Associate Professor, National University of Singapore
Dr. Benjamin C.K. Tee is Associate Professor in Materials Science and Engineering Department at the National University of Singapore. He also holds the Vice Dean (Research) appointment in the College of Design and Engineering Dean’s Office.
He leads the Sensors.AI Labs to develop technologies at the cutting edge of materials science, mechanics, electronics and biology, with a focus on sensitive skin-like electronic materials that has tremendous potential to advance robotics and healthcare technologies in an increasingly Artificial Intelligence (AI) era.
He was awarded the National Research Foundation Fellowship in 2017. He obtained his PhD at Stanford University, and was a Singapore-Stanford Biodesign Global Innovation Postdoctoral Fellow in 2014. He is an MIT TR35 Innovator (Global) in 2015 and listed as World Economic Forum’s Young Scientist of the year in 2019. He was featured by CNN International as one of their Tomorrow’s Hero series, by Channel News Asia International in the ASEAN’s Next Generation Leaders documentary and by BBC World Service Radio and National Geographic TV. He has co-founded two medtech companies and excited to tackle challenging needs in prosthetics, robotics and AI. He can be found on www.benjamintee.com
Towards Sustainable Sensor Technologies through Hybrid Approaches
The growth of flexible and wearable electronics commensurate with the proliferation of microelectronic devices is enabling high impact applications in healthcare and robotics. However, as such electronic devices increase in number, an increasingly urgent need to create materials and devices that can be part of a circular or self-repairable economy becomes critical. In this talk, I will discuss using organic materials science and engineering approaches as a way to scale skin-like sensors for more sustainable technological and societal impact through self-healing or degradability.
To scale to human-like performance, neuromorphic engineering provides an exciting avenue to mimic the high performance of the human nervous systems and sensors. Critically, the energy efficiency of the human neural networks for learning relies on event-driven, temporally encoded action potential streams. In this talk, I will discuss how we can digitize tactile information through inspiration from somatosensory neural science.
We have developed an asynchronous protocol for parallel transmission of tactile information in an artificial peripheral nervous system we call ACES: Asynchronously Coded Electronic Skins. The parallel transmission encodes spatial temporal information with very high temporal precision (sub-100ns) even when large numbers > 10,000 sensor nodes are transmitting simultaneously. Such systems can be interfaced with soft sensors or flexible/stretchable electronics to enable more intuitive robotics and healthcare applications.