Category: News

Dr. David LeBauer, the director of data science for the Arizona experiment station at University of Arizona gave a talk about how to integrates theory with observation to optimize food an fuel production. He will talk about the challenges opportunities for agriculture in the digital age and give examples about his research.

This Wednesday, Alina Zare presented a talk on Machine Learning and Applications in Remote Sensing in Global Centra Spring 2019 Webinars. Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label. However, obtaining accurate training label information is often time consuming and expensive, making it infeasible for […]

Gators giving back! In February, the UF Machine Learning Lab hosted a two hour workshop for GatorTrax, a free K-12 outreach program run by the UF College of Engineering and honor society. Conner McCurley, Susan Meerdink, and Weihuang Xu helped with a youth STEM outreach program called GatorTRAX and taught 40 kids about machine learning […]

Congratulations to James Bocinsky who defended his Master’s thesis titled “Learning Multiple Target Concepts From Uncertain, Ambiguous Data Using The Adaptive Cosine Estimator and Spectral Match Filter” this week on Monday, March 11th. His research focused on developing algorithms for explosive hazard detection. His thesis included investigating multiple instance learning, clustering, and various target detection […]

Our labmates Zou Sheng and Weihuang Xu recently presented their work on peanut maturity estimation and root segmentation at Plant Phenomics in Tucson, AZ. They did a great job presenting their work – and both received a 2019 Travel Award!

Abstract: Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL […]

The Machine Learning and Sensing Lab is excited to welcome our newest lab member Susan Meerdink! Susan got her M.S. and Ph.D. degrees from University of California Santa Barbara. She studies the ability to map plant species across seasons in diverse ecosystems and uses various technologies to study plant health across environmental gradients and physiology’s […]

Our labmate, Connor McCurley, was chose as the Graduate Student of the Week for the week of October 8th. He will talk about Machine Learning and Sensing Lab and also his research in the past years. Congratulations, Connor!

The Machine Learning and Sensing Lab is excited to welcome our newest lab members Alyssa Bowles, Caleb Robey, Layiwola Lawrence Ibukun, Jake Samuelson, Ross Spencer, Val Christian and Yutai Zhou! They will be working on : Alyssa, Ross, and Val will be working on our project in supervised learning from incomplete and uncertain data funded […]

Congrats to our labmate Joshua Peeples for receiving a 3 year travel award to attend the 2018 Southern Regional Education Board’s Institute on Teaching and Mentoring conference , held on October 25-28, 2018!