psychology department. Welcome to our lab, Aaron!

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Aditya Sai Pravarshi Kolluri!  Aditya is pursuing a Master’s degree in Mechanical Engineering at the University of Florida and will be working on our NASA project. Welcome to our lab, Aditya!

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Arielle Stevens!  Arielle is pursuing a Master’s degree in Electrical and Computer Engineering at the University of Florida, and will be working on our Army Research Office project. Welcome to our lab, Arielle!