Recent Submissions

  • 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 ...
  • On Using Inductive Biases for Designing Deep Learning Architectures 

    Shrivastava, Harsh (Georgia Institute of Technology, 2020-12-15)
    Recent advancements in field of Artificial Intelligence, especially in the field of Deep Learning (DL), have paved way for new and improved solutions to complex problems occurring in almost all domains. Often we have some ...
  • Performance Primitives for Artificial Neural Networks 

    Dukhan, Marat (Georgia Institute of Technology, 2021-05-10)
    Optimized software implementations of artificial neural networks leverage primitives from performance libraries, such as the BLAS. However, these primitives were prototyped decades ago, and do not necessarily reflect the ...
  • Interval Deep Learning for Uncertainty Quantification in Engineering Problems 

    Betancourt, David (Georgia Institute of Technology, 2021-05-06)
    Deep neural networks are becoming more common in important real-world safety-critical applications where reliability in the predictions is paramount. Despite their exceptional prediction capabilities, current deep neural ...
  • Data-Driven Approach using Machine Learning for Real-Time Flight Path Optimization 

    Kim, Jung Hyun (Georgia Institute of Technology, 2021-04-27)
    Airlines typically gather all available weather information before departure to generate flight routes that avoid hazardous weather while minimizing operating expenditures. However, pilots potentially have to perform ...
  • ROBUST COUNTERFACTUAL LEARNING FOR CLINICAL DECISION-MAKING USING ELECTRONIC HEALTH RECORDS 

    Choudhary, Anirudh (Georgia Institute of Technology, 2020-12-07)
    Building clinical decision support systems, which includes diagnosing patient’s disease states and formulating a treatment plan, is an important step toward personalized medicine. The counterfactual nature of clinical ...
  • Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes 

    Gemayel, Karl (Georgia Institute of Technology, 2020-12-01)
    Prokaryotic gene-prediction is the task of finding genes in archaeal or bacterial DNA sequences. These genomes consist of alternating gene-coding and non-coding regions, meaning the task is solved by determining the start ...
  • Interactive Scalable Interfaces for Machine Learning Interpretability 

    Hohman, Frederick (Georgia Institute of Technology, 2020-12-01)
    Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has ...
  • Deep representation learning on hypersphere 

    Liu, Weiyang (Georgia Institute of Technology, 2020-07-27)
    How to efficiently learn discriminative deep features is arguably one of the core problems in deep learning, since it can benefit a lot of downstream tasks such as visual recognition, object detection, semantic segmentation, ...
  • Asynchronous versions of Jacobi, multigrid, and Chebyshev solvers 

    Wolfson-Pou, Jordi (Georgia Institute of Technology, 2020-07-06)
    Iterative methods are commonly used for solving large, sparse systems of linear equations on parallel computers. Implementations of parallel iterative solvers contain kernels (e.g., parallel sparse matrix-vector products) ...
  • Learning dynamic processes over graphs 

    Trivedi, Rakshit (Georgia Institute of Technology, 2020-07-09)
    Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...
  • Large scale machine learning for geospatial problems in computational sustainability 

    Robinson, David Caleb (Georgia Institute of Technology, 2020-05-14)
    The UN laid out 17 Sustainable Development Goals as part of the “The 2030 Agenda for Sustainable Development”. Each goal consists of broad targets - such as increasing the percentage of forested land (indicator 15.1.1) - ...
  • Health data mining using tensor factorization: Methods and applications 

    Perros, Ioakeim (Georgia Institute of Technology, 2019-05-16)
    The increasing volume and availability of healthcare and biomedical data is opening up new opportunities for the use of computational methods to improve health. However, the data are diverse, multidimensional and sparse, ...
  • Modeling for inversion in exploration geophysics 

    Louboutin, Mathias (Georgia Institute of Technology, 2020-03-16)
    Seismic inversion, and more generally geophysical exploration, aims at better understanding the earth's subsurface, which is one of today's most important challenges. Firstly, it contains natural resources that are critical ...
  • Software and algorithms for large-scale seismic inverse problems 

    Witte, Philipp Andre (Georgia Institute of Technology, 2020-02-26)
    Seismic imaging and parameter estimation are an import class of inverse problems with practical relevance in resource exploration, carbon control and monitoring systems for geohazards. Seismic inverse problems involve ...
  • Fault-tolerance on near-term quantum computers and subsystem quantum error correcting codes 

    Li, Muyuan (Georgia Institute of Technology, 2020-03-20)
    Large-scale fault-tolerant quantum computers are supposed to provide exponential speedup over many classical algorithms for solving realistic computationally intensive problems. Given that practical quantum computers are ...
  • Learning neural algorithms with graph structures 

    Dai, Hanjun (Georgia Institute of Technology, 2020-01-13)
    Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis. Over the past several decades, ...
  • Towards tighter integration of machine learning and discrete optimization 

    Khalil, Elias (Georgia Institute of Technology, 2019-03-28)
    Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...
  • Efficient parallel algorithms for error correction and transcriptome assembly of biological sequences 

    Sachdeva, Vipin (Georgia Institute of Technology, 2018-05-29)
    Next-generation sequencing technologies have led to a big data age in biology. Since the sequencing of the human genome, the primary bottleneck has steadily moved from collection to storage and analysis of the data. The ...
  • Human-centered AI through scalable visual data analytics 

    Kahng, Minsuk Brian (Georgia Institute of Technology, 2019-11-01)
    While artificial intelligence (AI) has led to major breakthroughs in many domains, understanding machine learning models remains a fundamental challenge. How can we make AI more accessible and interpretable, or more broadly, ...

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