PeopleofSuperintelligence

Researcher

Faeze Brahman

Faeze Brahman is an AI researcher whose work spans narrative generation, language-model planning, model evaluation, and open language models. She earned degrees in electrical engineering and computer science before completing a computer science PhD at UC Santa Cruz in 2022. Her research includes emotion-aware storytelling, studies of model understanding, and evaluation of contextual noncompliance. She coauthored Olmo 3, Ai2’s fully open 7B and 32B model family, and as of October 2026 she is a senior research scientist on the OLMo Post-training team.

Timeline

  1. 2012–2018

    Engineering and computer science degrees

    She earns a bachelor's degree in electrical engineering in 2012, a master's degree in that field in 2014, and a master's degree in computer science in 2018.

  2. 2020–2021

    Emotion and character narratives

    In 2020, she and Snigdha Chaturvedi introduce Emotion Supervision and Emotion-Reinforced models that guide generated stories along desired protagonist emotion arcs. In 2021, she coauthors a LiSCU paper that introduces character-identification and character-description generation tasks.

  3. 2022–2023

    A PhD and small-model planning

    She completes a computer science PhD at UC Santa Cruz in 2022, working with Snigdha Chaturvedi. Her earlier internships include Microsoft Research on controllable grounded text generation, Ai2 on unsupervised rationale generation for non-monotonic reasoning, and Xerox PARC on an RFP response assistant. In May 2023, she coauthors PlaSma, which equips small language models with procedural knowledge and constrained language-planning capabilities.

  4. 2023–2024

    The generative AI paradox

    In 2023, she coauthors The Generative AI Paradox, a study of generation and understanding in language and image models that is accepted at ICLR 2024. The experiments find that models fall short of human capabilities on measures of understanding.

  5. 2024

    Contextual noncompliance in language models

    In 2024, she coauthors The Art of Saying No, which introduces a taxonomy of contextual noncompliance and a 1,000-prompt evaluation suite. The evaluation finds high compliance rates in some understudied request categories among existing models.

  6. April 2025

    Research scientist at Ai2

    In April 2025, she is a research scientist at Ai2 after postdoctoral research at Ai2 and the University of Washington with Yejin Choi. At SSNLP 2025, she gives a talk titled “Open Language Model Adaptation and Reliable Evaluation.”

  7. December 2025

    Olmo 3 open models

    In December 2025, she coauthors Olmo 3, a fully open family of 7B and 32B models for long-context reasoning, function calling, coding, instruction following, general chat, and knowledge recall.

  8. As of October 2026

    Senior research scientist at Ai2

    As of October 2026, she is a senior research scientist at Ai2 on the OLMo Post-training team. Her research interests include collaborative strategies and human-computer interfaces that combine human and AI abilities.

  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. Faeze Brahman fabrahman.github.io. Checked 19 October 2026.
  2. Modeling Protagonist Emotions for Emotion-Aware Storytelling aclanthology.org. Checked 19 October 2026.
  3. “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding aclanthology.org. Checked 19 October 2026.
  4. SSNLP 2025 and IMDA Technical Sharing Session imda.gov.sg. Checked 19 October 2026.
  5. Modeling Key Narrative Elements for Automatic Story Generation isi.edu. Checked 19 October 2026.
  6. PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning arxiv.org. Checked 19 October 2026.
  7. The Generative AI Paradox: "What It Can Create, It May Not Understand" arxiv.org. Checked 19 October 2026.
  8. The Art of Saying No: Contextual Noncompliance in Language Models arxiv.org. Checked 19 October 2026.
  9. SSNLP 2025: The 2025 Singapore Symposium on Natural Language Processing ssnlp-website.github.io. Checked 19 October 2026.
  10. Olmo 3 arxiv.org. Checked 19 October 2026.