With the looming end of Moore’s law, the rise of the success of artificial intelligence and machine learning, and the need for low power, efficient computing systems that can be deployed at the edge, brain-inspired computing systems have become increasingly popular in the last decade. Neuromorphic computing system in particular target the design of brain-inspired architectures, perhaps with novel devices and materials.

My research is focused primarily on the computer science aspect of neuromorphic computing, namely:

  • Algorithms: Approaches for designing spiking neural network “programs” for neuromorphic computers, either automatically through machine learning approaches or manually to perform non-cognitive tasks.
  • Applications: Applying neuromorphic computing to real-world applications
  • Software: Developing tools and interfaces to make using neuromorphic computers more accessible to the average programmer

Neuromorphic computing is inherently collaborative, and I typically collaborate with people doing research in one of the following areas:

  • Novel devices and materials for neuromorphic computing
  • Applications of neuromorphic computing
  • Neuroscience-inspiration for machine learning and neuromorphic computing

Beyond neuromorphic computing, I also have research interests in bio-inspired computing, specifically evolutionary algorithms, neural architecture search, and scaling machine learning approaches.