Simon Kristoffersson Lind

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About

Simon Kristoffersson Lind is a researcher advancing the reliability and safety of robotic perception through rigorous uncertainty quantification. His primary research areas lie at the intersection of deep learning, computer vision, and robotics, with a specific focus on making neural networks trustworthy in real-world, safety-critical applications. Lind’s major contribution is in developing robust metrics and methods for uncertainty quantification in deep regression models, a foundational need for any robot that must reason about the safety of its actions. His highly cited 2024 work, "Uncertainty quantification metrics for deep regression," has already garnered 19 citations, underscoring its immediate impact on the field. In parallel, Lind tackles the challenge of perception under severe lighting conditions with his innovative work "Making the Flow Glow," which leverages normalizing flow gradients for out-of-distribution detection. This approach directly addresses the well-known unreliability of neural networks in difficult imaging environments. Through these contributions, Lind is equipping robots with the tools to not only perceive the world but to know when their perception might be wrong, a critical step toward truly autonomous and safe systems.