Lisa Weijler is a university assistant and PhD student at the Computer Vision Lab (CVL), Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria. Her research interests are machine learning for 3D and unstructured data, especially point clouds. Additionally, she is passionate about combining her research with medical, social or political science in interdisciplinary projects.
Education
| 2020 | MSc. TU Wien, Biomedical Engineering. Thesis title: Detection of rare cell populations in flow cytometry data with small training sets. Thesis advisor: Michael Reiter |
| 2018 | Instituto Superior Técnico, Biomedical Engineering. Erasmus exchange |
| 2016 | BSc. TU Wien, Technical Mathematics. Thesis title: Cluster Analysis of Base Components within the BOSCH ECU-Software. Thesis advisor: Lothar Nannen |
Publications
- 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”. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 7956-7964. 2024.
- A. Uusküla, J. Rannap, L. Weijler, A. Abagiu, V. Arendt, G. Barrio, H. Barros 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 118, no. 11 (2023): 2177-2192.
- F. Kowarsch, L. Weijler, M. Wödlinger, M. Reiter, M. Maurer-Granofszky, A. Schumich, E. O. Sajaroff et al. “Towards self-explainable transformers for cell classification in flow cytometry data.” In International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, pp. 22-32. Cham: Springer Nature Switzerland, 2022.
- M. Wödlinger, M. Reiter, L. Weijler, M. Maurer-Granofszky, A. Schumich, E. O. Sajaroff, S. Groeneveld-Krentz et al. “Automated identification of cell populations in flow cytometry data with transformers“. Computers in Biology and Medicine 144 (2022): 105314.
- L. Weijler, F. Kowarsch, M. Wödlinger, M. Reiter, M. Maurer-Granofszky, A. Schumich and M. Dworzak. “UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia”. Cancers 2022, 14(4), 898; https://doi.org/10.3390/cancers14040898
- L. Weijler, M. Diem, M. Reiter, M. Maurer-Granofszky, A. Schumich and M. Dworzak. “Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP”. In Proceedings of the 25th International Conference on Pattern Recognition (ICPR), pp. 4903-4909. IEEE, 2021.
Talks
- “Efficient continuous group convolutions for local SE(3) equivariance in 3D point clouds”, Computer Vision Winter Workshop, February 2024
- “UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia”, International Conference on Machine Vision and Applications, Virtually, February 2022