Summer internship positions in the Trustworthy Machine Learning group in 2024

1. Differentially private machine learning (multiple positions)

Supervisor: Antti Honkela + PhD students and postdocs

Background: machine learning, mathematics, programming skills (Python, PyTorch and/or JAX)

Differentially private machine learning studies learning methods that can operate while guaranteeing privacy of the data subjects. These methods can be applied to solving predictive learning problems on private data but also for creating provably anonymised data sets. We apply differential privacy in the context of various modern machine learning methods, including Bayesian methods, deep learning and federated learning.

In this project you will participate in developing, analysing and applying new differentially private machine learning methods. Depending on your background and interests, the work will combine working on the mathematical theory of differential privacy, general methods development, implementation and application of the developed methods in different applications.

You will join an active community of ~10 researchers with diverse backgrounds working in the Research Programme in Privacy-Preserving and Secure AI at the Finnish Center for Artificial Intelligence FCAI. We are also affiliated with the DataLit project and the European Lighthouse on Secure and Safe AI (ELSA).

The topic is suitable for a Master's thesis topic.

Examples of related MSc theses done in the group:

Marlon Tobaben. Hyperparameters and neural architectures in differentially private deep learning. MSc Thesis, University of Helsinki, 2022.

Ossi Räisä. Differentially Private Metropolis–Hastings Algorithms. MSc Thesis, University of Helsinki, 2021.

Examples of our recent related papers:

Ossi Räisä, Joonas Jälkö, Samuel Kaski, and Antti Honkela. Noise-Aware Statistical Inference with Differentially Private Synthetic Data. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023) (2023).

Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, and Antti Honkela. On the Efficacy of Differentially Private Few-shot Image Classification. Transactions on Machine Learning Research, December 2023.

Antti Koskela, Marlon Tobaben, and Antti Honkela. Individual Privacy Accounting with Gaussian Differential Privacy. In Proceedings of the 11th International Conference on Learning Representations (ICLR 2023) (2023).

Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021) (2021).