World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
122900
World Ranking
3507
National Ranking
1687

Overview

Alex Graves is affiliated with Google in the United States and has contributed research across multiple scientific disciplines, focusing primarily on biochemistry, genetics, molecular biology, and computer science. Their work intersects medicine, particularly in areas related to stroke rehabilitation and recovery.

Graves's research spans several interconnected topics, including:

  • Bioinformatics and Genomic Networks
  • Stroke Rehabilitation and Recovery
  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Biomedical Text Mining and Ontologies
  • Gene expression and cancer classification
  • Acute Ischemic Stroke Management

Their frequent publication venues demonstrate a consistent interest in both computational and medical sciences, with papers appearing in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Stroke
  • arXiv (Cornell University)
  • Nature Communications

Graves has collaborated extensively with several coauthors, including Timothy Atkinson, Thomas D. Barrett, Bora Guloglu, Liviu Copoiu, and Alexandre Laterre.

Recent papers authored or coauthored by Graves include:

  • Protein sequence modelling with Bayesian flow networks, 2025, Nature Communications
  • Bayesian Flow Networks, 2023, arXiv (Cornell University)
  • A Practical Sparse Approximation for Real Time Recurrent Learning, 2020, arXiv (Cornell University)
  • Protein Sequence Modelling with Bayesian Flow Networks, 2024, bioRxiv (Cold Spring Harbor Laboratory)
  • Abstract P848: A Better Way to NIHSS, 2021, Stroke

The combination of biochemistry and machine learning found in Graves's work highlights an interdisciplinary approach to understanding protein dynamics and computational methods for biological data analysis. Their investigation into stroke rehabilitation adds a clinical dimension, reflecting research that spans from molecular biology to medical applications.

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Speech recognition with deep recurrent neural networks

    Alex Graves;Abdel-rahman Mohamed;Geoffrey Hinton

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

    Alex Graves;Santiago Fernández;Faustino Gomez;Jürgen Schmidhuber

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • 2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures

    Alex Graves;Jürgen Schmidhuber

  • Generating Sequences With Recurrent Neural Networks

    Alex Graves

  • Framewise phoneme classification with bidirectional LSTM and other neural network architectures

    Alex Graves;Jürgen Schmidhuber

  • Supervised Sequence Labelling with Recurrent Neural Networks

    Alexander Graves

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;koray kavukcuoglu

  • Supervised Sequence Labelling

    Alex Graves

  • A Novel Connectionist System for Unconstrained Handwriting Recognition

    A. Graves;M. Liwicki;S. Fernandez;R. Bertolami

  • Towards End-To-End Speech Recognition with Recurrent Neural Networks

    Alex Graves;Navdeep Jaitly

  • Hybrid speech recognition with Deep Bidirectional LSTM

    Alex Graves;Navdeep Jaitly;Abdel-rahman Mohamed

  • Conditional image generation with PixelCNN decoders

    Aäron van den Oord;Nal Kalchbrenner;Oriol Vinyals;Lasse Espeholt

  • Long Short-Term Memory

    Alex Graves

  • Neural Turing Machines

    Alex Graves;Greg Wayne;Ivo Danihelka

  • DRAW: A Recurrent Neural Network For Image Generation

    Karol Gregor;Ivo Danihelka;Alex Graves;Danilo Rezende

  • Hybrid computing using a neural network with dynamic external memory

    Alex Graves;Greg Wayne;Malcolm Reynolds;Tim Harley

  • Sequence Transduction with Recurrent Neural Networks

    Alex Graves

  • Towards End-to-End Speech Recognitionwith Recurrent Neural Networks

    Alex Graves;Navdeep Jaitly

Frequent Co-Authors

Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Aaron van den Oord
Aaron van den Oord Google (United States)
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Björn Schuller
Björn Schuller Imperial College London
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Florian Eyben
Florian Eyben Technical University of Munich

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