09/19/2022
By Erin Caples

The Francis College of Engineering, of Electrical & Computer Engineering, invites you to attend a doctoral Dissertation Defense by Varun Garg on Spatio-Temporal Data Analytics Using Participatory Sensing.

Ph.D. Candidate Varun Garg
Thursday, Oct 13, 2022
1:30 - 3:30 p.m.

This will be a virtual defense via Zoom. Those interested in attending should contact the student (Varun_garg@student.uml.edu) or the committee advisor (Thanuka_Wickramarathne​@​uml.edu) at least 24 hours prior to the defense to request access to the meeting.

Advisor: Thanuka Wickramarathne, Assistant Professor, Dept. Electrical and Computer Engineering, UMass Lowell

Committee Members:
Yan Luo, Professor, Dept. Electrical and Computer Engineering, UMass Lowell
Vinod Vokkarane, Professor, Dept. Electrical and Computer Engineering, UMass Lowell
Peter Bauer, Professor, Dept. Electrical Engineering, University of Notre Dame

Brief Abstract:
With the proliferation of the Internet of things, the availability of sensors has increased exponentially. Omnipresent sensors can be utilized to participate in secondary sensing tasks. 'Participants' use devices (e.g., smartphones, vehicles, roadside units) to collect 'Spatio-temporal data', providing opportunities to enhance the situational awareness of sensing systems. The enhanced situational awareness will help solve problems in areas such as intelligent transportation systems, civil infrastructure management and urban surveillance.

The developed systems process Spatio-temporal data to detect and predict attributes related to `targets' of interest. Our work focuses on developing data fusion and machine learning based methods for analyzing Spatio-temporal data belonging to the targets.

In particular, we develop novel methods for processing data streams for detection and prediction of the targets of interest, such as road conditions, target vehicle trajectory and potential threats. We utilize the Spatio-temporal data from multiple participatory sensors and systematically aggregate them to improve the overall performance of the sensing system. The developed methods work towards circumventing the challenges posed by measurement noise, mobility and differences among sensors.

All interested students and faculty members are invited to attend the online defense via remote access.