Xiaofan (Fred) Jiang
Jiang’s research lies at the intersection of systems and data, with a focus on intelligent embedded systems and their applications in mobile and wearable computing, intelligent built environments, Internet of Things, and connected health.
In the urban safety space, Jiang’s team is developing an intelligent wearable system that uses miniature microphones embedded in earphones or headsets, together with novel machine-learning classifiers, to detect and locate approaching vehicles and warn the wearer of imminent dangers from cars, buses, motorbikes, trucks, and trams. This research will help reduce pedestrian injuries and fatalities, and expand knowledge on designing wearable systems for enhancing safety in cities, workplaces, and the home.
In the sustainability space, Jiang’s team is creating a city-scale system to track each person’s unique, individual energy usage. This personal “energy footprint” is updated in real-time to reflect the user’s movements and actions. This knowledge will enable people to understand the energy implications and tradeoffs of their everyday choices. Users’ energy footprints data are collected and analyzed to provide actionable suggestions to reduce energy from a global perspective. This system comprises several components across the hardware-software stack, including energy-efficient wearable devices, real-time sensing and actuating, building energy monitoring, indoor localization, building-scale digital twins, recommender systems, and behavior-driven energy economics. It will fundamentally change how people live their everyday lives towards a more environmentally responsible and sustainable future.
Jiang led one of the earliest projects on IP-based smart-buildings, bringing about the first IPv6/6LowPAN-based smart-plug meter, web-service based HVAC and lighting monitoring, uniform data schema for physical sensor data, and fine-grain real-time energy analytics, paving the way for an information revolution in the building industry. His magnetic-based indoor localization system has an accuracy and consistency that is significantly better than the state of art. His air quality monitoring system has been deployed at scale in major metropolitan cities.
Jiang received his B.Sc. (2004) and M.Sc. (2007) in Electrical Engineering and Computer Science, and his Ph.D. (2010) in Computer Science, all from UC Berkeley. Before joining SEAS, he was Senior Staff Researcher and Director of Analytics and IoT Research at Intel Labs China. His research has been published in top venues with over four thousand citations and featured in many popular media outlets, including The Economist, New York Post, Mashable, Gizmodo, The Telegraph, and Fast Company. He is recipient of an NSF Graduate Fellowship, a Vodafone-US Foundation Fellowship, and an NSF CAREER Award.