Funded Projects

We study the overhearing problem of continuous acoustic sensing devices such as Amazon Echo and Google Home, and develop a smart cover that mitigates personal or contextual information leakage due to the presence of unwanted sound sources in the acoustic environment.

Supported by: NSF, 3 yr, 10/2018 - 9/2021, $252K, Role: Lead PI with Co-PI Jiang (Columbia).

A wearable head-set that combines four MEMS microphones, signal processing and feature extraction electronics, and machine learning classifiers running on a smartphone to help detect and localize imminent dangers such as approaching cars in real-time, and warns inattentive pedestrians.

SSupported by: NSF, 4 yr, 6/2017 - 5/2021, $320K, Co-PI with PI Jiang (Columbia).

This project takes a data-driven approach to understand the relationships among all IoT devices in a smart-home environment. Given two IoT devices, the goal is to devise algorithms that automatically identify correlations in their data and inference streams, for a given private activity context.

Supported by: NCDS Data Fellowship, 1 yr, 7/2016 - 6/2017, $50K, Role: Lead PI.

A low-cost, distributed acoustic sensing platform and a combination of unsupervised and supervised machine learning techniques with a human-in-the-loop are employed to discover, learn, identify, and predict HVAC faults and failures.

Supported by: NSF EAGER, 1 yr, 3/2016 - 2/2017, $20K, Co-PI with PI Srinivasan (UFL).