TBD
An approach to estimate real-world energy variability by leveraging commonly used smart building sensors such as PIR, door contact, acoustic noise, and illuminance sensors.
Reliable Detection of Intermittent Events with Energy Harvesting Sensors
An intuitive, device-free way to engage with IoT devices in a smart home setting. Uses BLE, WiFi, UWB, and Machine Learning.
A device-free solution for occupancy detection by leveraging WiFi Fine-Time Measurement~(FTM) signals, with enough resolution to detect stationary and moving occupants on single-antenna WiFi devices.
Developed a tool to monitor the air handling systems in smart labs to reduce energy consumption due to air changes while also making sure Smart Labs Code was met.
Created roadmaps, upgrade guidance, and performed energy audits to decarbonize UVA buildings based on an energy usage pattern classification scheme.
Evaluated Neural Network (NN) and non NN classifiers to predict if a tweet was made by a human or a bot.