Vasilis Ntinas
Memristive Computing Researcher · Electrical & Computer Engineer · Educator
About me
I am an Assistant Professor (Tenure-Track) at Aalborg University, affiliated with the Edge Computing and Networking Group, the CMI Section, and the Department of Electronic Systems.
My research focuses on memristive computing architectures for edge intelligence, physics-based and variability-aware device modeling, and stochastic phenomena in resistive switching systems, including noise, stochastic resonance, and random-telegraph noise. A central thread in this work is the study of Cellular Nonlinear Networks (CNNs) and their memristive implementations, alongside neuromorphic circuits based on threshold switches and bio-inspired spiking dynamics, and cellular automata for unconventional and probabilistic computation.
Prior to joining AAU, I was a Research Associate at TU Dresden in the Chair of Fundamentals of Electrical Engineering led by Prof. Ronald Tetzlaff. There, I worked on memristive Cellular Nonlinear Networks within the DFG project Mem2CNN, part of the priority programme Memristive Devices Toward Smart Technical Systems (SPP 2262).
I hold a joint Ph.D. from Universitat Politecnica de Catalunya and Democritus University of Thrace, under the supervision of Prof. Antonio Rubio and Prof. Georgios Ch. Sirakoulis. My doctoral work examined stochasticity in memristor systems across scales, from device physics to computing architecture. I also hold an M.Sc. in Microelectronics and Computer Systems and a Diploma in Electrical and Computer Engineering from Democritus University of Thrace, where I received the Best Diploma Thesis Award.
Academic Journey
Assistant Professor (Tenure-Track)
Copenhagen, Denmark
Research Associate
Dresden, Germany
Ph.D. in Electronic Engineering / Electrical & Computer Engineering
Barcelona, Spain & Xanthi, Greece
M.Sc. in Microelectronics and Computer Systems
Xanthi, Greece
Diploma in Electrical and Computer Engineering
Xanthi, Greece
Research Interests
Research highlights
Memristor Modeling
Device Physics & Compact Models
Physics-based and variability-aware models of resistive switching devices, capturing noise, drift, and device-to-device variability for circuit-level simulation.
Stochastic Resonance
Noise-Enhanced Computation
Harnessing noise and random-telegraph phenomena in memristive systems, where stochastic resonance improves signal detection and information processing.
Unconventional Computing
CNNs · Cellular Automata
Memristive Cellular Nonlinear Networks, neuromorphic threshold-switch circuits, and cellular automata for bio-inspired and probabilistic computation.