CV

My full CV is available as a PDF. A summary follows below.

Education

Ph.D in Computer Science, University of Tübingen, 2026 (expected)
M.Sc. in Computer Science, Sabanci University, 2022
B.Sc. in Computer Science and Engineering, Sabanci University, 2019
B.Sc. in Electronics Engineering, Sabanci University, 2018

Publications

Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

Mağara, Ş. S., Havemann, E., Jutz, D., Ünal, A. B., & Akgün, M. (2026). Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation. In International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB). https://arxiv.org/abs/2608.20118

Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach

Mağara, Ş. S., Dietrich, N., Ünal, A. B., & Akgün, M. (2024). Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach. Journal of Biomedical Informatics, Volume 171, 2025, 104920, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2025.104920.

Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy

Mağara, Ş. S., Motorcu, I., Mektepli, E. Ş., Baykara, C., Ünal, A. B., & Akgün, M. (2025). Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy. IEEE Access. https://ieeexplore.ieee.org/document/11126887

Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models

Balaban, B., Magara, S. S., Yilgor, C., Yucekul, A., Obeid, I., Pizones, J., Kleinstueck, F., Perez-Grueso, F. J. S., Pellisé, F., Alanay, A., Savas, E., Bağcı, Ç., Sezerman, O. U., & European Spine Study Group. (2025). Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models. IEEE Access, 13, 37431-37456.

Privacy Preserving Data Imputation via Multi-Party Computation for Medical Applications

Jentsch, J., Ünal, A. B., Mağara, Ş. S., & Akgün, M. (2024). Privacy Preserving Data Imputation via Multi-Party Computation for Medical Applications. In Proceedings of the 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom) (pp. 1–6). IEEE. https://doi.org/10.1109/HealthCom60970.2024.10880795

ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

Mağara, Ş. S., Yıldırım, C., Yaman, F., Dilekoğlu, B., Tutaş, F. R., Öztürk, E., Kaya, K., Taştan, Ö., & Savaş, E. (2021). ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies. arXiv preprint, arXiv:2110.11446. https://doi.org/10.48550/arXiv.2110.11446

Şeyma Selcan

CV

Education

Publications

Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach

Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy

Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models

Privacy Preserving Data Imputation via Multi-Party Computation for Medical Applications

ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

Teaching

Teaching Assistant – Introduction to Cryptography

Tutor – Secure Processing of Medical Data: Privacy-Enhancing Technologies in Practice

Teaching Assistant – Cryptography

Teaching Assistant – Logic and Digital Systems Design

Teaching Assistant – Distributed Systems

Undergraduate TA – Discrete Mathematics

Undergraduate TA – Electronic Circuits Implementations

Undergraduate TA – Electronic Circuits I