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Jingping Nie

Enable the Machines to Understand Humans: Intelligent and Privacy-Aware IoT Systems and Wearable Devices

Research Abstract:

Science is tightly connected to liberal arts and technology innovation can bring evolution to humanity. My research focuses on human-centric intelligent wearable devices and Artificial Intelligence of Things (AIoT) systems by bringing advances across diverse disciplines, including electrical engineering, biomedical engineering, and computer science. The ultimate goal is to seamlessly incorporate intelligent and privacy-aware wearable devices and sensors into our daily lives, enhancing machine understanding of humans and improving personal care and social welfare. My research includes four interconnected topics spanning from the individual level to the system level: (1) AI-based mental and physical wellness care in the smart home environment; (2) smart wearables for emotion and general wellness; (3) indoor intelligent sensor network platforms; and (4) city-scale human-in-the-loop EV-interfaced grid optimizations and recommendations. Looking forward, I plan to build on and expand my ongoing work and focus on intelligent, human-centric, health-oriented, and privacy-aware wearable devices and AIoT systems, leveraging cutting-edge technologies and introducing novel applications.


Jingping Nie is a Ph.D. candidate in the Department of Electrical Engineering at Columbia University, advised by Professor Xiaofan (Fred) Jiang and Professor Matthias Preindl. She received her Master of Science degree (Honor Student) in Electrical Engineering from Columbia University (2018) and her Bachelor of Science degree (Magna cum Laude with High Honors) in Engineering Science from Smith College (2017). Her research focuses on hardware-software co-design of next-generation human-centric intelligent and privacy-aware wearable devices in AIoT systems. Her recent projects include creating wearable devices for health and wellness, AIoT systems for mental health, as well as human-in-the-loop EV charging optimizations. Her works have been published in various top-tier journals and conferences and received multiple awards, including best demo award at ACM/IEEE IPSN, best demo runner-up award at ACM SenSys, and best paper award at IEEE ITEC. Jingping is the recipient of the Apple Scholars in AI/ML PhD fellowship and the Columbia University Jacob Millman Award. She was a machine learning research intern at Apple.