Brad Aimone

About Brad Aimone

I am a computational and theoretical neuroscientist focused on bridging the gap between next generation computing technology and neuroscience. I help lead a research program committed to identifying how and why we should bring neural inspiration into fields such as big data analytics and machine learning. On the neuroscience side, I study neural networks in the brain that are involved in learning and memory. I have been \"reverse engineering\" the neural circuits of the hippocampus, a region known to be important in learning and memory and potentially related to a range of neurological and psychiatric conditions. My theoretical and computational work has been successful in motivating numerous experimental studies around the world.Ultimately, my goal is to develop modeling approaches that are capable of both extracting knowledge about how the brain works - thus improving our approach to treating complex neurological disorders - as well as how its methods of computation can be applied to solving a wide range of real-world problems in many different industries, including post-Moore\'s Law computing.Specialties: Computational modeling of real neural systems, artificial intelligence, neural machine learning, quantitative analysis of biological data

Experience

1. Distinguished Member of Technical Staff

   Sandia National Laboratories

   Jun 2022 — Present · Albuquerque, NM, US

   Lead multiple efforts in neuromorphic computing and brain-inspired algorithms designPI for DOE Microelectronics Co-Design Project on probabilistic computing (COINFLIPS)PI for NSF / DOE Collaborative Research in Computational Neuroscience project

Education

• Rice University

  BS, Chemical Engineering

  1997 — 2001

• UC San Diego

  PhD, Computational Neuroscience

  2005 — 2009

• Rice University

  MChE, Chemical Engineering

  2001 — 2002

Skills

• Machine Learning
• Brain Inspired Computing
• Lifesciences
• Computational Biology
• Matlab
• Life Sciences
• Biotechnology
• Neuroscience
• Computational Neuroscience
• Neural Networks
• Bioinformatics
• Artificial Intelligence
• Research