Ali Burak Ünal

Eberhard Karls Universität Tübingen

H-index: 5

Europe-Germany

About Ali Burak Ünal

Ali Burak Ünal, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Eberhard Karls Universität Tübingen, specializes in the field of Machine Learning, Computational Biology, Privacy preserving machine learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption

A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods

Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach

ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves

A Privacy-Preserving Federated Learning Approach for Kernel methods

Towards a complete privacy preserving machine learning pipeline

CECILIA: Comprehensive secure machine learning framework

Escaped: Efficient secure and private dot product framework for kernel-based machine learning algorithms with applications in healthcare

Ali Burak Ünal Information

University	Eberhard Karls Universität Tübingen
Position	___
Citations(all)	89
Citations(since 2020)	89
Cited By	20
hIndex(all)	5
hIndex(since 2020)	5
i10Index(all)	3
i10Index(since 2020)	3
Email	Access Email
University Profile Page	Eberhard Karls Universität Tübingen
Google Scholar	View Google Scholar Profile

Ali Burak Ünal Skills & Research Interests

Machine Learning

Computational Biology

Privacy preserving machine learning

Top articles of Ali Burak Ünal

Title	Journal	Author(s)	Publication Date
FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption	arXiv preprint arXiv:2403.14428	Cem Ata Baykara Ali Burak Ünal Mete Akgün	2024/3/21
A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods		Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün	2023/11/1
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach		Nico Pfeifer Mete Akgün Ali Burak Ünal Sofiane Ouaari	2023/9/8
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves	Network and System Security	Nico Pfeifer Mete Akgün Ali Burak Ünal	2023/8/7
A Privacy-Preserving Federated Learning Approach for Kernel methods	arXiv preprint arXiv:2306.02677	Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün	2023/6/5
Towards a complete privacy preserving machine learning pipeline		Ali Burak Ünal	2022/8/5
CECILIA: Comprehensive secure machine learning framework	arXiv preprint arXiv:2202.03023	Ali Burak Ünal Nico Pfeifer Mete Akgün	2022/2/7
Escaped: Efficient secure and private dot product framework for kernel-based machine learning algorithms with applications in healthcare	Proceedings of the AAAI Conference on Artificial Intelligence	Ali Burak Ünal Mete Akgün Nico Pfeifer	2021/5/18
Privacy preserving gaze estimation using synthetic images via a randomized encoding based framework		Efe Bozkir Ali Burak Ünal Mete Akgün Enkelejda Kasneci Nico Pfeifer	2020/6/2
Privacy-preserving SVM on outsourced genomic data via secure multi-party computation		Huajie Chen Ali Burak Ünal Mete Akgün Nico Pfeifer	2020/3/16
PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering	Bioinformatics	Yasin Ilkagan Tepeli Ali Burak Ünal Furkan Mustafa Akdemir Oznur Tastan	2020/11/1
Identifying disease-causing mutations with privacy protection	Bioinformatics	Mete Akgün Ali Burak Ünal Bekir Ergüner Nico Pfeifer Oliver Kohlbacher	2020/11/1