As a primary focus, we use a combination of machine learning and end-to-end systems development to h
10/21/2022
Our deep-learning summary metric demonstrated that each K2 transfemoral amputee in a recent study showed improvement in prosthetic performance using a microprocessor-controlled knee relative to a mechanical knee, which may influence the inconsistent support for such knees in K2 subjects. Great work by Thasina Tabashum working with data from the Jayaraman lab. https://www.mdpi.com/2306-5354/9/10/572/htm
10/13/2022
Interested in how your brain processes sensory information? Download our app! Our team created an accessible, fun android app to demonstrate how efficient coding relates to how our brains process sensory information. Take pictures, record audio, and see how neurons in your brain might respond to images and sounds if you were evolved to process those rather than our natural world.
https://play.google.com/store/apps/details?id=com.biomedai.neuroscience
03/18/2022
Early publication of Trevor Exley’s BME MS thesis work “Predicting UPDRS Motor Symptoms in Individuals with Parkinson’s Disease from Force Plates Using Machine Learning” in IEEE J Biomedical and Health Informatics showing utility of quiet standing data in measuring the severity of PD.
https://ieeexplore.ieee.org/document/9730065
03/02/2022
Our book is out now! “Bridging Human Intelligence and Artificial Intelligence” published by Springer. Wonderful team effort between editors Mark Albert, Lin Lin, Michael J. Spector, and Lemoyne Dunn and the authors of all 24 chapters including over 20 faculty authors, 20+ graduate student coauthors, and 14 TAMS students.
https://link.springer.com/book/10.1007/978-3-030-84729-6
12/15/2021
Want a hands-on approach to understanding computational principles in early sensory processing? Check out Namratha Urs, Sahar Behpour, and Angie Georgaras’s paper & notebook published in AI Reviews “Unsupervised learning in images and audio to produce neural receptive fields: a primer and accessible notebook”
https://link.springer.com/article/10.1007/s10462-021-10047-7
12/14/2021
We’re presenting at 2021 today! & accepted paper “A collaborative and adaptive feedback system for physical exercises” using reinforcement learning for tailored feedback bringing proper form. Presented by Ishan Ranasinghe with Chengping Yuan and Ram Dantu.
12/12/2021
UNT’s first group of MS in Artificial Intelligence graduates. Had a nice dinner last week to celebrate. Looking forward to attending the graduation today, then they’re off to , , , and more
11/09/2021
Presented at : Reducing Health Disparities (Transdisciplinary Ancestral Genomics Research Investigations) with Heather Wheeler “... assessment of Mobility in Diverse Patient Populations” followed by “Transcriptome prediction performance across … diverse ancestries”
11/05/2021
Glad to talk about our submitted book “Bridging Human and Artificial Intelligence” and the process we used to engage the 60 coauthors in it’s production in the past year. Presented yesterday at and to be published by Springer in Dec 2021.
Kudos to editors Mark Albert, Lin Lin, J. Michael Spector, and Lemoyne Dunn for bringing this all together.
11/05/2021
Want a class project to be “plugged in” to the university network? Vandana Nunna and Lin Lin presented our prototype portal this week at to manage class project selection and coordination across multiple courses and students for sponsors at UNT.
07/30/2021
Just wrapped up this summer's NSF REU on Accelerated Deep Learning. Great projects & sync with the UNT AI Summer Program. Goodbye to the 10 REU students that worked with P*s Hui Zhao and Mark V. Albert. More to be posted as their DL projects get published. Glad to be a part.