World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8747
World Ranking
7919
National Ranking
77

Overview

Ann Nowé is affiliated with Vrije Universiteit Brussel in Belgium, contributing extensively to the fields of Computer Science and Engineering. Their research spans multiple subfields, with a primary focus on Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering, Management Science and Operations Research, and Computational Theory and Mathematics.

The scientist's main topics of work revolve around areas such as Reinforcement Learning in Robotics, Energy Load and Power Forecasting, Smart Grid Energy Management, Advanced Bandit Algorithms Research, Explainable Artificial Intelligence (XAI), Advanced Multi-Objective Optimization Algorithms, and Machine Fault Diagnosis Techniques.

Ann Nowé has authored numerous papers, including the following recent works:

  • A practical guide to multi-objective reinforcement learning and planning, 2022, Virtual Community of Pathological Anatomy (University of Castilla La Mancha)
  • Reviewing machine learning of corrosion prediction in a data-oriented perspective, 2022, npj Materials Degradation
  • A deep boosted transfer learning method for wind turbine gearbox fault detection, 2022, Renewable Energy
  • Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence, 2022, Mechanical Systems and Signal Processing
  • Autonomous agents and multiagent systems, 2021, AI Matters

Frequent collaborators of Ann Nowé include:

  • Timothy Verstraeten
  • Diederik M. Roijers
  • Jan Helsen
  • Tom Lenaerts
  • Roxana Rădulescu

The venues where Ann Nowé regularly publishes include:

  • arXiv (Cornell University)
  • Journal of Physics Conference Series
  • Wind energy science
  • Zenodo (CERN European Organization for Nuclear Research)
  • Autonomous Agents and Multi-Agent Systems

Best Publications

  • A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

    C. Lazar;J. Taminau;S. Meganck;D. Steenhoff

  • Batch effect removal methods for microarray gene expression data integration: a survey

    Cosmin Lazar;Stijn Meganck;Jonatan Taminau;David Steenhoff

  • Multi-objective reinforcement learning using sets of pareto dominating policies

    Kristof Van Moffaert;Ann Nowé

  • Scalarized multi-objective reinforcement learning: Novel design techniques

    Kristof Van Moffaert;Madalina M. Drugan;Ann Nowe

  • Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

    Jonatan Taminau;Stijn Meganck;Cosmin Lazar;David Steenhoff

  • Game Theory and Multi-agent Reinforcement Learning

    Ann Nowé;Peter Vrancx;Yann-Michaël De Hauwere

  • Reinforcement learning from demonstration through shaping

    Tim Brys;Anna Harutyunyan;Halit Bener Suay;Sonia Chernova

  • Evolutionary game theory and multi-agent reinforcement learning

    Karl Tuyls;Ann Nowé

  • Designing multi-objective multi-armed bandits algorithms: A study

    Madalina M. Drugan;Ann Nowe

  • Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

    Roxana Radulescu;Patrick Mannion;Diederik M. Roijers;Ann Nowé

  • Expressing arbitrary reward functions as potential-based advice

    Anna Harutyunyan;Sam Devlin;Peter Vrancx;Ann Nowe

  • Predicting disease-causing variant combinations

    Sofia Papadimitriou;Sofia Papadimitriou;Andrea Gazzo;Nassim Versbraegen;Charlotte Nachtegael

  • Decentralized Learning in Markov Games

    P. Vrancx;K. Verbeeck;A. Nowe

  • Hypervolume-Based Multi-Objective Reinforcement Learning

    K Kristof Van Moffaert;MM Madalina Drugan;A Ann Nowé

  • A computational method for the identification of Dengue, Zika and Chikungunya virus species and genotypes.

    Vagner S. Fonseca;Vagner S. Fonseca;Vagner S. Fonseca;Pieter J. K. Libin;Pieter J. K. Libin;Kristof Theys;Nuno Rodrigues Faria

  • Model-predictive control and reinforcement learning in multi-energy system case studies

    Glenn Ceusters;Román Cantú Rodríguez;Alberte Bouso García;Rüdiger Franke

  • Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem

    R. Bello;Y. Gomez;M.M. Garcia;A. Nowe

  • Learning multi-agent state space representations

    Yann-Michaël De Hauwere;Peter Vrancx;Ann Nowé

  • Reinforcement Learning: State-of-the-Art

    A. Nowé;P. Vrancx;Y-M. De Hauwere

  • Multi-objectivization of reinforcement learning problems by reward shaping

    Tim Brys;Anna Harutyunyan;Peter Vrancx;Matthew E. Taylor

  • Distilling Deep Reinforcement Learning Policies in Soft Decision Trees

    Youri Coppens;Kyriakos Efthymiadis;Tom Lenaerts;Ann Nowé

  • Introduction to Game Theory

    Karl Tuyls;Ann Nowe

Frequent Co-Authors

Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Martine De Cock
Martine De Cock University of Washington
Hugues Bersini
Hugues Bersini Université Libre de Bruxelles
Anne-Mieke Vandamme
Anne-Mieke Vandamme Rega Institute for Medical Research
Joost Duflou
Joost Duflou KU Leuven
Mario Pickavet
Mario Pickavet Ghent University
Piet Demeester
Piet Demeester Ghent University
Didier Colle
Didier Colle Ghent University
Yves Moreau
Yves Moreau KU Leuven

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