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Andrew Spielberg is a Postdoctoral Fellow at Harvard University, where he works with Prof. Jennifer A. Lewis in the Lewis Lab. His mission is to enable anyone to be able to design functional artifacts across scales and domains. He looks to empower novices and accelerate experts' workflows.
Andrew researches differentiable simulation, design algorithms, digital manufacturing processes, and methods for overcoming the sim-to-real gap, for inventing in both virtual and physical worlds. He has published over 30 papers in top refereed venues, and his work has been recognized with a CHI best paper award, ICRA and RoboSoft best paper nominations, Advanced Intelligent Systems journal highlights, and an NeurIPS oral presentation. He is a recipient of the Unity Global Fellowship, the DARPA I2O Fellowship, and a Harvard GRID $100K award. Andrew received his PhD from MIT's Computer Science and Artificial Intelligence Lab, where he was advised by Daniela Rus and Wojciech Matusik, and has spent time at Disney Research Pittsburgh and Zürich, Intel Labs, and Johns Hopkins University Applied Physics Lab.
Abstract: Complex cyberphysical systems are increasingly part of our modern world, in home robotics, heavy machinery, medical devices, and beyond. The behavior of these systems is jointly driven by their components, form, and on-board artificial intelligence. As computing and advanced manufacturing techniques expand the types of systems we can build and what built systems around us can do, we require design tools that cut through increasingly complex, often intractable possibilities. Those tools should be accurate, optimizing, explorative, and enable physical realization, with the goal of ideating and fabricating machines that approach the diversity and capability of biological life.
In this talk, I will discuss solutions for co-designing dynamical cyberphysical systems over their physical morphological and embodied artificial intelligence. In particular, I will discuss efficient methods for co-optimizing and co-learning morphology and control, digital fabrication methods that leverage spatially programmable materials for function, and data-driven modeling for overcoming the sim-to-real gap. These methods will be tied together in a vision for computational invention.