Juha Harviainen
I am an HIIT Postdoctoral Fellow in the Graph Algorithms and Bioinformatics research group at the University of Helsinki, under the supervision of Prof. Alexandru I. Tomescu. I earned my doctoral degree in 2024 under the supervision of Prof. Mikko Koivisto at the University of Helsinki. My research focuses currently on parameterized complexity in a variety of settings such as bioinformatics and artificial intelligence, with further interests in counting problems, randomized algorithms and fine-grained complexity theory.
Research
- Identifying All Snarls and Superbubbles in Linear-Time, via a Unified SPQR-tree Framework. Francisco Sena, Aleksandr Politov, Corentin Moumard, Manuel Cáceres, Sebastian Schmidt, Juha Harviainen, Alexandru I. Tomescu. 2025. arXiv preprint.
- Scaling Up Bayesian DAG Sampling. Daniele Nikzad, Alexander Zhilkin, Juha Harviainen, Jack Kuipers, Giusi Moffa, Mikko Koivisto. 2025. arXiv preprint.
- Improving Decision Trees through the Lens of Parameterized Local Search. Juha Harviainen, Frank Sommer, Manuel Sorge. NeurIPS 2025 (to appear).
- Graph Reconstruction with the Connected Components Oracle. Juha Harviainen, Pekka Parviainen. 2025. arXiv preprint.
- Quantum Speedups for Bayesian Network Structure Learning. Juha Harviainen, Kseniya Rychkova, Mikko Koivisto. UAI 2025.
- Optimal Decision Tree Pruning Revisited: Algorithms and Complexity. Juha Harviainen, Frank Sommer, Manuel Sorge, Stefan Szeider. ICML 2025.
- On Tractability of Learning Bayesian Networks with Ancestral Constraints. Juha Harviainen, Pekka Parviainen. AISTATS 2025.
- Estimating the Permanent by Nesting Importance Sampling. Juha Harviainen, Mikko Koivisto. ICML 2024.
- Faster Perfect Sampling of Bayesian Network Structures. Juha Harviainen, Mikko Koivisto. UAI 2024.
- Revisiting Bayesian Network Learning with Small Vertex Cover. Juha Harviainen, Mikko Koivisto. UAI 2023.
- On Inference and Learning With Probabilistic Generating Circuits. Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto. UAI 2023.
- A Faster Practical Approximation Scheme for the Permanent. Juha Harviainen, Mikko Koivisto. AAAI 2023.
- Trustworthy Monte Carlo. Juha Harviainen, Mikko Koivisto, Petteri Kaski. NeurIPS 2022.
- Approximating the Permanent with Deep Rejection Sampling. Juha Harviainen, Antti Röyskö, Mikko Koivisto. NeurIPS 2021.
- Software Framework for Data Fault Injection to Test Machine Learning Systems. Jukka K. Nurminen, Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen, Tommi Mikkonen. ISSRE Workshops 2019.
Other
- Tie koodariksi. A website for Finnish schools for teaching programming made by me, Antti Laaksonen, Roope Salmi and Topi Talvitie.
- Advances in Sampling and Counting Bipartite Matchings and Directed Acyclic Graphs. Juha Harviainen. 2024. Doctoral dissertation.
Contact
You can contact me via email:
juha.harviainen@helsinki.fi
Juha Harviainen 