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Recent advances in engineering science have led to new classes of medical devices with emergent mechanical, electrical, and thermal properties that offer new opportunities for interfacing with living cells. I will discuss conceptual advances in microfabrication, device physics, power transfer and microscale transport phenomena that enable novel biosensors and cell delivery systems, with an emphasis […] |
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Although machine intelligence is taking over the world, its current digital electronic platform is very inefficient in terms of energy consumption. Switching to analogue computation, which function more like human brains than digital computers, will allow enhancing the energy efficiency by several orders of magnitude. Optics presents a particularly promising platform for analogue AI; however, […] |
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The rich set of mechanoreceptors found in human skin offers a versatile engineering interface for transmitting information and eliciting perceptions, potentially serving a broad range of applications in patient care and other important industries. Targeted multisensory engagement of these afferent units, however, faces persistent challenges, especially for wearable, programmable systems that need to operate adaptively […] |
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: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the classical low-rank prior, and introduces scaled gradient descent (ScaledGD), a simple iterative approach to directly recover the low-rank factors for a wide range of matrix […] |
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Materials properties are governed by the structure and dynamics of the bonds between their constituent atoms. In addition to covalent, metallic, and ionic interactions that we typically think about, lone pair electrons can result in non-trivial directional interactions in materials. I will discuss molecular interactions involving lone pairs in materials, focusing on results from molecular […] |
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Deep learning with neural networks has emerged as a key approach for discovering patterns and modeling relationships in complex data. AI systems powered by deep learning are used widely in applications across a broad spectrum of scales. There are strong needs for scaling deep learning both upward and downward. Scaling up highlights the pursuit of […] |
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Medical electronic devices are an integral part of the healthcare system today and are used in a variety of applications around us. The design of such devices has several stringent requirements, the key being miniaturization, low-power operation, and wireless functionality. In this talk, I will present CMOS-based miniaturized, low-power and wireless biomedical devices in three […] |
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Low (or ideally zero) numerical dissipation is always critical for high-fidelity scale-resolving flow simulations, as numerical dissipation prevents the physics of inviscid kinetic energy and entropy conservation, which is an essential attribute of compressible turbulence. However, contrary to the requirement, numerical schemes in compressible flow heavily rely on numerical dissipation for stable computation, preventing high-fidelity […] |
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This talk will explore methods for accelerating numerical optimization constrained by transient problems using parallelism. Two types of transient problems will be considered. In the first case training algorithms for Neural ODEs will be discussed. Neural ODEs are a class of neural network architecture where the depth of the neural network (the layers) is modeled […] |
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Current medical image analysis projects involve months to years of data annotation and custom technical development. This talk introduces methods to train networks that generalize out-of-the-box to new modalities, anatomies, and datasets all without retraining for the specific use case. Our key contributions include (A) generative models driven by biomedical shape priors that synthesize wildly […] |
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