Jonas Weiss

Biography

Jonas Weiss received a Master's and a PhD degree in electrical engineering from the Swiss Federal Institute of Technology (ETH) in Zurich in 1997 and 2008, respectively. He has held various positions in the industry, designing integrated circuits for mobile applications and medical electronics systems, and in 2003 joined the IBM Research – Zurich laboratory in Rüschlikon, Switzerland, where he currently is a Research Staff Member in the Neuromorphic Devices and Systems group. Initially his research focused on very-high-speed CMOS circuits and ESD protection, and later also on advanced packaging and system architectures for very high-density electro-optical data-interconnects on the printed circuit board level. His interest then shifted towards system and link analysis, with emphasis on how to optimally integrate and deploy optics and silicon photonics in future servers and data-centers. More recently, his focus is on machine learning for medical applications, and on studying how physical devices can be applied to accelerate emerging cognitive and machine-learning workloads through analog computing techniques. Jonas Weiss received a Master's and a PhD degree in electrical engineering from the Swiss Federal Institute of Technology (ETH) in Zurich in 1997 and 2008, respectively. He has held various positions in the industry, designing integrated circuits for mobile applications and medical electronics systems, and in 2003 joined the IBM Research – Zurich laboratory in Rüschlikon, Switzerland, where he currently is a Research Staff Member in the Neuromorphic Devices and Systems group. Initially his research focused on very-high-speed CMOS circuits and ESD protection, and later also on advanced packaging and system architectures for very high-density electro-optical data-interconnects on the printed circuit board level. His interest then shifted towards system and link analysis, with emphasis on how to optimally integrate and deploy optics and silicon photonics in future servers and data-centers. More recently, his focus is on machine learning for medical applications, and on studying how physical devices can be applied to accelerate emerging cognitive and machine-learning workloads through analog computing techniques.

Research Interest

very-high-speed CMOS circuits and ESD protection