The UT SNR ‘Data Science Institute for Machine Learning and AI (DSIMLA)’ aims to promote and advance the education and research of data science, machine learning, and AI for companies engaged in forest management, cellulosic fiber utilization for forest products manufacturing, and biomaterials processing. Additionally, the Institute seeks to enhance the education and research of data science, machine learning, and AI for forest business and management companies. The goal of the Institute is to assist companies in learning, adapting, and effectively implementing the latest data science, machine learning, and AI technologies to optimize processes, leading to improved efficiency, utilization, energy savings, and cost reduction. The Institute operates as an expansion of a company’s innovation group by providing access to the most current technologies in the rapidly evolving field of data science, machine learning, and AI.
Education and Research Objectives
DSIMLA has the following education and research objectives:
- Expand the knowledge of a company’s workforce in statistical and data analytical methods as applied to manufacturing for the purpose of variation reduction;
- Enhance a company’s knowledge of analytical software to support continuous improvement efforts in data analytics and variation reduction;
- Promote learning and networking with virtual webinars, in-person workshops, and an annual conference;
- Establish a ‘Machine Learning (ML) Cooperative’ (ML Coop) to advance the direct application of the most contemporary ML algorithms for real-time prediction of key process parameters and product quality attributes;
- Within the ML Coop, the core principles of Total Quality data Management (TQdM) will focus on implementing TQdM for assessing relational databases created from data fusion algorithms;
- Conduct applied research in data science, machine learning, and AI to support the enhanced optimization of forest products and sustainable biomaterials processes;
- Conduct applied research in data science, machine learning, and AI to support improved forest management for forest businesses.