When: June 15, 2023 Where: University of Arkansas, Fayetteville Research Theme:Learning and Prediction DART Faculty participants:Khoa Luu, UAF DART Graduate Student participants:Thanh-Dat Truong, UAF Researchers at the University of Arkansas have developed a prototype of an insect trap that can help farmers monitor and identify potential pests more efficiently in order to protect valuable crops….Continue Reading Arkansas Researchers Invent AI-Enabled Technology to Monitor Insects & Pests in Real Time for Precision Pesticide
Tag: Learning and Prediction
DART Seed Grant Recipient Presentations, Session 2
Title: DART Seed Grant Recipient Presentations, Session 2Presenter(s): Yasir Rahmatallah, Jason Causey, Sudeep Bhattacharyya, and Melody GreerDate: October 26, 2022 Description This month’s webinar will feature three presentations from the recently awarded DART YR 3 Seed Grant Recipients. Yasir Rahmatallah (UAMS) will present on Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson’s Disease….Continue Reading DART Seed Grant Recipient Presentations, Session 2
DART Seed Grant Recipient Presentations, Session 1
Title: DART Seed Grant Recipient Presentations, Session 1Presenter(s): Xiao Huang and Ahmed Abu HalimehDate: September 28, 2022 Description This month’s webinar will feature two presentations from the recently awarded DART YR 3 Seed Grant Recipients. Dr. Xiao Huang (UAF)will present on AI-Supported Cyberinfrastructure for Scalable Flood Resilience Assessment. This project is part of the CI…Continue Reading DART Seed Grant Recipient Presentations, Session 1
Students and Faculty Attend Top AI Conference in New Orleans
When: June 19 – 24, 2022 Where: New Orleans, LA Research Theme:Learning and Prediction DART Faculty participants:Khoa Luu, UAF DART Student participants:Thanh-Dat Truong, UAFHan-Seok Seo, UAFNgan Le, UAF Students and faculty from the Computer Science and Computer Engineering Department attended the 35th IEEE/CVF Computer Vision and Pattern Recognition Conference, which is the highest ranking conference…Continue Reading Students and Faculty Attend Top AI Conference in New Orleans
Prediction and learning for designing materials for solar energy conversion
Title: Prediction and learning for designing materials for solar energy conversionPresenter(s): Rob CoridanDate: February 23, 2022 Description Materials for converting solar energy into chemical fuels requires balancing the many physical and chemical steps involved (light absorption, catalysis, product separation, diffusion of reactants and products) along multiple length scales (from single atoms to m2). This balance…Continue Reading Prediction and learning for designing materials for solar energy conversion
Precognition: Seeing through the Future
Title: Precognition: Seeing through the Future Presenter(s): Khoa Luu Date: January 26, 2022 Description Vision-based detection and recognition studies have been recently achieving highly accurate performance and were able to bridge the gap between research and real-world applications. Beyond these well-explored detection and recognition capabilities of modern algorithms, vision-based forecasting will likely be one of the next…Continue Reading Precognition: Seeing through the Future
Deep Learning in Biomedical Imaging
Title: Deep Learning in Biomedical Imaging Presenter(s): Ngan Le Date: December 1, 2021 Description Medical image analysis using deep learning has recently been prevalent, showing great performance for various downstream tasks including medical image segmentation and its sibling, volumetric image segmentation. Particularly, a typical volumetric segmentation network strongly relies on a voxel grid representation which…Continue Reading Deep Learning in Biomedical Imaging
Learning-based Approaches to Data-driven Predictions
A major challenge in building secure and widely adopted deep learning systems is that they sometimes make wrong, unexplainable, and/or unpredictable misclassifications. This talk overviews initial efforts towards techniques using large-scale deep learning with multi-source integrated data sets. In addition, we introduce the integration of statistical learning approaches with learning-based frameworks….Continue Reading Learning-based Approaches to Data-driven Predictions