Duy H. M. Nguyen
Nguyen Ho Minh Duy
Universitätsstraße 32
70569 Stuttgart, Germany
Room: 2.321
I am currently a Ph.D. Candidate under the supervision of Prof. Mathias Niepert at Max Planck Research School for Intelligent Systems (IMPRS-IS) and University of Stuttgart. I have also been a Researcher at the German Research Center for Artificial Intelligence (DFKI) since 2021.
My topics of interest are Hybrid Discrete-Continuous Learning (differentiable relaxations for discrete intermediate representations), Scalable Algorithms for Multi-modal Learning with applications for Healthcare, Simulation Science, and Efficient Deep Learning (model compression, accelerated training/inference, etc.)
Please visit my Google Scholar for a full list of publications and GitHub for source codes.
news
| May 01, 2025 | 🎉 Our first (i) preliminary version, MGPath has been accepted to the Workshop on Foundation Models in the Wild, ICLR 2025 and (ii) another one about LLaMA-Adapter’s prompt learning is accepted at ICML 2025 (Code is coming soon!). |
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| Apr 20, 2025 | 🎉 Our work in building a new Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data has been accepted at Scientific Report, Nature Portfolio. |
| Feb 20, 2025 |  Excited to share our latest work! 🎉: (i) On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation – We introduce a Mixture of Experts (MoE) perspective to explain the mechanism behind LLaMA-Adapter’s prompt learning. (ii) MGPath – A novel multi-granular prompt learning method for few-shot WSI pathology prediction, leveraging the power of foundation vision-language models. |
| Oct 08, 2024 | 🇨🇭 Start my visiting research at ETH AI Center, ETH Zurich. The topics are about Multi-Modal LLMs for Healthcare empowered by Retrieval-Augmented Generation. |
| Oct 07, 2024 | Excited to introduce our latest work on medical multi-modal LLMs: LoGra-Med, a novel pre-training algorithm that incorporates multi-graph alignment to effectively address the data-hungry nature of autoregressive learning. |
| Oct 06, 2024 |  The paper PiToMe has been accepted at NeurIPS 2024. Our code will be available soon! |
| Jun 10, 2024 | Our new preprint PiToMe is online. We propose a new method to do token merging in the Transformer with spectrum-preserving. |
| May 01, 2024 | A paper submitted to ICML 2024 on the molecular conformer aggregation network topic is accepted. |
| Jan 15, 2024 | A paper submitted to ICLR 2024 on the topic of accelerating transformers is accepted as an oral talk. |
| Sep 22, 2023 | A paper submitted to NeurIPS 2023 on a large-scale medical image pre-trained models using second-order graph matching is accepted. |
preprints
- Under Review
From Fragments to Geometry: A Unified Graph Transformer for Molecular Representation from Conformer Ensembles2025
selected publications
- ICLR (Oral)
Energy minimizing-based token merging for accelerating Transformers5th Workshop on practical ML for limited/low resource settings, International Conference on Learning Representations (ICLR), 2024 - CVPR
LMGP: Lifted multicut meets geometry projections for multi-camera multi-object trackingProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 - MedIA
TATL: Task agnostic transfer learning for skin attributes detectionMedical Image Analysis, 2022
