Faculty in Electrical & Computer Engineering (Tenure Track/Tenured)

The Department of Electrical & Computer Engineering invites applicants as part of a multi-position hiring initiative for Data-Driven AI & Its Transformative Impact on Special Education.

We seek faculty with expertise in Electrical & Computer Engineering or a related field. They are expected to lead the development of novel theory, state-of-art algorithms, and architectures for learning and real-time applications in human and machine-centered interaction and recognition, behavioral and neuro-cognitive deficits, and biomedical imaging and signal analysis, based on the disciplines of computer vision, signal/image processing, and AI/machine learning. This appointed is expected to be at the rank of Assistant Professor, but an appointment at a higher rank will be considered for exceptionally qualified candidates.

This faculty member will develop/maintain a vibrant, externally funded interdisciplinary research program in artificial intelligence (AI)/machine learning (ML) and data science. This research program will have focused application in special education and overall education areas, including emotional intelligence with earlier and better understanding, AI-driven adaptive learning systems, AI-driven therapeutic systems, immersive virtual learning environment, predictive analytics on behavior data and symptomology and AI-driven visual perception for special education. Collaboration with other faculty in the Electrical and Computer Engineering, the Department of Special Education, the School of Data Science, the Institute of Data Science, and the Vision Lab is expected.

Other Responsibilities:

The focus of this cluster hire is the interdisciplinary area of data-driven AI and its transformative impact on special education that serves people with various chronic health conditions and disabilities. AI and Machine Learning (ML) techniques are poised to significantly change many practices in special education, from emotional intelligence with earlier and better understanding of behavioral and neuro-cognitive deficits. Development of AI-driven adaptive learning systems, AI-driven therapeutic tools, immersive virtual learning environment and predictive analytics may benefit from availability of large-scale behavior and neuro-cognitive data. A few well-recognized examples include use AI-based computer vision techniques to recognize and identify subtle visual signs for early detection of autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), developmental disabilities, and Alzheimer and other dementia.