dr. A. Zjajo
Circuits and Systems (CAS), Department of Microelectronics
Expertise: Digital/mixed-signal circuit and system design for biomedical and mobile applications; Data sense-making; Neuromorphic computing; Sensor fusion; Reliability management.Themes: Electronic Systems and VLSI Design, VLSI design verification
Amir Zjajo received the M.Sc. and DIC degrees from the Imperial College London, London, U.K., in 2000 and the Ph.D. degree from Eindhoven University of Technology, Eindhoven, The Netherlands in 2010, all in electrical engineering. In 2000, he joined Philips Research Laboratories as a member of the research staff in the Mixed-Signal Circuits and Systems Group. From 2006 until 2009, he was with Corporate Research of NXP Semiconductors as a senior research scientist. He joined Delft University of Technology in 2009.
Dr. Zjajo has published more than 80 papers in referenced journals and conference proceedings, and holds more than 10 US patents or patent pending. He is the author of the books Brain-Machine Interface: Circuits and Systems (Springer, 2016), Stochastic Process Variations in Deep-Submicron CMOS: Circuits and Algorithms (Springer, 2013), and Low-Voltage High-Resolution A/D Converters: Design, Test and Calibration (Springer, 2011). He served as a member of Technical Program Committee of IEEE International Symposium on Quality Electronic Design, IEEE Design, Automation and Test in Europe Conference, IEEE International Symposium on Circuits and Systems, IEEE International Symposium on VLSI, IEEE International Symposium on Nanoelectronic and Information Systems, and IEEE International Conference on Embedded Computer Systems.
His research interests include power-efficient digital/mixed-signal circuit and system design for biomedical and mobile applications, data sense-making, sensor fusion, and neuromorphic electronic circuits for autonomous cognitive systems.
Programmable Systems for Intelligence in Automobiles
(a) fail-operational sensor-fusion framework, (b) dependable embedded E/E architectures, (c) safety compliant integration of AI approaches for object recognition, scene understanding, and decision making
Computational neuroscience and bio-inspired circuits and algorithms
Low-power neuro-inspired or neuromorphic circuits and algorithms; low-power circuits and systems for neural interfacing.
Design approach for resilient integrated electronic systems in automotive and avionics applications
Developing and deploying a unified design methodology and tools for system-level design and verification of heterogeneous systems
Computing Fabric for high performance Applications
Develop an open, flexible and high performance platform by substituting heterogeneous mixed HW/SW specialized sub-systems by application specific processor arrays.
Last updated: 15 Nov 2018