Artificial Intelligence and Machine Learning

Self-driving cars, intelligent assistants, and smart-home devices—we work to improve and discover AI technology in the fields of energy, finance, health care, education, transportation and more. We develop interactive decision-making tools, and create algorithms to respond to interdisciplinary fields where uncertainty is present: from construction management to the Mars rover. We investigate complex issues surrounding student persistence, and dig deep into machine learning.

"Students take a construction plan and overlay it with events that cause delays. Then we ask students to react to the scenarios."Nilufer Onder, associate professor, computer science, on developing ICDMA (interactive construction decision-making aid)

Current Project

Project Title: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity

Investigators: Laura Brown

Sponsors: National Science Foundation

Overview: This CRISP (Computer Retrieval of Information on Scientific Projects) project addresses the challenges associated with the rapid evolution of the electricity grid to a highly distributed infrastructure. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels for delivering electricity to customers, to distribution microgrids, entities that actively manage local production, storage and use of electricity, with participation from individual customers. Distribution microgrids combine the advantages of the traditional electricity grid with the advantages of emerging distributed technologies, including the ability to produce and use power locally in the event of grid outages. The project will result in a unified model that incorporates key aspects of power generation and delivery, information flow, market design and human behavior. The model predictions can be used by policymakers to guide a transition to clean energy via distribution microgrids. The expectation is to enable at least 50% of electric power to come from renewable resources. This cannot be done with either the traditional grid, due to its limited capacity to accommodate intermittent renewable power sources, or with fully decentralized approaches, which would not be affordable for most utility customers.

Researchers

Laura E. Brown

Associate Professor, Computer Science
Associate Dean of Data Science Initiatives, College of Computing
Director, Data Science, M.S. and B.S. Programs

lebrown@mtu.edu
906-487-3472
Rekhi Hall 307

Links of Interest

Area of Expertise

Artificial Intelligence and Machine Learning
Data Mining and Data Science
Applications of AI and ML to Energy (microgrids, power systems), Health, and other domains