PeopleofSuperintelligence

Researcher

Emma Bou Hanna

Emma Bou Hanna is an AI researcher whose work spans single-cell biology and models for language, vision, reasoning, audio, and coding. She coauthored a 2023 study on UnitedNet, an explainable neural network for single-cell multimodal data. Her papers covered Mixtral 8x7B and Pixtral 12B in 2024, then Magistral, Voxtral, and Devstral-Small in 2025. As of October 2026, she is an AI Scientist and one of MAIA's administrators, a Mines Paris Alumni network for artificial intelligence, data science, and machine learning professionals.

Timeline

  1. 2023

    UnitedNet for single-cell data

    UnitedNet is an explainable multi-task neural network that she coauthors in 2023 for analyzing single-cell multimodal data.

  2. January 2024

    Mixtral 8x7B

    In January 2024, she coauthors Mixtral of Experts, which introduces Mixtral 8x7B, a sparse mixture-of-experts language model that routes each token through two of eight experts.

  3. October 2024

    Pixtral 12B

    Pixtral 12B is a 12-billion-parameter multimodal model for images and documents, and she coauthors its paper in October 2024. Its vision encoder accepts images at their natural resolution and aspect ratio.

  4. June 2025

    The Magistral reasoning model

    She coauthors Magistral in June 2025, Mistral's first reasoning model; the paper also describes a scalable reinforcement-learning pipeline for language models.

  5. July 2025

    Voxtral audio models

    The July 2025 Voxtral paper she coauthors presents Voxtral Mini and Voxtral Small as multimodal audio chat models that understand speech and text. Voxtral Small can run locally and handle audio files up to 40 minutes.

  6. August 2025

    Devstral for coding agents

    A technical report she coauthors in August 2025 introduces Devstral-Small, a lightweight open model for coding agents. It describes a 24B model for agentic software development.

  7. As of October 2026

    AI Scientist and MAIA administrator

    As of October 2026, she is an AI Scientist and one of MAIA's administrators, a Mines Paris Alumni network for artificial intelligence, data science, and machine learning professionals.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. Explainable multi-task learning for multi-modality biological data analysis nature.com. Checked 19 October 2026.
  2. Mixtral of Experts arxiv.org. Checked 19 October 2026.
  3. Pixtral 12B arxiv.org. Checked 19 October 2026.
  4. Magistral arxiv.org. Checked 19 October 2026.
  5. Voxtral arxiv.org. Checked 19 October 2026.
  6. Devstral: Fine-tuning Language Models for Coding Agent Applications arxiv.org. Checked 19 October 2026.
  7. MAIA - Mines Alumni Intelligence Artificielle | Mines Paris Alumni mines-paris.org. Checked 19 October 2026.