PeopleofSuperintelligence

Researcher

Hind Raki

Hind Raki is a machine-learning researcher who studies interpretable models that make AI more trustworthy in health, environment and biotechnology. After a BSc in life sciences at Cadi Ayyad University in 2018 and an MSc in biotechnology at University Mohammed VI Polytechnic (UM6P) in 2021, she began a PhD in machine learning and computer science at UM6P in 2022. She was first author of a 2022 comparative study of crop classification with deep learning and a 2023 review in Foods on AI for crop-based food safety, and she coauthored 2023 papers on tomato leaf diseases and fertilizer emissions. In July 2025, she coauthored a Frontiers in Artificial Intelligence review of visible neural networks for multi-omics integration with colleagues at DFKI. She is a PhD student and researcher at UM6P and a visiting PhD researcher in the Data Science and its Applications group at DFKI.

Timeline

  1. 2014–2018

    Life sciences at Cadi Ayyad

    After preparatory classes in mathematics, physics and engineering sciences at CPGE Lycée Al Khansaa in 2014–2015, she studies life sciences and biology at Cadi Ayyad University. She completes her BSc in 2018.

  2. 2019–2021

    A biotechnology MSc at UM6P

    She studies for an MSc in Biotechnology at University Mohammed VI Polytechnic (UM6P) from 2019 to 2021.

  3. 2022–2025

    A PhD in machine learning

    She enters the PhD program in Machine Learning and Computer Science at UM6P in 2022. Her research centers on interpretable machine learning for AI trustworthiness, with applications in health, environment and biotechnology.

  4. 2022

    Crop classification with deep learning

    She is first author of "Crop classification using deep learning: a quick comparative study of modern approaches", which appears at the International Conference on Applied Informatics in 2022.

  5. 2023

    Tomato diseases and fertilizer emissions

    In October 2023, she coauthors a Heliyon study that classifies four tomato fungal leaf diseases from Gray Level Co-occurrence Matrix texture features, where an Artificial Neural Network reaches 94% overall accuracy. She is also second author of a 2023 study that applies expectile regression to predict nitrous oxide emissions from synthetic nitrogen fertilizer use.

  6. 2023

    AI and crop-based food safety

    She is first author of "Combining AI tools with non-destructive technologies for crop-based food safety: a comprehensive review", published in Foods in 2023 with coauthors including Diego H. Peluffo-Ordóñez, head of the SDAS Research Group.

  7. July 2025

    A review of visible neural networks

    In July 2025, she coauthors "Visible neural networks for multi-omics integration: a critical review" in Frontiers in Artificial Intelligence with David Antony Selby and Sebastian Vollmer, among others, from the Data Science and its Applications group at DFKI in Kaiserslautern. The systematic review identifies 86 recent papers on neural networks whose connections follow prior knowledge from gene ontologies and pathway databases.

  8. As of October 2026

    PhD research at UM6P and DFKI

    She is a PhD student and researcher at UM6P and a visiting PhD researcher in the Data Science and its Applications (DSA) group at the German Research Center for Artificial Intelligence (DFKI).

Career

  1. NowUniversité Mohammed VI PolytechniqueResearcher
  2. NowDFKIVisiting PhD researcher

Education

  1. From 2022Université Mohammed VI PolytechniquePhD
  2. 2021Université Mohammed VI PolytechniqueMSc
  3. 2018Cadi Ayyad UniversityBSc

From the biography.

  1. Nonprofit roles are 2.3 times as common in AI safetyIncludes DFKI. We estimate where people focused on safety, alignment and evaluation work. Nonprofit roles appear for 25.1% of safety people with known affiliations, compared with 11.0% in other areas.
  2. The top 10 organizations employed 22% of people shaping AI in 2025: 51% of company researchers and 21% of university researchersIncludes DFKI. We estimate the share of people shaping AI who worked at the top 10 organizations each year from 1955 to 2025, comparing concentration across companies, universities, and institutes and labs.
  3. The United States employs an estimated 48% of people shaping AIIncludes Université Mohammed VI Polytechnique. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes DFKI. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Hind Raki — Data Science and its Applications dsa.dfki.de. Checked 21 October 2026.
  2. Hind Raki - Google Scholar scholar.google.com. Checked 21 October 2026.
  3. A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features - PMC pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  4. GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques agris.fao.org. Checked 21 October 2026.
  5. Frontiers | Visible neural networks for multi-omics integration: a critical review frontiersin.org. Checked 21 October 2026.