How technology can learn from biology and how biology can benefit from technology
发布人: 星禧   发布时间: 2017-10-25    浏览次数:

讲人:  Jan Van der Spiegel


地点松江校区材料学院C321


时间 2017-11-3 10:00:00


组织单位: 纤维改性国家重点实验室、功能材料研究所


主讲人简介:


Jan Van der Spiegel is a Professor of the Electrical and Systems Engineering Department at the University of Pennsylvania and Senior Visiting Professor at the Departments of Micro&Nano Electronics at Tsinghua University. Dr. Van der Spiegel received his Master’s degree in Electro-Mechanical Engineering and his Ph.D. degree in Electrical Engineering from the University of Leuven, Belgium. His primary research interests are in mixed-mode VLSI design, CMOS vision sensors for polarization imaging, biologically based image sensors and brain-machine interfaces. He is the author of over 200 journal and conference papers and holds 4 patents.


He is a life fellow of the IEEE, the recipient of the IEEE Major Educational Innovation Award, the IEEE Third Millennium Medal, the UPS Foundation Distinguished Education Chair and the Bicentennial Class of 1940 Term Chair.


He is a member of the IEEE Solid-State Circuits Society AdCom, he is an Associate Editor of the Transaction of BioCAS, and a member of the Editorial Board of the IEEE Proceedings, and Section Editor of the Institute of Technology’s Journal of Engineering. He is currently the president of the IEEE SSCS.


内容摘要:


Advances in CMOS Technology and Material Science have allowed to create sophisticated bio-inspired sensors and signal processing systems. At the same time, a lot of progress has been made in understanding the human brain and the human sensory system. Major breakthroughs are now possible when these two fields come together. The presentation will give two examples. One is how learning from biology allowed us to create new types of sensors. We will focus on a bio-inspired polarization imager. Another example will illustrate how microelectronics technology can help people with neurological diseases. The talk will describe our recent work on a closed-loop, bi-directional Brian-Machine-Interface (BMI) that potentially can improve quality of life of these patients.