Publications

Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples...

Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an...

Estimations of human joint torques can provide quantitative, clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Standard methods for estimating joint torques are limited to laboratory or clinical settings since they require expensive equipment to measure joint kinematics and ground reaction...

In this paper we study dynamical distortion problems in future electrical energy systems with high renewable penetration. We introduce a new time-domain modeling of electrical energy systems comprising inverter-controlled distributed energy resources (DERs). This modeling is first used to quantify the relations between distortions and real/reactive power dynamics. Next, to...

In probabilistic inference, credible intervals constructed from posterior samples provide ranges of likely values for continuous parameters of interest. Intuitively, an inference procedure is optimal if it produces the most precise posterior intervals that cover the true parameter value with the expected frequency in repeated experiments. We present theories and...

Microgrids have been seen as a good solution to providing power to forward-deployed military forces. However, compatibility, robustness and stability of current solutions are often questionable. To overcome some of these problems, we first propose a theoretically-sound modeling method which defines common microgrid component interfaces using power and rate of...

The purpose of this study was to develop an automated method for classifying liver fibrosis stage >=F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each...

Early detection for sepsis, a high-mortality clinical condition, is important for improving patient outcomes. The performance of conventional deep learning methods degrades quickly as predictions are made several hours prior to the clinical definition. We adopt recurrent neural networks (RNNs) to improve early prediction of the onset of sepsis using...

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...