Abstract: Modern environments are alive with sensors, such as smartphones, wearables, vehicles, and cameras. However, despite the plethora of sensors, direct sensing of desired events is difficult in the real-world. This is primarily due to large amounts of noise introduced by our movements during everyday tasks, in addition to lack of dedicated sensors and deployment difficulties. We address this challenge by modeling movements to design and develop mobility-aware systems. This has opened up new opportunities in both urban tech and mobile health.

 

This talk will explore our research efforts in leveraging pervasive sensing devices, towards developing a multi-modal data integration and interpretation framework. In the first half, I will discuss sensing frameworks for urban spaces, particularly for camera virtualization and pedestrian safety. In the second half, I will present how we have expanded our techniques to the mHealth space to develop AI solutions. We have designed real-time monitoring tools that can capture fine-grained measures of users’ health in uncontrolled environments. In building these data-driven frameworks that introduce mobility-awareness in non-homogeneous sensors, we introduce new avenues for smart cities and healthcare analytics in the real-world. 

 

Bio: Shubham Jain is an Assistant Professor in the Department of Computer Science at Stony Brook University where she leads PiCASSo (Pervasive Computing and Smart Sensing) Lab. Her research interests lie in cyber-physical systems, mobile health, and data analytics in smart environments. She received her PhD in Electrical & Computer Engineering from Rutgers University in 2017. She is currently the PI on three NSF grants and an academic-industrial partnership. She is actively looking for students to join her group.