World's Best Scientists 2026 revealed!

Overview

András György is affiliated with New York University Abu Dhabi in the United Arab Emirates. Their research primarily focuses on computer science, with significant contributions in artificial intelligence, management science and operations research, and electrical and electronic engineering. Their work also spans specialized areas such as computer networks and communications, and epidemiology.

The main topics covered in their research include advanced bandit algorithms, machine learning and algorithms, reinforcement learning in robotics, adversarial robustness in machine learning, age of information optimization, IoT networks and protocols, and domain adaptation with few-shot learning.

Among their recent publications are:

  • "The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection" (2020, IRIS UNIMORE - University of Modena and Reggio Emilia)
  • "A reinforcement learning approach to age of information in multi-user networks with HARQ" (2021, IRIS UNIMORE - University of Modena and Reggio Emilia)
  • "Author Correction: Perception, performance, and detectability of conversational artificial intelligence across 32 university courses" (2023, Scientific Reports)
  • "Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting" (2021, arXiv - Cornell University)
  • "On the Role of Neural Collapse in Transfer Learning" (2021, arXiv - Cornell University)

András György frequently publishes in venues such as arXiv (Cornell University), with 35 papers, IRIS UNIMORE (University of Modena and Reggio Emilia), Scientific Reports, IEEE Transactions on Information Forensics and Security, and IEEE Journal on Selected Areas in Communications.

The scientist regularly collaborates with several coauthors, including:

  • Csaba Szepesvári (10 joint papers)
  • Denız Gündüz (6 joint papers)
  • Claire Vernade (5 joint papers)
  • Gellért Weisz (5 joint papers)
  • Elif Tuğçe Ceran (4 joint papers)

Best Publications

  • Average Age of Information With Hybrid ARQ Under a Resource Constraint

    Elif Tugce Ceran;Deniz Gunduz;Andras Gyorgy

  • The On-Line Shortest Path Problem Under Partial Monitoring

    András György;Tamás Linder;Gábor Lugosi;György Ottucsák

  • Online Learning under Delayed Feedback

    Pooria Joulani;Andras Gyorgy;Csaba Szepesvari

  • Degenerate Feedback Loops in Recommender Systems

    Ray Jiang;Silvia Chiappa;Tor Lattimore;András György

  • A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks

    Samuel O. Somuyiwa;Andras Gyorgy;Deniz Gunduz

  • Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection.

    Andrea Paudice;Luis Muñoz-González;András György;Emil C. Lupu

  • Online Markov Decision Processes under Bandit Feedback

    Gergely Neu;Andras Antos;András György;Csaba Szepesvári

  • Optimal entropy-constrained scalar quantization of a uniform source

    A. Gyorgy;T. Linder

  • On the structure of optimal entropy-constrained scalar quantizers

    A. Gyorgy;T. Linder

  • Efficient Tracking of Large Classes of Experts

    A. Gyorgy;T. Linder;G. Lugosi

  • The adversarial stochastic shortest path problem with unknown transition probabilities

    Gergely Neu;András György;Csaba Szepesvári

  • High-dimensional random geometric graphs and their clique number

    Luc Devroye;András György;Gábor Lugosi;Frederic Udina

  • Efficient Multi-Start Strategies for Local Search Algorithms

    András György;Levente Kocsis

  • The Online Loop-free Stochastic Shortest-Path Problem.

    Gergely Neu;András György;Csaba Szepesvári

  • Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost

    Elif Tugce Ceran;Deniz Gunduz;Andras Gyorgy

  • The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection

    Muhammad Zaid Hameed;Andras Gyorgy;Deniz Gunduz

  • Online Learning in Markov Decision Processes with Changing Cost Sequences

    Travis Dick;Andras Gyorgy;Csaba Szepesvari

  • Meta-learning of Sequential Strategies.

    Pedro A. Ortega;Jane X. Wang;Mark Rowland;Tim Genewein

  • Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities

    Ruitong Huang;Tor Lattimore;András György;Csaba Szepesvári

  • Efficient multi-start strategies for local search algorithms

    Levente Kocsis;András György

Frequent Co-Authors

Csaba Szepesvári
Csaba Szepesvári University of Alberta
Tamas Linder
Tamas Linder Queen's University
Deniz Gunduz
Deniz Gunduz Imperial College London
Gábor Lugosi
Gábor Lugosi Pompeu Fabra University
Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Michael Bowling
Michael Bowling University of Alberta
Dale Schuurmans
Dale Schuurmans University of Alberta
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Wilsun Xu
Wilsun Xu University of Alberta

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