Nicolas

Nicolas Huynh

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I am a third-year PhD student in Machine Learning at the University of Cambridge (department of Applied Mathematics and Theoretical Physics), in the van der Schaar lab. Previously, I earned a Diplome d'ingénieur from Ecole Polytechnique (majoring in applied mathematics) and a MSc in Machine Learning from Ecole Normale Supérieure Paris-Saclay.

I am interested in generative AI (e.g. diffusion models and flow matching), and data-centric AI (e.g. data curation, data evaluation, and data augmentation). I have also been interested in scientific discovery with large language models more recently, with applications in biology.

Publications

(* denotes equal contribution)

  • Decision Tree Induction with Dynamic Feature Generation: A Framework for Interpretable DNA Sequence Analysis
    Nicolas Huynh, Krzysztof Kacprzyk, Ryan Sheridan, David Bentley, Mihaela van der Schaar
    ICLR Machine Learning for Genomics Explorations Workshop (Spotlight), 2025
    ICLR AI for Nucleic Acids Workshop, 2025
  • Decision Tree Induction Through LLMs via Semantically-Aware Evolution
    Tennison Liu*, Nicolas Huynh*, Mihaela van der Schaar
    International Conference on Learning Representations (ICLR), 2025
    [Paper]
  • Curated LLM: Synergy of LLMs and Data Curation for Tabular Augmentation in Ultra low-data Regimes
    Nabeel Seedat*, Nicolas Huynh*, Boris van Breugel, Mihaela van der Schaar
    International Conference on Machine Learning (ICML), 2024
    [Paper]
  • Time Series Diffusion in the Frequency Domain
    Jonathan Crabbé*, Nicolas Huynh*, Jan Stanczuk, Mihaela van der Schaar
    International Conference on Machine Learning (ICML), 2024
    [Paper]
  • You Can't Handle the (Dirty) Truth: Data-centric Insights Improve Pseudo-labeling
    Nabeel Seedat*, Nicolas Huynh*, Fergus Imrie, Mihaela van der Schaar
    Journal of Data-centric Machine Learning Research (DMLR)
    [Paper]
  • DAGnosis: Localized Identification of Data Inconsistencies using Structures
    Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
    [Paper]
  • Active Preference-Based Gaussian Process Regression for Reward Learning and Optimization
    Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh
    International Journal of Robotics Research (IJRR)
    [Paper]
  • GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings
    Dan Berrebbi*, Nicolas Huynh*, Oana Balalau
    The Web Conf, 2nd International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, 2022
    [Paper]
  • Active Preference-Based Gaussian Process Regression for Reward Learning
    Erdem Bıyık*, Nicolas Huynh*, Mykel J. Kochenderfer, Dorsa Sadigh
    Robotics: Science and Systems (RSS),  2020
    [Paper]