PeopleofSuperintelligence

Researcher

Bart van Merriënboer

Bart van Merriënboer is an AI researcher whose work spans neural machine translation, machine-learning tools and bioacoustics. He coauthored studies of translation and deep-learning software, contributed to Tangent and multiscale sequence modeling, and completed a PhD at Mila under Yoshua Bengio. He later coauthored a review of bioacoustic-model generalization and research on agile modeling and Perch 2.0. As of October 2026, he is a Google DeepMind researcher coauthoring research on geographic calibration for Perch and BirdNET.

Timeline

  1. 2014

    RNN encoder-decoder translation

    In 2014, he coauthors a neural machine-translation study comparing an RNN Encoder–Decoder with a gated recursive convolutional network. The translation performs relatively well on short sentences without unknown words.

  2. 2015

    Blocks and Fuel

    In 2015, he coauthors Blocks and Fuel, two Python frameworks for training neural networks on large datasets.

  3. 2017

    Tangent and sequence modeling

    In 2017, he contributes to Tangent, a Python library for automatic differentiation, during an internship. He also coauthors a multiscale sequence model that performs better than a regular LSTM on language-modeling tasks, especially for smaller models.

  4. By 2019

    A Mila PhD and Google Brain

    By 2019, he is a research scientist at Google Brain and completes a PhD at Mila under Yoshua Bengio. His thesis examines sequence-to-sequence learning for machine translation and automatic differentiation for machine-learning software tools.

  5. 2024

    Bioacoustic model generalization

    In 2024, he coauthors a review on generalization in bioacoustic models, covering sound-event detection, machine-learning metrics, transfer learning, few-shot learning and domain generalization.

  6. May 2025

    Agile modeling for bioacoustics

    In May 2025, he coauthors The Search for Squawk, which introduces a data-efficient system for developing recognizers for novel bioacoustic problems in under an hour.

  7. August 2025–February 2026

    Perch 2.0 for bioacoustics

    In August 2025, Google DeepMind releases an update to Perch, a model that helps conservationists analyze bioacoustic data. By February 2026, he is part of the Google DeepMind Perch Team behind Perch 2.0.

  8. As of October 2026

    Google DeepMind researcher

    In September 2026, he coauthors a study on calibrating Perch and BirdNET with geographic priors. As of October 2026, he is a researcher at Google DeepMind.

Career

  1. NowGoogle DeepMindResearcher
  2. From 2019Google BrainIntern

Education

  1. 2019MilaPhD

From the biography.

  1. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. Google has the highest combined h-index among AI organizationsIncludes Google. 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.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Google. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Bart van Merriënboer yet. Send us a free-licensed photo.

  1. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches arxiv.org. Checked 21 October 2026.
  2. Blocks and Fuel: Frameworks for deep learning arxiv.org. Checked 21 October 2026.
  3. Tangent: Source-to-Source Debuggable Derivatives opensource.googleblog.com. Checked 21 October 2026.
  4. Multiscale sequence modeling with a learned dictionary arxiv.org. Checked 21 October 2026.
  5. News | Program Transformations for Machine Learning program-transformations.github.io. Checked 21 October 2026.
  6. Yoshua BENGIO - La recherche - Université de Montréal recherche.umontreal.ca. Checked 21 October 2026.
  7. Birds, bats and beyond: evaluating generalization in bioacoustics models frontiersin.org. Checked 21 October 2026.
  8. The Search for Squawk: Agile Modeling in Bioacoustics arxiv.org. Checked 21 October 2026.
  9. How AI is helping advance the science of bioacoustics to save endangered species — Google DeepMind deepmind.google. Checked 21 October 2026.
  10. How AI trained on birds is surfacing underwater mysteries research.google. Checked 21 October 2026.
  11. Beyond Discrimination: Calibrated Geoprior Fusion for Bioacoustic Monitoring arxiv.org. Checked 21 October 2026.