Prof. Dr. Fatma Deniz is an academic whose research lies at the intersection of computer science and cognitive neuroscience, focusing on artificial intelligence, language processing, brain imaging, and computational models. In 2025, she was elected rector of Technische Universität Berlin (TU Berlin), with her term set to begin on 1 April 2026. Previously, she served as vice-rector for digitalization and sustainability at the same university.

Fatma Deniz was born on 11 June 1983 in Munich, Germany. Her family has Turkish origins. She completed her secondary education at Bursa Girls’ High School in Turkey, graduating as valedictorian in 1997–2001. After secondary school, she returned to Germany and completed her undergraduate studies in computer science in Munich. She earned her PhD in computer science from Technische Universität Berlin in collaboration with the Berlin Bernstein Center for Computational Neuroscience (BCCN).

Between 2013 and 2020, she conducted research at the University of California, Berkeley, in the United States. During this period, she worked on computational modeling of large-scale brain data and was affiliated with the Berkeley Institute for Data Science (BIDS).

After her time in the United States, she returned to Berlin and in April 2023 assumed the position of head of the Department of Language and Communication in Biological and Artificial Systems at TU Berlin. This professorship is funded by the Berlin Equal Opportunities Program.

She is a member of the Berlin Bernstein Center for Computational Neuroscience (BCCN) and actively participates in the Berlin University Alliance, which comprises Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin, and Charité – Universitätsmedizin Berlin.

Prof. Dr. Deniz’s research integrates disciplines including computer science, neuroscience, linguistics, and music. The central focuses of her work are:

• Neural representation of sensory information: Investigating how the brain encodes language and other sensory stimuli.
• fMRI-based computational modeling: Applying machine learning approaches to large-scale brain imaging data.
• Language and communication processes: Studying language processing mechanisms in both biological and artificial systems.
• Cross-modal studies: Focusing on how language is represented across different sensory modalities in the human brain.
• Open science, reproducible research, and responsible use of artificial intelligence: Contributing to improvements in academic production processes.

On 24 January 2024, Prof. Dr. Fatma Deniz was elected by the Extended Academic Senate of TU Berlin as vice-rector for digitalization and sustainability. In this role, she led the development of the university’s digital transformation strategies and sustainable campus policies.

During her tenure, she was responsible for areas including the digitalization of educational infrastructure, the responsible integration of data science and artificial intelligence applications, and the strengthening of institutional sustainability processes.

The TU Berlin rectoral election took place in December 2025. The process began with five candidates; after the first round, three candidates withdrew, leaving two finalists: Prof. Dr. Fatma Deniz and the incumbent rector, Prof. Dr. Geraldine Rauch, who competed in the second round.

In the vote conducted by the Academic Senate:

• Prof. Dr. Fatma Deniz: 42 votes
• Prof. Dr. Geraldine Rauch: 18 votes
• Invalid vote: 1

With this result, Fatma Deniz received more than two-thirds of the Senate’s support and was elected as the new rector of Technische Universität Berlin. She will assume office on 1 April 2026.

• Kitzes, J., Turek D., Deniz F., editors, The Practice of Reproducible Research: Case Studies in Data Science, UC Press, 2017.^【1】

• Deniz F., "pyMooney: Generating a Database of Two-Tone, Mooney Images". In The Practice of Reproducible Research: Case Studies in Data Science. Kitzes, J., Turek D., Deniz F., editors. UC Press, 2017.
• Turek D., Deniz F., "Introducing the Case Studies". In The Practice of Reproducible Research: Case Studies in Data Science. Kitzes, J., Turek D., Deniz F., editors. UC Press, 2017.^【2】

• Negi, A.*, Oota, S. R.*, Nunez-Elizalde, A., Gupta, M., Deniz F., Brain-Informed Fine-Tuning for Improved Multilingual Understanding in Language Models. NeurIPS, 2025.
• Chen C., Dupre la Tour T., Gallant J. L., Klein D., Deniz F., The cortical representation of language timescales is shared between reading and listening. Nature Communications Biology 7, 284, 2024.
• Elfaramawy N., Deniz F., Grunske L., Hilbrich M., Kehrer T., Lamprecht A-L., Mendling J., Weidlich M., On Managing Large Collections of Scientific Workflows. Modellierung 2024 Satellite Events. Gesellschaft für Informatik e.V.. RDiMOD. Potsdam. March 12–15.
• Gong X. L., Huth A. G., Deniz F., Johnson K., Gallant J. L., Theunissen F. E., Phonemic segmentation of narrative speech in human cerebral cortex. Nature Communications, 14:4309, 2023.
• Deniz F.*, Tseng C.*, Wehbe L., Dupre la Tour T., Gallant J. L., Semantic representations during language comprehension are affected by context. Journal of Neuroscience, Featured Research, 28 March 2023.
• Lamarre M., Chen C., Deniz F., Attention weights accurately predict language representations in the brain. Association for Computational Linguistics: EMNLP (Empirical Methods in Natural Language Processing), pages 4513–4529, 2022.
• Popham S., Huth A. G., Bilenko N. Y., Deniz F., Gao, S. J., Nunez-Elizalde A., Gallant J. L., Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature Neuroscience 24, 1628–1636, 2021.
• Ramakrishnan K. and Deniz F., Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract Words. In Proceedings of NAACL HLT Workshop on Cognitive Modeling and Computational Linguistics (CMCL), 2021.
• Deniz F., Nunez-Elizalde A.O., Huth A. G., Gallant J. L., The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality. Journal of Neuroscience, 39(39):7722–7736, 2019.
• Wu M.*, Deniz F.*, Prenger R., Gallant J. L., The unified maximum a posteriori (MAP) framework for neuronal system identification. (* First authorship shared), arXiv, https://arxiv.org/abs/1811.01043, 2018.
• Holdgraf C., Culich A., Rokem A., Deniz F., Ushuzima D., Alegro M., Portable learning environments for hands-on computational instruction: Using container- and cloud-based technology to teach data science. In Proceedings of the Practice and Experience in Advanced Research Computing on Sustainability, Success and Impact (p. 32), ACM, 2017.
• Castelluccia C., Duermuth M., Golla M., Deniz F., MooneyAuth: Towards Practical Implicit Memory-based Authentication. In Network and Distributed System Security Symposium (NDSS), 2017.
• Kizilirmak J. M., Gomes da Silva J. G., Imamoglu F., Richardson-Klavehn A., Generation and the subjective feeling of "aha!": Independent contributions to learning from insight. Psychological Research 80 (6), 1059–1074, 2015.
• Castelluccia C., Duermuth M., Imamoglu F., Learning from Neuroscience to Improve Internet Security. ERCIM News, Research and Innovation, 99:46, 2014.
• Imamoglu F., Heinzle J., Imfeld A., Haynes J.-D., Activity in high-level brain regions reflects visibility of low-level stimuli. Neuroimage, 102:688–694, 2014.
• Imamoglu F., Kahnt T., Koch C., Haynes J.-D., Changes in functional connectivity support conscious object recognition. Neuroimage, 63:1909–1917, 2012.^【3】