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Laure Ciernik

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Technische Universität Berlin
Machine Learning (ML)

Marchstraße 23, 10587 Berlin
https://web.ml.tu-berlin.de/

© BIFOLD

Laure Ciernik

Doctoral Researcher

Laure Ciernik is a PhD student at the TU Berlin Machine Learning Group. She completed her MSc at ETH Zurich in Data Science, focusing on ML for Healthcare and Bioinformatics. Her master’s thesis was conducted at the Boeva Lab for Computational Cancer Genomics. Laure’s research interests revolve around the intersection of Machine Learning and Biomedicine.

  • Biomedical Data Analysis
  • Computational genomics
  • Computational pathology
  • Explainable AI

Mina Jamshidi Idaji, Julius Hense, Tom Neuhäuser, Augustin Krause, Yanqing Luo, Oliver Eberle, Thomas Schnake, Laure Ciernik, Farnoush Rezaei Jafari, Reza Vahidimajd, Jonas Dippel, Christoph Walz, Frederick Klauschen, Andreas Mock, Klaus-Robert Müller

Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology

March 09, 2026
https://doi.org/10.48550/arXiv.2603.08328

Marco Morik, Laure Ciernik, Lukas Thede, Luca Eyring, Shinichi Nakajima, Zeynep Akata, Lukas Muttenthaler

Revealing Task-Dependent Layer Relevance via Attentive Multi-Layer Fusion

March 02, 2026
https://openreview.net/forum?id=cc417AET6g

Laure Ciernik, Agnieszka Kraft, Florian Barkmann, Josephine Yates, Valentina Boeva

Robust and efficient annotation of cell states through gene signature scoring

February 18, 2026
https://doi.org/10.1101/gr.280926.125

Laure Ciernik, Marco Morik, Lukas Thede, Luca Eyring, Shinichi Nakajima, Zeynep Akata, Lukas Muttenthaler

Beyond the final layer: Attentive multilayer fusion for vision transformers

January 14, 2026
https://doi.org/10.48550/arXiv.2601.09322

Laure Ciernik, Marco Morik, Lukas Thede, Luca Eyring, Shinichi Nakajima, Zeynep Akata, Lukas Muttenthaler

Attentive Multi-Layer Fusion for Vision Transformers

January 14, 2026
https://doi.org/10.48550/arXiv.2601.09322

News
Machine LearningBIFOLD Update| Jul 23, 2026

AI for the Sciences

The greatest potential of artificial intelligence may lie not only in solving existing tasks more efficiently, but in fundamentally transforming science itself. The Hector Fellow Academy’s new video profile, “AI in Science: How AI Is Changing the Questions We Ask – Klaus-Robert Müller,” explores how artificial intelligence is enabling new discoveries and why it is poised to shape the future of research for years to come.

News
BIFOLD Update| Apr 19, 2024

Girls' Day: dEIn Labor and BIFOLD offer AI workshop

TU Berlin is once again participating in this year's "Girls' Day" on April 25, 2024. dEIn Labor and BIFOLD offer a workshop for schoolgirls from the age of 15.