Current Role
Postdoctoral researcher at the ELLIS Institute Finland and the Probabilistic ML group at Aalto University. Our main focus is scaling deep learning to solve probabilistic machine learning tasks, tackling challenges such as efficient inference, out-of-distribution generalization, reasoning capabilities inspired by LLMs, and conditioning on multiple datasets.
Selected Publications
"In-Context Multi-Objective Optimization." ↗ arXiv preprint arXiv:2512.11114 (2025).
"Efficient Autoregressive Inference for Transformer Probabilistic Models." ↗ arXiv preprint arXiv:2510.09477 (2025).
"Federated Variational Inference Methods for Structured Latent Variable Models." ↗ arXiv preprint arXiv:2302.03314 (2024).
"Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis." ↗ arXiv preprint arXiv:2307.15424 (2024).
"Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics." ↗ Philosophical Transactions of the Royal Society A (2023).
Recent Coding Projects
Efficient autoregressive inference for tabular foundation models such as TabPFNv2.