Seminar
Learned Imaging Systems
Abstract: Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Of particular interest in recent years has been the development of end-to-end learned “Deep Optics” systems that use differentiable optical simulation in combination with backpropagation to simultaneously learn optical design and deep network post-processing for applications such as hyperspectral [...]
ARPA-H and America’s Health: Pursuing High-Risk/High-Reward Research to Improve Health Outcomes for All
Dr. Andy Kilianski will provide an overview of ARPA-H, a new U.S. government funding agency pursuing R&D for health challenges. He will review the unique niche occupied by ARPA-H within the Department of Health and Human Services and how ARPA-H is already partnering with academia and industry to transform health outcomes across the country. Discussion [...]
Robots Crossing Boundaries
Abstract: Over the last 50 years, autonomous robots have made the leap from being novel research contributions in university labs to becoming the fundamental technology upon which companies are built. While they traditionally have belonged to the engineering and computer science disciplines, robots have now crossed into other areas of study and research - making impacts in oceanography, geology, archaeology, biomechanics and biology. [...]
What Makes Learning to Control Easy or Hard?
Abstract: Designing autonomous systems that are simultaneously high-performing, adaptive, and provably safe remains an open problem. In this talk, we will argue that in order to meet this goal, new theoretical and algorithmic tools are needed that blend the stability, robustness, and safety guarantees of robust control with the flexibility, adaptability, and performance of machine [...]
Soft Wearable Haptic Devices for Ubiquitous Communication
Abstract: Haptic devices allow touch-based information transfer between humans and intelligent systems, enabling communication in a salient but private manner that frees other sensory channels. For such devices to become ubiquitous, their physical and computational aspects must be intuitive and unobtrusive. The amount of information that can be transmitted through touch is limited in large [...]
Robots That Know When They Don’t Know
Abstract: Foundation models from machine learning have enabled rapid advances in perception, planning, and natural language understanding for robots. However, current systems lack any rigorous assurances when required to generalize to novel scenarios. For example, perception systems can fail to identify or localize unfamiliar objects, and large language model (LLM)-based planners can hallucinate outputs that [...]