Huyen Vo (Emily)

Hi, I’m Huyen. You can also call me Emily. 🍀

I’m a PhD student at Saarland University (UdS) and Max Planck Institute for Software Systems (MPI-SWS), part of the CS@Max Planck PhD program, where I’m fortunate to be advised by Prof. Isabel Valera in the Probabilistic Machine Learning group.

👩‍💻 My research focuses on multimodal generative models, with a particular interest in the optimization and inference challenges that arise when learning joint representations across multiple data modalities. Previously, I worked on implicit models such as neural ODEs. Across my work, I aim to leverage probabilistic and optimization perspectives to develop models that are both effective and efficient.

Before my PhD, I was a Research Resident at FPT Software AI Center, working with Prof. Tan Minh Nguyen and Dr. Thieu N. Vo. In 2023, I graduated from the Honors Program at Hanoi University of Science and Technology (HUST) with a Computer Science Bachelor’s degree. During my time there, I was a research student at the Data Science Laboratory, advised by Dr. Linh Van Ngo.

I’m always happy to chat about new and interesting research ideas — feel free to reach out! 🤗

Besides research, I have an infinite love for mountains and hiking — the Alps have my whole heart. 🏔️

news

Jan 22, 2026	My first PhD paper got a Spotlight (2.5%) at AISTATS 2026! 🎉
Oct 01, 2024	I started my PhD at the CS@Max Planck PhD program. Excited for the journey ahead! 👩‍💻
Sep 15, 2022	Our paper got accepted at NeurIPS 2022! 🎉

selected publications

AISTATS
Spotlight

Hellinger Multimodal Variational Autoencoders

Huyen Vo and Isabel Valera

In The 29th International Conference on Artificial Intelligence and Statistics, 2026

Spotlight Presentation [2.5%]

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Spotlight

Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference distributions using either a product of experts (PoE), a mixture of experts (MoE), or their combinations to approximate the joint posterior. In this work, we revisit multimodal inference through the lens of probabilistic opinion pooling, an optimization-based approach. We start from Hölder pooling with α=0.5, which corresponds to the unique symmetric member of the α-divergence family, and derive a moment-matching approximation, termed Hellinger. We then leverage such an approximation to propose HELVAE, a multimodal VAE that avoids sub-sampling, yielding an efficient yet effective model that: (i) learns more expressive latent representations as additional modalities are observed; and (ii) empirically achieves better trade-offs between generative coherence and quality, outperforming state-of-the-art multimodal VAE models.
NeurIPS

Improving Neural Ordinary Differential Equations with Nesterov’s Accelerated Gradient Method

Nghia Nguyen, Tan Minh Nguyen, Huyen Vo, and 2 more authors

In Advances in Neural Information Processing Systems, 2022

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We propose the Nesterov neural ordinary differential equations (NesterovNODEs), whose layers solve the second-order ordinary differential equations (ODEs) limit of Nesterov’s accelerated gradient (NAG) method, and a generalization called GNesterovNODEs. Taking the advantage of the convergence rate O(1/k^2) of the NAG scheme, GNesterovNODEs speed up training and inference by reducing the number of function evaluations (NFEs) needed to solve the ODEs. We also prove that the adjoint state of a GNesterovNODEs also satisfies a GNesterovNODEs, thus accelerating both forward and backward ODE solvers and allowing the model to be scaled up for large-scale tasks. We empirically corroborate the advantage of GNesterovNODEs on a wide range of practical applications, including point cloud separation, image classification, and sequence modeling. Compared to NODEs, GNesterovNODEs require a significantly smaller number of NFEs while achieving better accuracy across our experiments.