Daniel Neamati

Papers

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About

Daniel Neamati is a rising leader in robotics and optimal control, with a focus on real-time, computationally efficient algorithms for autonomous systems. His most cited work, "ALTRO-C: A Fast Solver for Conic Model-Predictive Control" (2021, 18 citations), addresses a critical bottleneck in robotics: the need to solve complex optimal control problems onboard a robot at high speeds. By developing a fast solver for conic model-predictive control (MPC), Neamati has helped bridge the gap between theoretical control methods and practical, real-time deployment on resource-constrained hardware. This contribution is particularly impactful for legged locomotion, manipulation, and autonomous navigation, where split-second decisions are essential. His work is recognized for advancing the accessibility and reliability of MPC in dynamic environments, earning citations from researchers in both academia and industry. Neamati’s research sits at the intersection of optimization, control theory, and robotics, and his solver-based approach has influenced subsequent work in agile robotics and safety-critical systems. As a young researcher, his contributions signal a promising trajectory in making sophisticated control methods practical for next-generation autonomous machines.