World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8390
World Ranking
10536
National Ranking
4415

Overview

Tim Oates is affiliated with the University of Maryland, Baltimore County in the United States. Their research primarily spans the field of Engineering, with a focus on several subfields including Mechanics of Materials, Mechanical Engineering, Control and Systems Engineering, Electrical and Electronic Engineering, and Artificial Intelligence.

Their scholarly work shows a concentration on topics related to mineral and mining engineering as well as advanced computational methods. Key topics of their research include:

  • Mineral Processing and Grinding
  • Mining Techniques and Economics
  • Rock Mechanics and Modeling
  • Geotechnical and Geomechanical Engineering
  • Graph theory and CDMA systems
  • Coding theory and cryptography

Tim Oates has published multiple articles in notable venues. Frequent publication outlets include:

  • Journal of the Southern African Institute of Mining and Metallurgy
  • arXiv (Cornell University)

The most recent papers by Tim Oates are:

  • "Stemming and best practice in the mining industry: A literature review," 2021, Journal of the Southern African Institute of Mining and Metallurgy
  • "A study of UG2 pillar strength using a new pillar database," 2023, Journal of the Southern African Institute of Mining and Metallurgy
  • "A Walsh Hadamard Derived Linear Vector Symbolic Architecture," 2024, arXiv (Cornell University)

Throughout their work, Tim Oates has collaborated with several researchers, including:

  • W. Spiteri
  • D.F. Malan
  • Mohammad Mahmudul Alam
  • Alexander Oberle
  • Edward Raff

The breadth of Tim Oates's research reflects a multidisciplinary approach, combining engineering principles with computational and theoretical methods. Their contributions cover the study of rock and mineral behavior in mining contexts, along with explorations in coding theory and graph-based communication systems.

Best Publications

  • Time series classification from scratch with deep neural networks: A strong baseline

    Zhiguang Wang;Weizhong Yan;Tim Oates

  • Imaging time-series to improve classification and imputation

    Zhiguang Wang;Tim Oates

  • Efficient progressive sampling

    Foster Provost;David Jensen;Tim Oates

  • Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks

    Zhiguang Wang;Tim Oates

  • The Effects of Training Set Size on Decision Tree Complexity

    Tim Oates;David Jensen

  • Detecting spam blogs: a machine learning approach

    Pranam Kolari;Akshay Java;Tim Finin;Tim Oates

  • Modeling the Spread of Influence on the Blogosphere

    Akshay Java;Pranam Kolari;Tim Finin;Tim Oates

  • Using dynamic time warping to bootstrap HMM-based clustering of time series

    Tim Oates;Laura Firoiu;Paul R. Cohen

  • Cooperative information-gathering: a distributed problem-solving approach

    Tim Oates;M. V. Nagendra Prasad;Victor R. Lesser

  • Identifying distinctive subsequences in multivariate time series by clustering

    Tim Oates

  • Searching for Structure in Multiple Streams of Data.

    Tim Oates;Paul R. Cohen

  • A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments

    Tim Oates;Matthew D. Schmill;Paul R. Cohen

  • A Review of Recent Research in Metareasoning and Metalearning

    Michael L. Anderson;Tim Oates

  • A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection

    Adam Page;Chris Sagedy;Emily Smith;Nasrin Attaran

  • PERUSE: An unsupervised algorithm for finding recurring patterns in time series

    T. Oates

  • Large datasets lead to overly complex models: an explanation and a solution

    Tim Oates;David Jensen

  • Visualizing Variable-Length Time Series Motifs.

    Yuan Li;Jessica Lin;Tim Oates

  • Hierarchical Bayesian Models for Latent Attribute Detection in Social Media.

    Delip Rao;Michael J. Paul;Clayton Fink;David Yarowsky

  • SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification

    Ali Jafari;Ashwinkumar Ganesan;Chetan Sai Kumar Thalisetty;Varun Sivasubramanian

  • Neo: learning conceptual knowledge by sensorimotor interaction with an environment

    Paul R. Cohen;Marc S. Atkin;Tim Oates;Carole R. Beal

  • The metacognitive loop I: Enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance

    Michael L. Anderson;Tim Oates;Waiyian Chong;Donald Perlis

Frequent Co-Authors

Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Tim Finin
Tim Finin University of Maryland, Baltimore County
David Jensen
David Jensen University of Massachusetts Amherst
Anupam Joshi
Anupam Joshi University of Maryland, Baltimore County
Douglas W. Oard
Douglas W. Oard University of Maryland, College Park
Veselin Stoyanov
Veselin Stoyanov Facebook (United States)
Yelena Yesha
Yelena Yesha University of Miami
Victor Lesser
Victor Lesser University of Massachusetts Amherst
Mohamed Younis
Mohamed Younis University of Maryland, Baltimore County

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