Dr. Asif Salekin of Syracuse University

Supported by rapid innovations in machine learning, signal processing, computing, and wearable-systems, the concept of smart and connected sensing is leading to redesign almost every aspect of our lives. Innovating novel, low cost and noninvasive sensing techniques to model/identify an individual’s behavioral or physiological states. However, the accuracy of detecting or sensing human events is often not adequate to have any practical use. This is due to the lack of adaptability of the current state-of-the-art machine learning and data analytics techniques, with the characteristics and constraints of real-word sensing applications. This talk aims to demonstrate sensing and data analytics approaches that address the challenges of real-world applications, such as the uncertainties in physical world sensing, human factors (e.g., user context and mobility), the limitation of current technologies, and resource constraints of the sensing data and computation platform. An example application to be presented is the detection of mental disorders from weakly labelled data. 

Biography:

 Dr. Asif Salekin is an assistant professor in the Department of EECS at Syracuse University. He has a Ph.D. in Computer Science from the University of Virginia, receiving an Outstanding Research award.  His research takes a multi-disciplinary approach to develop novel and practical human event sensing technologies that capture observable low-level physical signals from human bodies and surrounding environments and employ new machine learning, signal processing and natural language processing techniques. He has published at the leading conferences in ubiquitous computing, connected health, and wireless sensor networks. His work, AsthmaGuide, was nominated for the best paper award in the Wireless Health 2016 conference. 

Email:

Address: 4-297 CST, Syracuse University, Syracuse, New York, United States