PhD Thesis Proposal

Abstract As machine learning becomes more ubiquitous, clustering has evolved from primarily a data analysis tool into an integrated component of complex machine learning systems, including those involving dimensionality reduction, anomaly detection, network analysis, image segmentation and classifying groups of data. With this integration into multi-stage systems comes a need to better understand interactions between [...]

Abstract: In complex dynamic multi-robot domains, we have a set of individual robots that must coordinate together through a team planner that inevitably makes assumptions based on probabilities about the state of world and the actions of the individuals. Eventually, the individuals may encounter failures, because the team planner’s models of the states and actions [...]

Abstract: For this thesis, we propose to study how to automatically combine multiple neighborhood-based heuristics. For most computationally challenging problems, there exists multiple heuristics, and it is generally the case that any such heuristic exploits only a limited number of aspects among all the possible problem characteristics that we can think of. As a result, [...]

Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones are today. Such pervasive integration of robots would greatly benefit from faster design and manufacturing of robots that cater to individual needs. However, robots of today often take years to be created [...]

Abstract: Soft machines and electronics are key components for emerging applications in wearable biomonitoring, human-machine interaction, and soft robotics. In contrast to conventional machines and electronics, soft-matter technologies provide a method for replicating these traditionally rigid devices using intrinsically soft materials that exhibit properties similar to soft biological tissue. This provides a path forward for [...]

Abstract: Our visual world is highly structured and the visual data is highly redundant. In recent years, the computer vision field has been transformed by the success of Convolutional Neural Networks (ConvNets). However, the structure and redundancy in visual data has not been well explored in deep learning. The benefits of exploring data redundancy are [...]

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. The predictions the agent made will not affect any future examples it will see (i.e., examples are identically and independently sampled from some unknown [...]

Abstract: Depth sensors for robust navigation must measure scenes in darkness, bright light, and in scattering media. Scanning LIDAR devices are the most robust to these conditions, but capture sparse measurements, are slow, and expensive. Consumer depth cameras, on the other hand, are inexpensive and produce dense, high rate depth measurements, but fail in bright [...]

Abstract: ​Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give computers insight [...]

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 practice due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) can capture images at a depth [...]