PeopleofSuperintelligence

Researcher

Chaitanya Jobanputra

Chaitanya Jobanputra is a Research Assistant at DFKI and a Data Science and AI student at Saarland University, focused on spatio-temporal data analysis and causal modeling. In Winter 2024/25, he publishes coding solutions for Saarland University's Neural Networks: Theory and Implementation course; in 2025, he explores MoLFormer for molecular-property prediction, analyzes NetMob23 mobile traffic across French cities, and studies spatial Gaussian processes. In 2026, he implements prototypical networks for molecular-property regression and coauthors Puffin, which uses satellite embeddings to turn coarse regional totals into fine-scale estimates with calibrated uncertainty.

Now at
DFKI

Timeline

  1. 2024–2025

    Neural network coursework

    In Winter 2024/25, he publishes coding solutions for Saarland University's Neural Networks: Theory and Implementation course.

  2. May 2025

    Transfer learning for molecules

    In May 2025, he explores transfer-learning and data-selection strategies with MoLFormer for molecular-property prediction. His LipophilicityPrediction project predicts lipophilicity (logD) from SMILES representations.

  3. May 2025

    NetMob23 mobility analysis

    In May 2025, he analyzes NetMob23 mobile traffic across 20 French cities to study adult web-consumption patterns. The project integrates demographic, temperature, and point-of-interest data.

  4. 2025

    Spatial Gaussian processes

    In 2025, he analyzes spatially correlated data with spatial Gaussian processes for Saarland University's Spatiotemporal Models and Inference seminar (SS25).

  5. August 2026

    Prototypical networks for molecules

    In August 2026, he implements prototypical networks for regression on molecular properties and evaluates out-of-distribution generalization.

  6. October 2026

    Puffin for fine-scale estimates

    In October 2026, he coauthors Puffin with Sebastian Vollmer and Gerrit Großmann. The method uses high-resolution satellite embeddings to disaggregate coarse totals into fine-scale estimates with calibrated uncertainty.

  7. As of October 2026

    Research Assistant at DFKI

    As of October 2026, he is a Research Assistant in DFKI's Data Science and its Applications group and a Data Science and AI student at Saarland University. His focus includes spatio-temporal data analysis and causal modeling; recent projects include large-scale mobility data and machine-learning models for molecular data.

Career

  1. NowDFKIResearch Assistant

Education

  1. Saarland UniversityData Science and AI student

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

Photo: We have no published photo of Chaitanya Jobanputra yet. Send us a free-licensed photo.

  1. Jobanputra02 (Chaitanya Jobanputra) / Repositories · GitHub github.com. Checked 22 October 2026.
  2. GitHub - Jobanputra02/LipophilicityPrediction: Transfer Learning for Molecular Property Prediction — Exploring fine-tuning strategies and data selection methods using MoLFormer for lipophilicity (logD) prediction from SMILES representations. · GitHub github.com. Checked 22 October 2026.
  3. Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations | alphaXiv alphaxiv.org. Checked 22 October 2026.
  4. [2610.11914] Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations arxiv.org. Checked 22 October 2026.
  5. Chaitanya Jobanputra — Data Science and its Applications dsa.dfki.de. Checked 22 October 2026.