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Nhi Pham I'm a PhD student at the Max Planck Institute for Informatics in Saarbrücken, Germany, where I am advised by Prof. Bernt Schiele and Dr. Jonas Fischer. As part of PhD preparatory phase, I was jointly supervised by Dr. Adam Kortylewski on interpreting image classifiers with 3D neural object volumes. Before this, I received my B.S in Computer Science and Mathematics at New York University, where I worked on computational linguistics with Prof. Adam Meyers, and on operator theory with Prof. Ilya Spitkovsky. I also interned at Goldman Sachs, Amazon AWS AI, and Meta (Facebook). Email / CV / Google Scholar / Twitter / Github |
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News04.2026 Our workshop on World Model benchmarking is accepted at ECCV 2026. See you in Malmö! 04.2026 Accepted at ICVSS 2026. See you in Sicily! 01.2026 Our paper CAVE is accepted at ICLR 2026. Check it out! 04.2025 I presented CAVE at the Generative Intelligence Lab. 03.2025 I’ve finished my PhD preparatory phase in the IMPRS-TRUST program, and officially joined D2 MPII for my PhD. 12.2024 H-POPE is accepted to NeurIPS 2024 - Statistical Foundations of LLMs and Foundation Models Workshop! |
Featured ResearchMy current research interest lies in geometric representation learning, with an emphasis on interpretable and robust vision models grounded in 3D structure. I am also interested in extending these ideas to video diffusion models, where learning holistic, geometry-aware tokens can support structured reconstruction and generation over time. In my previous life, I enjoyed playing around with special matrices and their eigenvalues. |
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Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
(CAVE)
Nhi Pham, Artur Jesslen, Bernt Schiele, Adam Kortylewski*, Jonas Fischer* (*equal senior advisorship) ICLR 2026 paper / arXiv / project page / code Design an inherently-interpretable and robust classifier by extending existing 3D-aware classifiers with concepts extracted from their volumetric representations for classification. We also propose a new metric to measure 3D consistency of concepts. |
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H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in
Large Vision-Language Models
Nhi Pham*, Michael Schott* Statistical Foundations of LLMs and Foundation Models Workshop, NeurIPS 2024 arXiv A coarse-to-fine-grained benchmark that systematically assesses hallucination in object existence and attributes. |
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On 3-by-3 Row Stochastic Matrices
Nhi Pham, Ilya Spitkovsky Special Matrices 2023 paper The known constructive tests for the shapes of the numerical ranges in the 3-by-3 case are further specified when the matrices in question are row stochastic. Auxiliary results on the unitary (ir)reducibility of such matrices are also obtained. |
Others |
Academic Service |
Reviewer: T-PAMI 2026, XAI4CV 2025, XAI4CV 2024 |
Graduate Teaching |
Teaching Assistant, Explainable
Machine Learning (ExML) Seminar, Winter 2025/26
Teaching Assistant, High-level Computer Vision, Summer 2025 Teaching Assistant, Neural Networks: Theory and Implementation, Winter 2024/25 |
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Source code and design are borrowed from Jon Barron's website. |