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Filtering by: Creator Dale, A. C. Remove constraint Creator: Dale, A. C. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Degree Field Computer Science Remove constraint Degree Field: Computer Science Degree Level Doctoral Remove constraint Degree Level: Doctoral

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  • Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

    Creator:
    Almadhoun, Eman
    Abstract:
    In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
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    Dissertation
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    participants have an accurate mental model of a singly linked list in C, after learning about and implementing

  • Machine Learning for Precision Medicine: Application to Cancer Chemotherapy Response Prediction and Wound Healing Status Assessment

    Creator:
    Wei, Qi
    Abstract:
    Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
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    Dissertation
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    Assessment Abstract approved: Stephen A. Ramsey Ph.D. candidate Qi Wei’s thesis consists of two projects

  • Monte Carlo Tree Search with Fixed and Adaptive Abstractions

    Creator:
    Hostetler, Jesse A.
    Abstract:
    Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
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    Dissertation
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    enable more sophisticated solution algorithms. c©Copyright by Jesse A. Hostetler March 20, 2017 All

  • Walking tree methods for biological string matching

    Creator:
    Hsu, Tai C.
    Abstract:
    Approximate string matching is commonly used to align genetic sequences (DNA or RNA) to determine their shared characteristics. In contrast with the standard dynamic programming methods which use local edit distance models, the Walking Tree heuristic method was created to handle non-local changes, e.g., translocations, inversions, and duplications, altogether and...
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    Dissertation
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    , 2003 All Rights Reserved Walking Tree Methods for Biological String Matching by Tai C. Hsu A

  • Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

    Creator:
    Fallatah, Abrar
    Abstract:
    While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
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    Dissertation
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    . . . . . . . . . . . . . . . . . . . 18 3.6 Four Examples of Caring Behaviors: (a) and (b) volunteering to help, (c) anticipating the

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
    Resource Type:
    Dissertation
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    of segments. . . . 105 2 MWISPathGraph(c), a dynamic programming algorithm to search for Maximum

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
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    on three datasets: (a) Sachs et al. cell signaling; (b) Asia lung disease (mut1); and (c) Asia lung

  • Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

    Creator:
    Seo, Eugene
    Abstract:
    Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
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    Dissertation
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    : A) common pollinators that visit many plant species; B) rare polli- nators; C) common pollinators

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
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    Dissertation
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    Ramsey We describe a series of novel computational models, CERENKOV (Computational Elu- cidation of the

  • Behavior graphs for data-driven animation of 3d characters

    Creator:
    Srinivasan, Madhusudhanan
    Abstract:
    In this dissertation, we present a user-in-the-loop method for the design of an interactive motion data structure that benefits from the advantages of both motion graphs and blend-based techniques. Our novel approach automatically analyzes a traditional motion graph built from labeled motion clips. The result is a more condensed, coarser...
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    Dissertation
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    : Behavior Graphs for Data-driven Animation of 3D Characters Abstract approved: Ronald A. Metoyer In this