Developing state-of-the-art tools that process 3D surfaces and volumes
We aim to understand 3D object structure from a single image. We propose an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, by training it on both real 2D-annotated images and synthetic 3D data and by integrating a 3D-to-2D projection layer.
When discussing racial disparities in medical treatments, critics often cite social factors as confounders which explain away any differences. Comparing the health of whites to that of non-whites we do see that environmental and social factors conspire to yield higher rates of disease and shorter life spans in non-white populations. But does that really show that medical treatment itself is free from bias? We examine end-of-life care in the ICU, stratified by ethnicity, and controlled for acuity using severity assessment scores. Our analysis agrees with previous studies that nonwhites tend to receive more aggressive (high-risk, high reward) treatments, such as mechanical ventilation than non-whites, despite receiving comparable-or-moderately-less noninvasive treatments. Going further, we show that using treatment patterns and clinical notes, we are able to infer a patient's race. Finally, we show evidence suggesting nonwhite have a much greater distrust of the medical community among than whites do. We find that race, even in the great equalizer of end-of-life care, does continue to influence the treatments administered to a patient.
Our goal is to help medical researchers and clincians understand the growing repositories of waveform and signal data collected from critically ill patients.
This work presents a differentiable cloth simulator that extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact. A novel back-propagation technique is proposed to accelerate gradient computation. We demonstrate the effectiveness of the simulator through inverse tasks including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design.
Many optimization problems in machine learning rely on noisy, estimated parameters. Neglecting this uncertainty can lead to great fluctuations in performance. We are developing algorithms for these already nonconvex problems that are robust to such errors.
Our goal is to understand the nature of cyber security arms races between malicious and bonafide parties. Our vision is autonomous cyber defenses that anticipate and take measures against counter attacks.
Interact with robots and other devices by gesturing, using wearable muscle and motion sensors
Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.