Max Robotics — Global Service Robot Directory + US Certification

About

Jennifer Hasler is a pioneering researcher in neuromorphic engineering and reconfigurable analog VLSI (AVLSI) systems, with a focus on creating biologically inspired hardware for autonomous robotics and neural computation. Her major contributions include developing field-programmable analog arrays (FPAAs) that enable real-time, low-power path planning for robots in complex environments, as demonstrated in her 2016 work on single-objective path planning using reconfigurable analog circuits (14 citations). She advanced this approach to three-dimensional robot navigation in 2014 (4 citations), showcasing the versatility of analog hardware for spatial reasoning. Notably, her 2012 study on "Learning in silicon" (2 citations) introduced a floating-gate-based neuromorphic system with synaptic plasticity, modeling biological neural behavior with unprecedented energy efficiency. Hasler’s work bridges the gap between theoretical neuromorphic computing and practical hardware implementation, achieving orders-of-magnitude reductions in size and power consumption compared to digital alternatives. Her research has profound implications for autonomous systems, edge computing, and brain-inspired AI, positioning her as a key innovator in analog computing for robotics and neural networks.