I am a PhD student in 3D vision at the Computer Vision Lab (TU Wien), advised by Pedro Hermosilla. My research focuses on geometry-aware deep learning for 3D scene understanding and generation: hyperbolic representations, equivariant networks for point clouds, and generative modeling on Riemannian manifolds. Previously, I applied deep learning to leukemia cell detection in flow cytometry data, in collaboration with St. Anna CCRI.
More information, including project pages and code, is on my website: lisaweijler.github.io
Student projects
If you are interested in any of the following directions, feel free to reach out. I’m happy to talk about possible Bachelor’s thesis or project ideas.
- Hyperbolic representations for hierarchical visual understanding
- Feed-forward 3D reconstruction and novel view synthesis
- Generative models on non-Euclidean spaces, e.g. flow matching on manifolds
For thesis supervision, please also contact Pedro Hermosilla.
Publications
2026
- L. Weijler, I. Ballester, G. Mei, T. Birdal, P. Hermosilla. Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models. NeurIPS 2026.
2025
- L. Weijler, S. Koch, F. Poiesi, T. Ropinski, P. Hermosilla. OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields. NeurIPS 2025.
- L. Weijler, M. J. Mirza, L. Sick, C. Ekkazan, P. Hermosilla. TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models. 3DV 2025.
- L. Weijler, P. Hermosilla. Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds. 3DV 2025.
2024
- L. Weijler, F. Kowarsch, M. Reiter, P. Hermosilla, M. Maurer-Granofszky, M. Dworzak. FATE: Feature-Agnostic Transformer-Based Encoder for Learning Generalized Embedding Spaces in Flow Cytometry Data. WACV 2024.
- L. Weijler, M. Reiter, P. Hermosilla, M. Maurer-Granofszky, M. Dworzak. On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data. ICPR 2024.
- F. Kowarsch, M. Maurer-Granofszky, L. Weijler, et al. FCM Marker Importance for MRD Assessment in T-cell Acute Lymphoblastic Leukemia: An AIEOP-BFM-ALL-FLOW Study Group Report. Cytometry Part A, 2024.
2023
- A. Uusküla, J. Rannap, L. Weijler, et al. Incarceration History Is Associated with HIV Infection among Community-Recruited People Who Inject Drugs in Europe: A Propensity-Score Matched Analysis of Cross-Sectional Studies. Addiction, 2023.
- F. Kowarsch, L. Weijler, F. Kleber, et al. Explainable Techniques for Analyzing Flow Cytometry Cell Transformers. arXiv preprint, 2023.
2022
- F. Kowarsch, L. Weijler, M. Wödlinger, et al. Towards Self-Explainable Transformers for Cell Classification in Flow Cytometry Data. iMIMIC Workshop at MICCAI 2022.
- M. Wödlinger, M. Reiter, L. Weijler, et al. Automated Identification of Cell Populations in Flow Cytometry Data with Transformers. Computers in Biology and Medicine, 2022.
- L. Weijler, F. Kowarsch, M. Wödlinger, et al. UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia. Cancers, 2022.
2021
- L. Weijler, M. Diem, M. Reiter, M. Maurer-Granofszky. Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP. ICPR 2020.