Recent Submissions

  • Data Tiling for Sparse Computation 

    An, Xiaojing (Georgia Institute of Technology, 2022-11-11)
    Many real-world data contain internal relationships. Efficient analysis of these relationship data is crucial for important problems including genome alignment, network vulnerability analysis, ranking web pages, among ...
  • Extracting Signals and Graphical Models from Compressed Measurements 

    Zhang, Hang (Georgia Institute of Technology, 2021-12-08)
    The thesis is to give an integrated approach to efficiently learn the interdependency relation among high dimensional signal components and reconstruct signals from observations collected in a linear sensing system, Broadly ...
  • Scalable Data Mining via Constrained Low Rank Approximation 

    Eswar, Srinivas (Georgia Institute of Technology, 2022-08-01)
    Matrix and tensor approximation methods are recognised as foundational tools for modern data analytics. Their strength lies in their long history of rigorous and principled theoretical foundations, judicious formulations ...
  • Robust Reservoir Computing Approaches for Predicting Cardiac Electrical Dynamics 

    Shahi, Shahrokh (Georgia Institute of Technology, 2022-07-29)
    Computational modeling of cardiac electrophysiological signaling is of vital importance in understanding, preventing, and treating life-threatening arrhythmias. Traditionally, mathematical models incorporating physical ...
  • Efficient methods for read mapping 

    Zhang, Haowen (Georgia Institute of Technology, 2022-08-01)
    DNA sequencing is the mainstay of biological and medical research. Modern sequencing machines can read millions of DNA fragments, sampling the underlying genomes at high-throughput. Mapping the resulting reads to a reference ...
  • Deep generative models for solving geophysical inverse problems 

    Siahkoohi, Ali (Georgia Institute of Technology, 2022-07-19)
    My thesis presents several novel methods to facilitate solving large-scale inverse problems by utilizing recent advances in machine learning, and particularly deep generative modeling. Inverse problems involve reliably ...
  • High-Performance Software for Quantum Chemistry and Hierarchical Matrices 

    Erlandson, Lucas Alden (Georgia Institute of Technology, 2022-05-20)
    Linear algebra is the underpinning of a significant portion of the computation done in the modern age. Applications relying on linear algebra include physical and chemical simulations, machine learning, artificial intelligence, ...
  • DEEP LEARNING METHODS FOR MULTI-MODAL HEALTHCARE DATA 

    Biswal, Siddharth (Georgia Institute of Technology, 2022-05-19)
    Abstract: Today, enormous transformations are happening in health care research and applications. In the past few years, there has been exponential growth in the amount of healthcare data generated from multiple sources. ...
  • Finding Dense Regions of Rapidly Changing Graphs 

    Gabert, Kasimir Georg (Georgia Institute of Technology, 2022-05-02)
    Many of today's massive and rapidly changing graphs contain internal structure---hierarchies of locally dense regions---and finding and tracking this structure is key to detecting emerging behavior, exposing internal activity, ...
  • NEWS DATA VISUALIZATION INTERFACE DEVELOPMENT USING NMF ALGORITHM 

    Ahn, Byeongsoo (Georgia Institute of Technology, 2022-05-03)
    News data is a super large-scale dataset. It covers a wide range of topics ranging from heavy topics such as politics and society to beauty and entertainment, relatively light topics. At the same time, it is also the most ...
  • Understanding, Fortifying and Democratizing AI Security 

    Das, Nilaksh (Georgia Institute of Technology, 2022-04-19)
    As we steadily move towards an AI-powered utopia that could only be imagined in lofty fiction in the recent past, a formidable threat is emerging that endangers the acute capitalization of AI in our everyday lives. A growing ...
  • Knowledge Reasoning with Graph Neural Networks 

    Zhang, Yuyu (Georgia Institute of Technology, 2021-12-15)
    Knowledge reasoning is the process of drawing conclusions from existing facts and rules, which requires a range of capabilities including but not limited to understanding concepts, applying logic, and calibrating or ...
  • Towards Performance Portable Graph Algorithms 

    Yasar, Abdurrahman (Georgia Institute of Technology, 2021-12-14)
    In today's data-driven world, our computational resources have become heterogeneous, making the processing of large-scale graphs in an architecture agnostic manner crucial. Traditionally, hand-optimized high-performance ...
  • Cooperation in Multi-Agent Reinforcement Learning 

    Yang, Jiachen (Georgia Institute of Technology, 2021-12-13)
    As progress in reinforcement learning (RL) gives rise to increasingly general and powerful artificial intelligence, society needs to anticipate a possible future in which multiple RL agents must learn and interact in a ...
  • Developing Robust Models, Algorithms, Databases and Tools With Applications to Cybersecurity and Healthcare 

    Freitas, Scott (Georgia Institute of Technology, 2021-12-13)
    As society and technology becomes increasingly interconnected, so does the threat landscape. Once isolated threats now pose serious concerns to highly interdependent systems, highlighting the fundamental need for robust ...
  • Parallel Algorithms and Generalized Frameworks for Learning Large-Scale Bayesian Networks 

    Srivastava, Ankit (Georgia Institute of Technology, 2021-08-13)
    Bayesian networks (BNs) are an important subclass of probabilistic graphical models that employ directed acyclic graphs to compactly represent exponential-sized joint probability distributions over a set of random variables. ...
  • Interactive Visual Text Analytics 

    Kim, Hannah (Georgia Institute of Technology, 2020-12-07)
    Human-in-the-Loop machine learning leverages both human and machine intelligence to build a smarter model. Even with the advances in machine learning techniques, results generated by automated models can be of poor quality ...
  • Learning from Multi-Source Weak Supervision for Neural Text Classification 

    Ren, Wendi (Georgia Institute of Technology, 2020-07-28)
    Text classification is a fundamental text mining task with numerous real-life applications. While deep neural nets have achieved superior performance for text classification, they rely on large-scale labeled data to achieve ...
  • On Computation and Application of Optimal Transport 

    Xie, Yujia (Georgia Institute of Technology, 2021-07-28)
    The Optimal Transport (OT) problem naturally arises in various machine learning problems, where one needs to align data from multiple sources. For example, the training data and application scenarios oftentimes have a ...
  • Optimizing resource allocation in computational sustainability: Models, algorithms and tools 

    Gupta, Amrita (Georgia Institute of Technology, 2021-01-21)
    The 17 Sustainable Development Goals laid out by the United Nations include numerous targets as well as indicators of progress towards sustainable development. Decision-makers tasked with meeting these targets must frequently ...

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