Speaker: Dr. Dixon Vimalajeewa
From: Texas A&M University
Abstract
The recent advancements in Internet of Things (IoT), Internet of Nano-Things (IoNT), and Information and Communication Technologies have made it possible to collect large amounts of data from previously inaccessible locations, such as the human body, at a higher sampling rate. However, the complex nature of this data presents challenges for extracting valuable insights using existing data processing techniques, including scalability, interpretability, and generalizability. As a result, advanced machine learning techniques are required to overcome these obstacles.
In this talk, I will present three research schemes to address these challenges: high-frequency data analysis, distributed data processing, and mathematical modeling, including some of the techniques that I have proposed recently. These techniques are applicable to several fields, including biomedical engineering, precision farming, and personalized healthcare. Furthermore, I will discuss how these approaches can be utilized in these fields to unlock the full potential of the vast amounts of data being collected and extract valuable insights to drive progress in these areas.
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