World's Best Scientists 2026 revealed!

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Computer Science

D-Index
44
Citations
10630
World Ranking
7445
National Ranking
236

Overview

Fabio Ramos is affiliated with the University of Sydney in Australia and has contributed extensively to research in robotics and artificial intelligence. Their work spans key areas such as robot manipulation, path planning, reinforcement learning, and sensor-based localization.

The main fields of study for Fabio Ramos include Computer Science and Engineering. Within these areas, they specialize in various subfields including Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Aerospace Engineering, and Statistical and Nonlinear Physics.

Fabio Ramos's research topics cover:

  • Robot Manipulation and Learning
  • Robotic Path Planning Algorithms
  • Reinforcement Learning in Robotics
  • Robotics and Sensor-Based Localization
  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Algorithms
  • Advanced Control Systems Optimization

The scientist has published papers in multiple venues, with a notable presence in:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2022 International Conference on Robotics and Automation (ICRA)
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Recent publications showcasing Fabio Ramos's research contributions include:

  • "Robot Learning From Randomized Simulations: A Review," 2022, Frontiers in Robotics and AI
  • "Bayesian Local Sampling-Based Planning," 2020, IEEE Robotics and Automation Letters
  • "Stein ICP for Uncertainty Estimation in Point Cloud Matching," 2021, IEEE Robotics and Automation Letters
  • "Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation," 2022, IEEE Robotics and Automation Letters
  • "STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation," 2021, arXiv (Cornell University)

Fabio Ramos frequently collaborates with various researchers, including:

  • Dieter Fox
  • Tin Lai
  • Yashraj Narang
  • Lionel Ott
  • Eric Heiden

Best Publications

  • Simple online and realtime tracking

    Alex Bewley;Zongyuan Ge;Lionel Ott;Fabio Ramos

  • Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network

    Edmar Rezende;Guilherme Ruppert;Tiago Carvalho;Fabio Ramos

  • Gaussian process occupancy maps

    Simon T O'Callaghan;Fabio T Ramos

  • Gaussian process modeling of large-scale terrain

    Shrihari Vasudevan;Fabio Ramos;Eric Nettleton;Hugh Durrant-Whyte

  • Bayesian optimisation for Intelligent Environmental Monitoring

    Roman Marchant;Fabio Ramos

  • Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent

    Fabio Tozeto Ramos;Lionel Ott

  • Gaussian process modeling of large-scale terrain: Vasudevan et al.: Gaussian Process Modeling of Large-Scale Terrain

    Shrihari Vasudevan;Fabio Ramos;Eric Nettleton;Hugh Durrant-Whyte

  • Bayesian Optimisation for informative continuous path planning.

    Román Marchant;Fabio Tozeto Ramos

  • Malicious Software Classification Using VGG16 Deep Neural Network’s Bottleneck Features

    Edmar Rezende;Guilherme Ruppert;Tiago Carvalho;Antonio Theophilo

  • Unsupervised clustering of people from 'skeleton' data

    Adrian Ball;David Rye;Fabio Ramos;Mari Velonaki

  • Airborne vision-based mapping and classification of large farmland environments

    Mitch Bryson;Alistair Reid;Fabio Ramos;Salah Sukkarieh

  • BayesSim: Adaptive Domain Randomization Via Probabilistic Inference for Robotics Simulators

    Fabio Ramos;Rafael Carvalhaes Possas;Dieter Fox

  • A sparse covariance function for exact Gaussian process inference in large datasets

    Arman Melkumyan;Fabio Ramos

  • Modeling and decision making in spatio-temporal processes for environmental surveillance

    Amarjeet Singh;Fabio Ramos;Hugh Durrant Whyte;William J Kaiser

  • CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching

    Fabio T. Ramos;Dieter Fox;Hugh F. Durrant-Whyte

  • Classification and Semantic Mapping of Urban Environments

    B. Douillard;D. Fox;F. Ramos;H. Durrant-Whyte

  • Airborne vision-based mapping and classification of large farmland environments: Bryson et al.: Airborne Vision-Based Mapping and Classification of Farmlands

    Mitch Bryson;Alistair Reid;Fabio Ramos;Salah Sukkarieh

  • Contextual occupancy maps using Gaussian processes

    Simon O'Callaghan;Fabio. T. Ramos;Hugh Durrant-Whyte

  • Multi-kernel Gaussian processes

    Arman Melkumyan;Fabio Ramos

  • Hilbert maps

    Fabio Ramos;Lionel Ott

  • IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

    Ajay Mandlekar;Fabio Ramos;Byron Boots;Silvio Savarese

  • Gaussian Process modeling of large scale terrain

    Shrihari Vasudevan;Fabio Ramos;Eric Nettleton;Hugh Durrant-Whyte

Frequent Co-Authors

Hugh Durrant-Whyte
Hugh Durrant-Whyte University of Sydney
Ben Upcroft
Ben Upcroft Queensland University of Technology
Dieter Fox
Dieter Fox University of Washington
Byron Boots
Byron Boots University of Washington
Salah Sukkarieh
Salah Sukkarieh University of Sydney
Bernard W. Balleine
Bernard W. Balleine University of New South Wales
Juan Nieto
Juan Nieto Microsoft (United States)
Peter Dayan
Peter Dayan Max Planck Institute for Biological Cybernetics
Anthony Harris
Anthony Harris University of Sydney
Ruzena Bajcsy
Ruzena Bajcsy University of California, Berkeley

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