PeopleofSuperintelligence

Researcher

Mouad Elhamdi

Mouad Elhamdi is a machine learning researcher in Morocco who works on dimensionality reduction, the techniques that turn high-dimensional data into pictures people can read. He studied applied mathematics at Cadi Ayyad University (a BSc in 2015–2018 and an MSc in 2019–2021) before starting a PhD in computer science at Mohammed VI Polytechnic University in 2022. Papers in 2022 and 2023 covered crop classification, kernel functions for data representation and normalized cut clustering, and in 2025 he coauthored a review of neural spatiotemporal point processes that TMLR published. He has been a visiting PhD student in the Data Science Applications group at DFKI, the German Research Center for Artificial Intelligence, and in 2026 he published the IEEE Access paper "Heavy-Tailed Neighbor Retrieval Visualizer". He is a PhD student at Mohammed VI Polytechnic University.

Timeline

  1. 2015–2018

    A BSc at Cadi Ayyad

    He studies for a BSc in Applied Mathematics at Cadi Ayyad University from 2015 to 2018.

  2. 2019–2021

    A master's at Cadi Ayyad

    From 2019 to 2021, he completes an MSc in Applied Mathematics at Cadi Ayyad University.

  3. 2022

    A PhD at Mohammed VI Polytechnic

    In 2022, he starts a PhD in computer science at Mohammed VI Polytechnic University in Morocco. He is also a coauthor of "Crop classification using deep learning: a quick comparative study of modern approaches", presented at the International Conference on Applied Informatics in 2022.

  4. 2023

    Kernels, clustering and emissions papers

    In 2023, he coauthors "Joint exploration of kernel functions potential for data representation and classification" in SN Computer Science, a first step toward interactive, interpretable dimensionality reduction. His other 2023 papers include a study of greenhouse-gas emission prediction for synthetic nitrogen fertilizers in Atmosphere and a TechRxiv preprint on normalized cut clustering with prior knowledge.

  5. 2025

    Neural point processes review

    In February 2025, he and six coauthors, including Sebastian Vollmer and Gerrit Grossmann, post the review "Neural Spatiotemporal Point Processes: Trends and Challenges" to arXiv. The review categorizes existing approaches, unifies key design choices and identifies open challenges, and TMLR publishes it in 2025.

  6. 2026

    Neighbor retrieval and AlphaEarth papers

    In 2026, he is first author of "Heavy-Tailed Neighbor Retrieval Visualizer" in IEEE Access, with Yahya Aalaila and two other coauthors. In July 2026, he coauthors an arXiv paper with Aalaila, Großmann, Daniel Jenson, Elizaveta Semenova and Vollmer that adds AlphaEarth embeddings to a log-Gaussian Cox process model for emergency medical services forecasting, and the embeddings improve out-of-region predictions across all history lengths.

  7. As of October 2026

    PhD student at UM6P

    He is a PhD student in computer science at Mohammed VI Polytechnic University in Morocco, where his research develops dimensionality reduction techniques for visualization and knowledge discovery in high-dimensional data. He has been a visiting PhD student in the Data Science Applications group at DFKI, the German Research Center for Artificial Intelligence.

Career

  1. DFKIVisiting PhD Student

Education

  1. From 2022Université Mohammed VI PolytechniquePhD in Computer Science
  2. 2021Cadi Ayyad UniversityMSc in Applied Mathematics
  3. 2018Cadi Ayyad UniversityBSc in Applied Mathematics

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 DFKI. 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.

Sources and credits

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  1. Mouad Elhamdi — Data Science and its Applications dsa.dfki.de. Checked 21 October 2026.
  2. Mouad Elhamdi - Google Scholar scholar.google.com. Checked 21 October 2026.
  3. [2502.09341] Neural Spatiotemporal Point Processes: Trends and Challenges arxiv.org. Checked 21 October 2026.
  4. Elhamdi, Mouad | ML Anthology mlanthology.org. Checked 21 October 2026.
  5. [2607.01082] When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting arxiv.org. Checked 21 October 2026.