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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
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X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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DTSTART;TZID=America/New_York:20260810T153000
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SUMMARY:Using Sound to Steer Light and Light to Measure Sound
DESCRIPTION:Abstract:   Light and sound are traditionally treated as distinct physical phenomena\, yet their interaction provides a powerful mechanism for manipulating and sensing information across imaging\, communication\, and measurement. This talk explores computational acousto-optic systems that co-design acoustics\, optics\, and signal processing to enable programmable control of light and high-speed optical sensing without mechanical motion. \nFirst\, an acousto-optic structured-light system is presented in which ultrasound generates rapidly varying refractive-index patterns that steer laser beams at megahertz rates. Combined with event-based vision\, this approach projects up to two million structured-light planes per second and enables 3D scanning at up to 1\,000 frames per second. \nNext\, the same acousto-optic principle is applied to underwater optical backscatter communication by dynamically steering retroreflected light toward or away from a receiver using ultrasound. This enables low-power underwater devices to achieve megabit-per-second data transmission without mechanically actuated optics. \nFinally\, the talk investigates the complementary direction of using light to measure sound. By combining a continuous-wave laser\, a high-speed photodetector\, and commodity software-defined radios\, phase-based depth measurements are performed at megahertz rates\, recovering microscopic surface vibrations and acoustic signals from remote objects. \nTogether\, these systems demonstrate that acoustic waves can serve as programmable optical elements while optical measurements provide sensitive probes of acoustic phenomena. By tightly integrating acoustics\, optics\, computational imaging\, and signal processing\, this work establishes a unified framework for building fast\, programmable sensing and communication systems that operate beyond the capabilities of conventional mechanically actuated approaches. \nBio:  Dhawal Sirikonda is a PhD candidate at Dartmouth College working with Prof. Adithya Pediredla in the Rendering and Imaging Science Lab (RISc). He builds next-generation ultra-fast LiDAR sensors and novel imaging systems for high-speed perception and communication applications. His research focuses on designing computational imaging systems by leveraging diverse sensor modalities\, including event cameras\, RGB-D sensors\, and emerging hybrid sensing architectures. His current work explores novel acousto-optic imaging systems for fast scanning and communication applications. He is particularly interested in combining machine learning and physics-based computational methods to develop robust real-world sensing systems. Prior to his PhD\, he completed his Master’s degree working with Prof. P. J. Narayanan at the intersection of 3D Vision and Real-Time Graphics. \nHomepage:   https://dhawal.xyz \nSponsor:\nThe VASC seminar is generously sponsored by HeyGen\, an all-in-one AI-powered video generation platform that leverages advances in computer vision\, generative modeling\, and multimodal learning to make high-quality video creation both scalable and accessible.
URL:https://www.ri.cmu.edu/event/using-sound-to-steer-light-and-light-to-measure-sound/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:Seminar,VASC Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/8-10-26.jpeg
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DTSTART;TZID=America/New_York:20260831T153000
DTEND;TZID=America/New_York:20260831T163000
DTSTAMP:20260912T200602
CREATED:20260825T175254Z
LAST-MODIFIED:20260825T175617Z
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SUMMARY:My Bitter Lesson with Computer Graphics
DESCRIPTION:Abstract:  In his essay “The Bitter Lesson\,” Richard Sutton argued that general methods leveraging computation ultimately outperform hand-crafted ones. In this talk\, I share my own version of this lesson\, learned the hard way over a decade in computer graphics. Where does the bitter lesson apply to graphics\, and where does it not? I argue that the answer depends on whether a problem genuinely requires 3D\, physics\, and control\, or if it simply produces 2D pixels. I will also share a few advices for graduate students starting their research today\, showing which directions will compound in value over a decade\, and which will be quietly subsumed by the next scale-up. \nBio:  Giljoo Nam is a research scientist at Meta focused on Physical AI\, building systems that understand the structure and behavior of the 3D physical world. His research bridges computer vision and graphics\, with focus areas including generative AI\, 3D reconstruction\, motion tracking\, inverse rendering\, computational imaging\, and human modeling. He earned his Ph.D. in Computer Science from KAIST in 2019.. \nHomepage:   https://giljoonam.github.io/ \n  \nSponsor: \nThe VASC seminar is generously sponsored by HeyGen\, an all-in-one AI-powered video generation platform that leverages advances in computer vision\, generative modeling\, and multimodal learning to make high-quality video creation both scalable and accessible.
URL:https://www.ri.cmu.edu/event/my-bitter-lesson-with-computer-graphics/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:Seminar,VASC Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/8-31-26.jpg
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