PeopleofSuperintelligence

Researcher

Rahul Sharma

Rahul Sharma is a researcher at the German Research Centre for Artificial Intelligence (DFKI) in Kaiserslautern, working on explainable and automated machine learning. After a B.Tech in computer science and engineering at the University of Petroleum and Energy Studies, he spent 3.5 years in India as a software engineer and then a data scientist. In January 2024, he posted the preprint X Hacking: The Threat of Misguided AutoML as first author, showing how an automated machine learning pipeline can be adapted to search for a desired explanation. The paper was accepted to ICML 2025, and in July 2025 DFKI announced that its Data Science and its Applications team, with Sharma among the authors, was presenting the analysis there. He is a researcher in that department and leads its Education team, with interests in speech processing and edge machine learning.

Now at
DFKI

Timeline

  1. By 2024

    Engineer and data scientist in India

    He holds a B.Tech in computer science and engineering from the University of Petroleum and Energy Studies and spends 3.5 years in India, first as a software engineer and then as a data scientist. By January 2024, he works in the Data Science and its Applications department of the German Research Centre for Artificial Intelligence in Kaiserslautern.

  2. January 2024

    The X Hacking preprint

    In January 2024, he posts the preprint X Hacking: The Threat of Misguided AutoML as first author, with Sergey Redyuk, Sumantrak Mukherjee, Andrea Šipka, Eyke Hüllermeier, Sebastian Vollmer and David Selby. The paper introduces X-hacking, a form of p-hacking applied to explainability metrics such as SHAP values, and shows how an automated machine learning pipeline can be adapted to search for a desired explanation.

  3. 2025

    A paper at ICML 2025

    The paper is accepted to ICML 2025 and appears in volume 267 of the Proceedings of Machine Learning Research, the proceedings of the 42nd International Conference on Machine Learning. Its experiments on real-world datasets show that Bayesian optimisation accelerates X-hacking 3-fold for features susceptible to it, versus random sampling, and that the vulnerability of a dataset to X-hacking can be determined by information redundancy among features.

  4. July 2025

    Guidance for transparent AutoML

    In July 2025, DFKI announces that its Data Science and its Applications team, led by Sebastian Vollmer and with Sharma among the authors, is presenting a systematic analysis of X-hacking at ICML 2025, where the paper is a poster. The team recommends explanation histograms, full documentation of the modelling pipeline and interdisciplinary training for fields that use AutoML.

  5. As of October 2026

    Researcher leading the Education team

    He is a researcher in the Data Science and its Applications department at DFKI and leads its Education team. His interests include speech and natural language processing and edge machine learning, and his education includes an MSc in computer science at Technische Universität Kaiserslautern.

Career

  1. NowDFKIResearcher; Education Team Lead

Education

  1. University of Petroleum and Energy StudiesB.Tech.
  2. University of KaiserslauternMSc

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.

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. Rahul Sharma — Data Science and its Applications (DFKI) dsa.dfki.de. Checked 22 October 2026.
  2. X Hacking: The Threat of Misguided AutoML (arXiv HTML, v1) arxiv.org. Checked 22 October 2026.
  3. [2401.08513] X Hacking: The Threat of Misguided AutoML arxiv.org. Checked 22 October 2026.
  4. X-Hacking: The Threat of Misguided AutoML (PMLR 267) proceedings.mlr.press. Checked 22 October 2026.
  5. ICML 2025: DFKI research warns of deceptive explainability in AI systems nachrichten.idw-online.de. Checked 22 October 2026.
  6. ICML Poster X-Hacking: The Threat of Misguided AutoML icml.cc. Checked 22 October 2026.
  7. Team — Data Science and its Applications (DFKI) dsa.dfki.de. Checked 22 October 2026.