Stephen McLaughlin

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About

Stephen McLaughlin is a leading figure in computational imaging and sensing, whose work bridges the gap between advanced hardware and intelligent algorithms. His primary research areas include single-photon avalanche diode (SPAD) imaging, time-of-flight (ToF) depth sensing, and high-speed 3D reconstruction. A standout contribution is his pioneering work on hybrid-mode imaging and guided upsampling, which enables high-speed 3D sensing with unprecedented temporal resolution—a critical advance for applications from autonomous vehicle navigation to fluorescence lifetime microscopy. His landmark 2020 paper on this topic has garnered 88 citations, reflecting its impact on the field. McLaughlin’s research is distinguished by its practical focus on overcoming the limitations of conventional imaging systems, particularly in low-light and high-speed scenarios. His achievements include developing novel computational methods that extract maximal information from SPAD arrays, pushing the boundaries of what is possible in real-time depth sensing. For students and researchers, McLaughlin’s work exemplifies how integrating physical optics with machine learning can unlock new capabilities in imaging, making him a key figure to follow in the evolving landscape of computational photography and sensing.