PeopleofSuperintelligence

Researcher

Rashika Jakhmola

Rashika Jakhmola is a machine learning researcher who builds biologically informed neural networks for multi-omics data in cancer research. A mathematics graduate of the University of Delhi, she completed an MSc in Mathematics in Data Science at the Technical University of Munich in 2025, with a thesis in the Theis Lab on a single-cell cancer atlas. At the German Research Center for Artificial Intelligence (DFKI) she worked as a research assistant, and in 2025 she was the second author of a systematic review of 86 papers on visible neural networks. In 2026 she published MiracleNet, a model that predicts disease-free survival in resected non-small-cell lung cancer from circulating miRNA. She is a doctoral researcher in the MLO Lab, with affiliations at Goethe University Frankfurt, the German Cancer Consortium and the German Cancer Research Center.

Timeline

  1. 2017–2020

    Mathematics at the University of Delhi

    She studies for a BSc (Honours) in Mathematics at the University of Delhi from 2017 and completes it in 2020.

  2. 2021–2025

    Data science master's in Munich

    She studies for an MSc in Mathematics in Data Science at the Technical University of Munich, starting in 2021 and completing it in 2025. Her master thesis, written in the Theis Lab at Helmholtz Zentrum Munich, builds a benchmarking pipeline for a continual learning–based autoencoder framework that constructs a comparative single-cell cancer atlas.

  3. By 2025

    Research assistant at DFKI

    By 2025, she is a research assistant in the Data Science and its Applications group at the German Research Center for Artificial Intelligence (DFKI), working on multi-omics based biologically informed deep learning models for cancer. The role lasts one and a half years, and she also gains single-cell data analysis experience at the University Medical Center Mainz.

  4. July 2025

    A review of visible neural networks

    In July 2025, Frontiers in Artificial Intelligence publishes a systematic review of 86 papers on biologically informed, or visible, neural networks, whose connections are constrained by gene ontologies and pathway databases. She is the second author, one of three credited with equal contribution alongside David Antony Selby and Maximilian Sprang.

  5. July 2026

    MiracleNet for lung cancer

    In July 2026, she publishes MiracleNet in the Computational and Structural Biotechnology Journal as first author, with David A. Selby and Maximilian Sprang among the coauthors. The visible neural network follows the miRNA → target gene → pathway hierarchy to predict disease-free survival from circulating miRNA in non-small-cell lung cancer, and reaches a maximum concordance index of 0.76.

  6. As of October 2026

    Doctoral researcher in the MLO Lab

    She is a doctoral researcher in the MLO Lab, with affiliations at Goethe University Frankfurt, the German Cancer Consortium (DKTK) and the German Cancer Research Center (DKFZ). Her research interests span probabilistic machine learning for multi-omic data integration and foundation-model approaches for single-cell representation learning.

Career

  1. NowMLO LabDoctoral Researcher
  2. NowGoethe University FrankfurtDoctoral Researcher
  3. NowGerman Cancer ConsortiumDoctoral Researcher
  4. NowHelmholtz Association of German Research CentresDoctoral Researcher
  5. DFKIResearch Assistant

Education

  1. 2025Technical University of MunichMSc
  2. 2020University of DelhiBSc (Honours)

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Helmholtz Association of German Research Centres. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. 5 of the 25 people with the most-cited recent papers also lead on career citationsIncludes Helmholtz Association of German Research Centres. We compare citations to 2023–2025 papers with career citations and h-index, after correcting reviewed publication-date errors: who leads, when they started and where they work.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Goethe University Frankfurt. 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.
  4. Google has the highest combined h-index among AI organizationsIncludes Helmholtz Association of German Research Centres. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.

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. Rashika Jakhmola — Data Science and its Applications dsa.dfki.de. Checked 22 October 2026.
  2. Rashika Jakhmola | MLO Lab mlo-lab.github.io. Checked 22 October 2026.
  3. Frontiers | Visible neural networks for multi-omics integration: a critical review frontiersin.org. Checked 22 October 2026.
  4. Rashika Jakhmola - Google Scholar scholar.google.com. Checked 22 October 2026.
  5. MiracleNet: A Biologically Interpretable Machine Learning Model for Resected Non-small-cell Lung Cancer - PMC pmc.ncbi.nlm.nih.gov. Checked 22 October 2026.