PhD Thesis Defense

Abstract: In classic supervised machine learning, a learning agent behaves as a passive observer: it receives examples from some external environment which it has no control over and then makes predictions. Reinforcement Learning (RL), on the other hand, is fundamentally interactive: an autonomous agent must learn how to behave in an unknown and possibly hostile [...]

Abstract: Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environments and are subject to imperfect sensing and actuation. This brings substantial uncertainty into the problems. Reasoning under this uncertainty can provide higher level of robustness but is computationally significantly more challenging. More specifically, sequential decision making under motion and sensing uncertainty [...]

Abstract: Optical Coherence Tomography (OCT) and Ultrasound (US) are non-ionizing and non-invasive imaging modalities that are clinically used to visualize anatomical structures in the body. OCT has been widely adopted in clinical ophthalmology due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) captures images of tissue at a [...]

Abstract: Humans are arguably one of the most important entities that AI systems would need to understand to be useful and ubiquitous. From autonomous cars observing pedestrians to assistive robots helping the elderly, a large part of this understanding is focused on recognizing human actions, and potentially, their intentions. Humans themselves are quite good at [...]

Abstract: Non-Rigid Structure from Motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from a sequence of images with 2D correspondences. Current NRSfM algorithms are mainly limited within two perspectives: (i) the number of images, and (ii) the type of shape variability they can handle. These [...]

Abstract: While robotics has made tremendous progress over the last few decades, most success stories are still limited to carefully engineered and precisely modeled environments. Getting these robots to work in the complex and diverse world that we live in has proven to be a difficult challenge. Interestingly, one of the most significant successes in [...]

Abstract: With the advent of affordable and high-quality smartphone cameras, any significant events will be massively captured both actively and passively from multiple perspectives. This opens up exciting opportunities for low-cost high-end VFX effects and large scale media analytics. However, automatically organizing large scale visual data and creating a comprehensive 3D scene model is still [...]

Abstract: There is a famous tale in computer vision: Once, a graduate student asked the famous computer vision scientist Takeo Kanade: "What are the three most important problems in computer vision?" Takeo replied: "Correspondence, correspondence, correspondence!" Indeed, even for the most commonly applied Convolutional Neural Networks (ConvNets), they are internally learning representations that lead to [...]

Abstract: Achieving a precise predictive understanding of the future is difficult, yet widely studied in the natural sciences. Significant research activity has been dedicated to building testable models of cause and effect. From a certain view, a perfect predictive model of the universe is the “holy grail”; the ultimate goal of science. If we had [...]

Abstract: Acoustic imaging sonars have been used for a variety of tasks intended to increase the autonomous capabilities of underwater vehicles. Among the most critical tasks of any autonomous vehicle are localization and mapping, which are the focus of this work. The difficulties presented by the imaging sonar sensor have led many previous attempts at [...]