Artificial Intelligence and Big Data

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Advances in computing capabilities, access to large datasets from networked devices and sensors and development of innovative algorithms have accelerated the impact of Artificial Intelligence (AI) on our daily lives.  This technology will drive creative solutions for many societal issues in areas such as biomedicine, health care delivery, security, cybersecurity and transportation.  For example, genomic sequences, medical images and biomedical signals, social network data, financial data, educational data, remote sensing data, and many other types are all part of the era of big data. In order to understand and extract meaning from such data, and to engineer new systems based on the insights from big data, there is a need for research into fundamental algorithms, and systems for handling and harnessing big data, such as AI and machine learning.

Current ECE Focus:

Current ECE faculty are mainly focused on the algorithmic aspects of big data and informatics, namely the design of AI, statistical signal processing, data mining, data fusion, machine learning, image processing, game theory, and computer vision methods for processing various types of signals and data with different levels of quality and volumes.  For example, faculty are working on developing AI methods for analyzing images and video including medical images. Faculty are also working on the analysis of genomic data for the detection of cancer gene markers, the detection of toxicity and pathogens in the water supply, face recognition for security applications, and other problems of importance.

Opportunities for Interdisciplinary Collaboration:

Faculty in ECE are currently developing novel signal and image processing methodologies for use in collaborative research with faculty in the Miller School of Medicine and faculty in Arts & Sciences on interdisciplinary research questions.  By having additional faculty in this strategic thrust, the ECE Department will be able to expand such collaboration by offering additional problem solving dimensions (e.g., design of algorithms and hardware/software systems for understanding medical data that is subject to privacy, and security requirements as well as federal regulations).

COE Thrust Supported: 

Health Care Engineering and Data Sciences  

Graduate Courses Offered: 

ECE 637 (Principles of Artificial Intelligence), ECE 648 (Machine Learning), ECE 653 (Neural Networks), ECE 674 (Agent Technology), ECE 677 (Data Mining), ECE 696 (Game Theory & Online Learning), ECE 730 (Statistical Learning), ECE 753 (Pattern Recognition and Neural Networks), ECE 79x (Advanced Big Data Analytics)

Number of PhD Students and Research Personnel: 7

Faculty Participants:

Coordinator: Mohamed Abdel-Mottaleb
ECE Members: Mohamed Abdel-Mottaleb, James M. Tien, Xiaodong Cai, Mei-Ling Shyu, Kamal Premaratne, Manohar Murthi, Miroslav Kubat, Jie Xu

Other Department & Schools Participants: Odelia Schwartz and Liang Liang (Computer Science), Vittorio Porciatti and Richard K. Lee (Ophthalmology, Cell Biology, and Neuroscience), Daniel S. Messinger and Lynn K. Perry (Psychology), Chaoming Song (Physics)

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