2026 Ilya Sutskever: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	68	2012	1949	1014	975	113	418151

Overview

Ilya Sutskever is affiliated with OpenAI in the United States and is primarily active in the field of Computer Science. Their work has contributed substantially to several subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems, and Computational Theory and Mathematics.

The scientist's research spans a variety of topics including:

Topic Modeling
Natural Language Processing Techniques
Multimodal Machine Learning Applications
Speech and Audio Processing
Generative Adversarial Networks and Image Synthesis
Text Readability and Simplification
Speech Recognition and Synthesis

Among recent publications, the following works are notable:

Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper), 2023, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Learning Transferable Visual Models From Natural Language Supervision, 2021, arXiv (Cornell University)
Language Models are Few-Shot Learners, 2020, arXiv (Cornell University)
Evaluating Large Language Models Trained on Code, 2021, arXiv (Cornell University)
Zero-Shot Text-to-Image Generation, 2021, arXiv (Cornell University)

Frequent coauthors of Sutskever include:

Alec Radford
Aditya Ramesh
Mark Chen
Prafulla Dhariwal
Pranav Shyam

The scientist has published extensively in several venues, with the highest number of publications appearing in arXiv (Cornell University). Other publication venues include Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Journal of Statistical Mechanics Theory and Experiment, Dagstuhl Research Online Publication Server, and Geoscientist.

Best Publications

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky;Ilya Sutskever;Geoffrey E. Hinton

213588
Citations
Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava;Geoffrey Hinton;Alex Krizhevsky;Ilya Sutskever

43248
Citations
Distributed Representations of Words and Phrases and their Compositionality

Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado

31424
Citations
Sequence to Sequence Learning with Neural Networks

Ilya Sutskever;Oriol Vinyals;Quoc V. Le

19434
Citations
Mastering the game of Go with deep neural networks and tree search

David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

17878
Citations
Intriguing properties of neural networks

Christian Szegedy;Wojciech Zaremba;Ilya Sutskever;Joan Bruna

12789
Citations
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

12781
Citations
Improving neural networks by preventing co-adaptation of feature detectors

Geoffrey E. Hinton;Nitish Srivastava;Alex Krizhevsky;Ilya Sutskever

10528
Citations
Learning Transferable Visual Models From Natural Language Supervision

Alec Radford;Jong Wook Kim;Chris Hallacy;Aditya Ramesh

9393
Citations
On the importance of initialization and momentum in deep learning

Ilya Sutskever;James Martens;George Dahl;Geoffrey Hinton

4711
Citations
Language Models are Few-Shot Learners

Tom B. Brown;Benjamin Mann;Nick Ryder;Melanie Subbiah

4104
Citations
InfoGAN: interpretable representation learning by information maximizing generative adversarial nets

Xi Chen;Yan Duan;Rein Houthooft;John Schulman

4021
Citations
Recurrent Neural Network Regularization

Wojciech Zaremba;Ilya Sutskever;Oriol Vinyals

3012
Citations
Exploiting Similarities among Languages for Machine Translation

Tomas Mikolov;Quoc V. Le;Ilya Sutskever

2167
Citations
Robust Speech Recognition via Large-Scale Weak Supervision

Unknown

1966
Citations
An Empirical Exploration of Recurrent Network Architectures

Rafal Jozefowicz;Wojciech Zaremba;Wojciech Zaremba;Ilya Sutskever

1921
Citations
Generating Text with Recurrent Neural Networks

Ilya Sutskever;James Martens;Geoffrey E. Hinton

1676
Citations
Improved Variational Inference with Inverse Autoregressive Flow

Durk P. Kingma;Tim Salimans;Rafal Jozefowicz;Xi Chen

1515
Citations
Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Tim Salimans;Jonathan Ho;Xi Chen;Ilya Sutskever

1421
Citations
Evaluating Large Language Models Trained on Code

Mark Chen;Jerry Tworek;Heewoo Jun;Qiming Yuan

1401
Citations
Dota 2 with Large Scale Deep Reinforcement Learning

Christopher Berner;Greg Brockman;Brooke Chan;Vicki Cheung

1168
Citations
Zero-Shot Text-to-Image Generation

Aditya Ramesh;Mikhail Pavlov;Gabriel Goh;Scott Gray

1076
Citations
Generating Long Sequences with Sparse Transformers.

Rewon Child;Scott Gray;Alec Radford;Ilya Sutskever

809
Citations

Frequent Co-Authors

Geoffrey E. Hinton University of Toronto

Wojciech Zaremba OpenAI

Quoc V. Le Google (United States)

Pieter Abbeel University of California, Berkeley

Oriol Vinyals DeepMind (United Kingdom)

John Schulman OpenAI

Navdeep Jaitly Google (United States)

Shixiang Gu Google (United States)

Jeffrey Dean Google (United States)

Sergey Levine University of California, Berkeley

External Links

Wikipedia of Ilya Sutskever Personal website of Ilya Sutskever Google Scholar page

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