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About

Alexander Amini is a robotics and machine learning researcher whose work sits at the intersection of safe autonomous systems, biologically inspired neural networks, and robust robot learning. His research spans several cutting-edge domains, including safety-critical control, autonomous navigation, soft robotics, and neuromorphic computing. Among his most influential contributions is the development of BarrierNet, which introduced differentiable control barrier functions to guarantee safety in learned neural network controllers — a breakthrough approach that has garnered 99 citations since its 2023 publication. His work on liquid neural networks for robust flight navigation (79 citations) demonstrated that biologically inspired architectures can generalize powerfully to unseen environments, pushing the frontier of autonomous aerial systems. Amini has also pioneered the use of neuronal circuit policies inspired by the *C. elegans* nervous system, developing interpretable, compact control agents that bridge neuroscience and reinforcement learning. His research extends to practical challenges such as occluded obstacle detection for autonomous vehicles, uncertainty-aware tactile sensing, and co-learning sensor placement for soft robotics. Across these diverse areas, Amini consistently addresses real-world deployment challenges — robustness, safety, and interpretability — making his work particularly valuable for students and practitioners building the next generation of intelligent autonomous systems.