PeopleofSuperintelligence

Researcher

Hakan Inan

Hakan Inan is an AI researcher whose work connects language modeling, computational neuroscience and large-language-model safety. He earned a B.S. in electrical engineering at Middle East Technical University and pursued graduate study at Stanford, where he worked with the Schnitzer Group on neural data. In 2016 and 2017, he coauthored work on word-vector models and calcium-imaging signals; in 2023, he coauthored the Llama 2 report and Llama Guard, a safeguard for prompts and responses. As of October 2026, he is a research scientist at Meta, a role he has held since 2019.

Timeline

  1. 2008–2015

    Electrical engineering study

    From 2008 to 2013, he studies electrical engineering at Middle East Technical University and earns a B.S. He begins a PhD in Stanford's Electrical Engineering department in 2013 and earns an M.S. there in 2015.

  2. 2014–2017

    Research at the Schnitzer Group

    In 2014, he works as a research assistant in Stanford's Electrical Engineering department. By 2017, he is part of the Schnitzer Group, building computational tools to analyze in-vivo calcium-imaging data.

  3. 2016

    Tying word vectors

    In 2016, he posts Tying Word Vectors with Khashayar Khosravi and Richard Socher. Their framework ties input embeddings to output projections and reduces trainable parameters.

  4. 2017

    Robust neural-signal estimation

    In 2017, he and Murat Erdogdu and Mark Schnitzer publish Robust Estimation of Neural Signals in Calcium Imaging. Their robust-loss method uses matrix factorization to extract neurons and their activity from calcium-imaging data.

  5. 2023

    Llama 2 models

    In 2023, he coauthors the Llama 2 report, which describes pretrained and fine-tuned models from 7 billion to 70 billion parameters. Llama 2-Chat is optimized for dialogue use cases.

  6. December 2023

    Llama Guard safety model

    In December 2023, he coauthors Llama Guard, an LLM-based input-output safeguard for human-AI conversations. The model classifies prompts and responses with a safety-risk taxonomy.

  7. Since 2019

    Research scientist at Meta

    He is a research scientist at Meta, a role he has held since 2019.

  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. Hakan Inan web.stanford.edu. Checked 19 October 2026.
  2. Hakan Inan web.stanford.edu. Checked 19 October 2026.
  3. Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling arxiv.org. Checked 19 October 2026.
  4. Robust Estimation of Neural Signals in Calcium Imaging papers.nips.cc. Checked 19 October 2026.
  5. Llama 2: Open Foundation and Fine-Tuned Chat Models arxiv.org. Checked 19 October 2026.
  6. Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations arxiv.org. Checked 19 October 2026.
  7. Hakan Inan — Lacuna lacuna.tiptreesystems.com. Checked 19 October 2026.