Machine Learning and Artificial Intelligence at EECE

The availability of massive amounts of data and increased computational capacity have recently led to unprecedented breakthroughs in machine learning (ML) methods. The machine learning and artificial intelligence (AI) research group at the Department of Electrical and Computer Engineering is a vibrant community of faculty members, graduate students, and undergraduates who are pushing the envelope of what can be accomplished with ML and AI. This group discovers ML and AI solutions for a variety of high-impact problems across a range of domains. As part of Marquette's tradition of commitment to the highest standards of graduate and undergraduate education, we offer a range of courses that provide a comprehensive foundation and hands-on experience on state-of-the-art methods and technologies for the design of ML and AI systems.

Undergraduate Research

A robust Research Experience for Undergraduate Students program funded by the National Science Foundation as well as by fellowships from the Opus College of Engineering provide opportunities for undergraduates to be directly involved in these activities and make their own contribution to help define the future of machine learning and artificial intelligence.

Graduate Research

The machine learning and artificial intelligence research group is a collaboration of three research laboratories that focus on cutting-edge research. There are many opportunities for graduate students to participate in active research projects. A brief overview of the focus of each lab follows.

Knowledge and Information Discovery (KID) Lab

Dr. Povinelli's KID lab focuses on research at the intersection of machine learning and signal processing. We have applied machine learning and signal processing in numerous application areas, including detecting pavement defects, determining if a metal part is correctly forged, predicting energy demand, and estimating state transition probabilities for blackouts in the electric grid. Learn more about the KID Lab.

System Analytics for Communications and Energy (SACE) Lab

Dr. Hayat research activities cover a broad range of topics including resilience and reliability of interdependent cyberphysical systems, dynamical modeling of cascading phenomena with applications to power systems, avalanche photodiodes, statistical communication theory, signal and image processing, algorithms for spectral and radar sensing and imaging, optical communication, and networked computing. Learn more about the SACE Lab.

Marquette Embedded Systems (MESS) Lab

Dr. Ababei and his graduate students in the MESS Lab develop novel deep machine learning models and apply them to developing algorithms for the design and optimization of embedded systems, for performance improvement and energy usage reduction in multicore processors and datacenters, for state of charge balancing in battery packs, and for energy optimization and cost reduction in smart buildings. Learn more about the MESS Lab.

Data-intensive Computing Distributed Systems Lab (DiCDSL)

Dr. Deshpande’s DiCDSL’s mission is to significantly improve people's lives through our work in Artificial Intelligence. DiCDSL focuses on designing, developing and implementing databases, machine learning, data science and artificial intelligence algorithms for data-intensive applications. The research conducted in our group is applied to various domains, including biomedical and health care informatics, neuroscience, sports, security, and space technology data. Learn more about the DiCDSL Lab.