University of Oxford
Ali FarhadiCorporate Vice President · Microsoft AIResearcher↗
Researcher · Founder
Karén Simonyan
Microsoft AIChief Scientist
Simonyan helped shape two generations of modern AI: he co-created VGGNet, whose simple, deep design became a standard for image recognition[1], then helped develop AlphaGo and AlphaZero—systems that showed how machines could learn complex strategies through self-play.[2][3] His work spans computer vision and reinforcement learning.
Career & education
Current roleMicrosoft AI2024-presentChief Scientist
Microsoft AI2024-presentPreviously
Inflection AI2022-2024Co-founder and Chief Scientist
Google DeepMind2014-2022Research Scientist
Inflection AI2022-2024Google DeepMind2014-2022Education
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations321,877As of Nov 1, '26
citations+3,952Since last snapshot, Oct 1, '26
Selected contributions
Very Deep Convolutional Networks for Large-Scale Image Recognition
With Andrew ZissermanVGG networks improved image recognition by stacking more layers.
Mastering the game of Go without human knowledge
With David Silver, Julian Schrittwieser, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker and others ·AlphaGo Zero learned Go through self-play without examples of human games.
WaveNet: A Generative Model for Raw Audio
With Aäron van den Oord, Sander Dieleman, Heiga Zen, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior and others ·WaveNet generates natural-sounding speech directly as a sequence of audio samples.
References
- Very Deep Convolutional Networks for Large-Scale Image Recognitionarxiv.org
he co-created VGGNet, whose simple, deep design became a standard for image recognition
- Mastering the game of Go without human knowledge — AlphaGo Zeronature.com
then helped develop AlphaGo and AlphaZero—systems that showed how machines could learn complex strategies through self-play.
- Mastering Chess and Shogi by Self-Play — AlphaZeroarxiv.org
then helped develop AlphaGo and AlphaZero—systems that showed how machines could learn complex strategies through self-play.
Based on public sources. If accuracy is important, verify details using the links or references.
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Reviewed August 13, 2026 · Checked for accuracy and new information.
