Melika Payvand

Work phone:

I received my masters and PhD degree in Electrical and Computer Engineering at University of California Santa Barbara in 2012 and 2016 respectively. During my years in Santa Barbara, I grew large interests in understanding how the brain works and with the background I had in VLSI, I was naturally attracted to the field of neuromorphic VLSI. During my PhD my main focus was to design configurable CMOS memory and neuromorphic platforms to enable memristor integration and utilize their nano-features as memory and artificial synapses.
I joined INI in March 2017 as a post-doc and then as a scientist in Neuromorphic Cognitive Systems group to continue pursuing my interests in the field by working on designing neuromorphic chips which exploit different characteristics of memristors to enable low power and highly dense solutions for biomedical and IoT edge applications.

Summary of Interests and Experience:Neuromorphic VLSI Design, Mixed-Signal IC Design, Neural Coding Algorithms, Spiking Neural Networks



  • Enea Ceolini, Charlotte Frenkel, Sumit Bam Shrestha, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati Hand-gesture recognition based on EMG and event-based camera sensor fusion: a benchmark in neuromorphic computing, Frontiers in Neuroscience, 2020 pdf
  • Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi, Ahmed Eltawil, Emre O. Neftci Error-triggered three-factor learning dynamics for crossbar arrays, 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020 pdf
  • Melika Payvand, Yigit Demirag, Thomas Dalgathy, Elisa Vianello, Giacomo Indiveri Analog weight updates with compliance current modulation of binary ReRAMs for on-chip learning, ISCAS, 2020 pdf
  • Giacomo Indiveri, Bernabe Linares Barranco, Melika Payvand System-level integration in neuromorphic co-processors, Memristive Devices for Brain-Inspired Computing, 2020 pdf