Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks...
Image feature detection and matching are two critical processes for many computer vision tasks. Currently, intensity-based local interest region detectors and local feature-based matching methods are used widely in computer vision applications. But in some applications, such as biological object recognition tasks, within-class changes in pose, lighting, color, and texture...
Capacity Planning is a gradually recognized need in UNIX environments. However, the effort to address this problem has been more or less event driven and is often discarded after crises are over. The main cause to this situation is that the tools to support the CP function in UNIX environments...
The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
This thesis consists of two major components. The first part is concerned with video object instance segmentation (VOS), which is the task of assigning per-pixel labels perframe of a video sequence to indicate foreground object instance membership, given the first frame ground truth mask. VOS has myriad applications, from video...
This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
Electric grid is a critical cyber-physical infrastructure that serves as lifeline for modern society. With the increasing trend of cyber-attacks, electric grid security has become a significant concern. Electric grid operators are working hard to reduce the risk of these attacks towards the system. Having security metrics for monitoring the...
We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...