PhD Thesis Proposal

Abstract: In domains requiring effective situational awareness with limited resources, prioritizing focus is critical. Search and rescue tasks require fast identification of safe avenues for rescuers to traverse the area. Inspection tasks must realize trends over long durations to identify issues caused by the confluence of high-stress modes that compound into catastrophic failure. Deploying robots [...]

Abstract: Developing 'social intelligence' for assistive robots to seamlessly interact with humans remains an open research challenge. However, socially assistive robots typically engage in types of interactions that already exist between humans, which makes models of human-human interactions useful to inform the design of robot social behaviors. In particular, in applications such as healthcare, therapy [...]

Abstract: There are many problems in real life that involve collecting and aggregating evaluation from people, such as conference peer review and peer grading. In this thesis, we consider multiple sources of biases that may arise in this process: (1) human bias -- the data collected from people are noisy and reflect people's calibration criteria [...]

Abstract: Teams of aerial sensing robots can provide valuable situational awareness to first responders during disasters and emergencies by locating victims, mapping environments, identifying hazards, and locating gas sources. While individual sensing tasks may feature a variety of goals, many tasks will be time-sensitive such as after a widespread disaster or due to imminent danger [...]

Abstract: Accurate and reliable state estimation is essential for safe mobile robot operation in real-world environments because ego-motion estimates are required by many critical autonomy functions such as control, planning, and mapping. Computing accurate state estimates depends on the physical characteristics of the environment, the selection of suitable sensors to capture that information, and the [...]

Abstract: Computer vision today excels at recognition in narrow slices of the real world. Our systems seem to accurately detect cats, cars, or chairs, but largely ignore the vast diversity of objects in the world that are absent from our training datasets. Perception in the open world, however, requires detecting and tracking any object, regardless [...]

Abstract: Panoptic segmentation is a recent problem in computer vision that attempts to classify each pixel in an image according to its semantic and instance label (accomplishing both semantic segmentation and instance segmentation respectively). Most existing panoptic and instance segmentation methods run a detection-first pipeline, where a bounding box is placed around an object and [...]

Abstract: With the deployment of more robots, human-robot interaction will no longer be limited to a one-to-one interaction between a user and a robot. Instead, users will likely have to interact with multiple robots, simultaneously or sequentially, throughout their day to receive services and complete different tasks. In this thesis proposal, I am proposing joint [...]

Abstract: In recent years, the pace of innovations in the fields of machine learning has accelerated. To cope with the sheer computational complexity of training large ML models on large datasets, researchers in SysML have created algorithms and systems that parallelize ML training and inference over multiple CPUs or GPUs, or even multiple computing nodes [...]

Abstract: Traffic congestion is a widespread problem throughout global metropolitan areas. In this thesis, we consider methods to optimize the performance of traffic signals to reduce congestion. We begin by presenting Expressive Real-time Intersection Scheduling (ERIS), a schedule-driven intersection control strategy that runs independently on each intersection in a traffic network. For each intersection, ERIS [...]