World's Best Scientists 2026 revealed!
Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
35
Citations
15822
World Ranking
818
National Ranking
7

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

David Lopez-Paz is a researcher affiliated with Facebook AI Research (FAIR) in Paris, France. Their work primarily spans the field of Computer Science, with a particular focus on Artificial Intelligence.

The scientist's recent publications cover a range of topics and have appeared in several notable venues. Key recent papers include:

  • Mixup: Beyond empirical risk minimization, 2024, arXiv (Cornell University)
  • Predicting cellular responses to complex perturbations in high-throughput screens, 2023, Molecular Systems Biology
  • Using Hindsight to Anchor Past Knowledge in Continual Learning, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • In Search of Lost Domain Generalization, 2020, arXiv (Cornell University)
  • Poincaré maps for analyzing complex hierarchies in single-cell data, 2020, Nature Communications

Lopez-Paz frequently collaborates with several co-authors, including Léon Bottou, Badr Youbi Idrissi, Mohammad Pezeshki, Kartik Ahuja, and Martín Arjovsky.

Their frequent publication venues reflect a mixture of preprints and peer-reviewed journals, with 21 publications in arXiv (Cornell University), 2 in bioRxiv (Cold Spring Harbor Laboratory), and others in Molecular Systems Biology, Proceedings of the AAAI Conference on Artificial Intelligence, and Nature Communications.

The scientist's research spans multiple subfields within Computer Science, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Cognitive Neuroscience
  • Biophysics

Main topics covered in their work include:

  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Anomaly Detection Techniques and Applications
  • Adversarial Robustness in Machine Learning
  • Single-cell and spatial transcriptomics
  • Cell Image Analysis Techniques

Best Publications

  • mixup: Beyond Empirical Risk Minimization

    Hongyi Zhang;Moustapha Cisse;Yann N. Dauphin;David Lopez-Paz

  • Gradient Episodic Memory for Continual Learning

    David Lopez-Paz;Marc'Aurelio Ranzato

  • Invariant Risk Minimization

    Martin Arjovsky;Léon Bottou;Ishaan Gulrajani;David Lopez-Paz

  • Interpolation consistency training for semi-supervised learning.

    Vikas Verma;Kenji Kawaguchi;Alex Lamb;Juho Kannala

  • Interpolation Consistency Training for Semi-supervised Learning.

    Vikas Verma;Alex Lamb;Juho Kannala;Yoshua Bengio

  • Manifold Mixup: Better Representations by Interpolating Hidden States

    Vikas Verma;Alex Lamb;Christopher Beckham;Amir Najafi

  • Optimizing the Latent Space of Generative Networks

    Piotr Bojanowski;Armand Joulin;David Lopez-Paz;Arthur Szlam

  • Unifying distillation and privileged information

    David Lopez-Paz;Léon Bottou;Bernhard Schölkopf;Vladimir Vapnik;Vladimir Vapnik

  • Revisiting Classifier Two-Sample Tests

    David Lopez-Paz;Maxime Oquab

  • Interpolation Consistency Training for Semi-Supervised Learning

    Vikas Verma;Kenji Kawaguchi;Alex Lamb;Juho Kannala

  • The Randomized Dependence Coefficient

    David Lopez-Paz;Philipp Hennig;Bernhard Schölkopf

  • Discovering Causal Signals in Images

    David Lopez-Paz;Robert Nishihara;Soumith Chintala;Bernhard Scholkopf

  • Randomized Nonlinear Component Analysis

    David Lopez-Paz;Suvrit Sra;Alex Smola;Zoubin Ghahramani

  • Randomized Nonlinear Component Analysis

    David Lopez-Paz;Suvrit Sra;Alex Smola;Zoubin Ghahramani

  • Single-Model Uncertainties for Deep Learning

    Natasa Tagasovska;David Lopez-Paz

  • Towards a Learning Theory of Cause-Effect Inference

    David Lopez-Paz;David Lopez-Paz;Krikamol Muandet;Bernhard Sch lkopf;Iliya Tolstikhin

  • Using Hindsight to Anchor Past Knowledge in Continual Learning

    Arslan Chaudhry;Albert Gordo;Puneet K. Dokania;Philip H. S. Torr

  • In Search of Lost Domain Generalization

    Ishaan Gulrajani;David Lopez-Paz

  • Manifold Mixup: Better Representations by Interpolating Hidden States.

    Vikas Verma;Alex Lamb;Christopher Beckham;Amir Najafi

  • Towards a Learning Theory of Cause-Effect Inference

    David Lopez-Paz;Krikamol Muandet;Bernhard Schölkopf;Ilya Tolstikhin

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Léon Bottou
Léon Bottou Facebook (United States)
Yoshua Bengio
Yoshua Bengio University of Montreal
José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
Michèle Sebag
Michèle Sebag University of Paris-Saclay
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Vladimir Vapnik
Vladimir Vapnik Princeton University
Juho Kannala
Juho Kannala Aalto University
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Jean-Rémi King
Jean-Rémi King École Normale Supérieure

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education has become increasingly popular for students seeking flexibility and affordability. In particular, an online bachelor degree in Computer Science can open doors to many technology-driven roles while fitting easily around your schedule and location.

Beyond core computer science, students may consider specialized fields like engineering. Accredited engineering online programs offer pathways into areas such as software, robotics, and systems engineering—highly relevant for tech-oriented careers.

For those aiming for leadership or business roles in the tech industry, earning an executive mba programs online can help professionals combine computing skills with advanced management expertise, making them valuable in upper-level positions.

Alternatively, tech-savvy students interested in information management may pursue an library sciences degree. This field covers digital cataloging, information retrieval, and systems design—skills that align well with computer science foundations.

Best Scientists Citing David Lopez-Paz

Trending Scientists