PeopleofSuperintelligence

Researcher

Alexander Novikov

Alexander Novikov is a Research Scientist at Google DeepMind whose research includes neural-network compression, reinforcement learning and algorithm discovery. He coauthored Tensorizing Neural Networks, completed a PhD under Dmitry Vetrov and Ivan Oseledets, and worked on offline reinforcement learning at DeepMind by 2021. At DeepMind, he contributed to AlphaTensor for matrix multiplication and quantum circuits, helped develop AlphaEvolve, and coauthored Magellan for compiler optimization. As of October 2026, he focuses on machine learning and algorithmic discovery at Google DeepMind.

Works
24
Citations
2,275
h-index
14
Badges

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

  1. 2015

    Compressing neural networks

    In 2015, he coauthors Tensorizing Neural Networks, which uses the Tensor Train format to reduce parameters in fully connected layers while preserving expressive power.

  2. By 2021

    DeepMind and offline RL

    By 2021, he has completed a PhD under Dmitry Vetrov and Ivan Oseledets and works as a senior research scientist at DeepMind on offline reinforcement learning.

  3. 2022

    AlphaTensor discovers algorithms

    In 2022, he contributes to AlphaTensor, the DeepMind system that uses reinforcement learning to discover new matrix multiplication algorithms.

  4. March 2025

    Optimizing quantum circuits

    In March 2025, he coauthors Quantum circuit optimization with AlphaTensor, which applies deep reinforcement learning and tensor decomposition to optimize quantum circuits.

  5. May 2025

    Developing AlphaEvolve

    In May 2025, he helps develop AlphaEvolve, a Gemini-powered coding agent that uses large language models and evolutionary search to discover and optimize algorithms.

  6. 2026

    Automating compiler heuristics

    In 2026, he coauthors Magellan, an agentic framework that uses a language-model coding agent, evolutionary search and autotuning to evolve compiler optimization heuristics.

  7. As of October 2026

    Research at Google DeepMind

    As of October 2026, he is a Research Scientist at Google DeepMind, focused on machine learning and algorithmic discovery.

A language model matched the links in this timeline to people, organizations and papers (provisional, model-judged).

Career

  1. Since 2022Google DeepMindSenior Research Scientist
  2. 2016–2018National Research University Higher School of Economics
  3. 2015–2018Department of Mathematical Sciences

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. Tensorizing Neural Networks2015 · 658 citations
  2. Mathematical discoveries from program search with large language models2023 · Nature · 518 citationsWith Alhussein Fawzi, Bernardino Romera-Paredes, Matej Balog and Pushmeet Kohli
  3. Discovering faster matrix multiplication algorithms with reinforcement learning2022 · Nature · 477 citationsWith Aja Huang, Alhussein Fawzi, Bernardino Romera-Paredes, David Silver, Demis Hassabis and Julian Schrittwieser
  4. Ultimate tensorization: compressing convolutional and FC layers alike2016 · 142 citations
  5. Critic Regularized Regression2020 · 128 citationsWith Caglar Gulcehre, Nando de Freitas and Scott Reed

From OpenAlex, as of 18 October 2026.

  1. The median h-index is 55 at universities and 20 at frontier AI labsIncludes Alexander Novikov. The median h-index is 55 at universities and 20 at frontier AI labs. We compare publishing careers, citations and moves to estimate how universities and frontier labs contribute to AI research.
  2. 13% of AI career moves in the 2020s went from a university to a companyIncludes Alexander Novikov. Our estimates of how university-to-company career transitions changed by decade, including roles added alongside an earlier job.
  3. Google has the highest combined h-index among AI organizationsIncludes Google. Our estimates of combined and median h-index at AI organizations, using current affiliations and OpenAlex records.
  4. Google alumni founded or led 79 other organizations, the most among the labs we compareIncludes Google. We trace 181 organizations founded or led by AI lab alumni, and 187 career moves between the labs.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Tensorizing Neural Networks arxiv.org. Checked 18 October 2026.
  2. OpenTalks.AI - Speakers 2021 opentalks.ai. Checked 18 October 2026.
  3. Discovering novel algorithms with AlphaTensor deepmind.google. Checked 18 October 2026.
  4. Quantum circuit optimization with AlphaTensor nature.com. Checked 18 October 2026.
  5. AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms deepmind.google. Checked 18 October 2026.
  6. Magellan: Autonomous Discovery of Novel Compiler Optimization Heuristics with AlphaEvolve arxiv.org. Checked 18 October 2026.
  7. 1st Workshop on Machine Learning and Combinatorial Optimization (ML&CO 2026) ai4co.org. Checked 18 October 2026.