I am an associate professor in machine learning.

My main research interests lie within probabilistic graphical models, especially in structure learning in Bayesian networks. 

Academic article

• Juha Harviainen; Pekka Parviainen (2025). On Tractability of Learning Bayesian Networks with Ancestral Constraints. (external link)
• Tuukka Korhonen; Fedor Fomin; Pekka Parviainen (2024). Structural perspective on constraint-based learning of Markov networks. (external link)
• Rune D. Kjærsgaard; Pekka Parviainen; Saket Saurabh et al. (2024). Fair Soft Clustering. (external link)
• Madhumita Kundu; Pekka Parviainen; Saket Saurabh (2024). Time–Approximation Trade-Offs for Learning Bayesian Networks. (external link)
• Madhumita Kundu; Pekka Parviainen; Saket Saurabh (2024). Discovering Bayesian Networks when Few Variables Matter. (external link)
• Amir Miraki; Pekka Parviainen; Reza Arghandeh (2024). Electricity demand forecasting at distribution and household levels using explainable causal graph neural network. (external link)
• Tanmay Inamdar; Madhumita Kundu; Pekka Parviainen et al. (2024). Exponential-Time Approximation Schemes via Compression. (external link)
• Xiangju Qin; Paul Blomstedt; Eemeli Leppäaho et al. (2019). Distributed Bayesian matrix factorization with limited communication. (external link)
• Amir Miraki; Pekka Parviainen; Reza Arghandeh (2025). Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model. (external link)
• Hyeongji Kim; Pekka Parviainen; Ketil Malde (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. (external link)
• Johannes Fiedler; Adria Salvador Palau; Eivind Kristen Osestad et al. (2023). Realistic mask generation for matter-wave lithography via machine learning. (external link)
• Topi Talvitie; Pekka Parviainen (2020). Learning Bayesian Networks with Cops and Robbers. (external link)
• Hyeongji Kim; Changkyu Choi; Michael Christian Kampffmeyer et al. (2025). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)
• Pierre Gillot; Pekka Parviainen (2020). Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph. (external link)
• Pierre Gillot; Pekka Parviainen (2022). Convergence of Feedback Arc Set-Based Heuristics for Linear Structural Equation Models. (external link)
• Osestad Eivind Kristen; Pekka Parviainen; Johannes Fiedler (2025). A novel gas sensing principle based on quantum fluctuations. (external link)
• Brigt Arve Toppe Håvardstun; Cesar Ferri; José Hernández-Orallo et al. (2022). On the trade-off between fidelity and teaching complexity. (external link)

Academic book chapter

• Pierre Gillot; Pekka Parviainen (2022). Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics. (external link)
• Brigt Arve Toppe Håvardstun; Cesar Ferri; José Hernández-Orallo et al. (2023). XAI with Machine Teaching When Humans Are (Not) Informed About the Irrelevant Features. (external link)

Doctoral thesis (PhD)

• Hyeongji Kim; Ketil Malde; Pekka Parviainen (2023). On the Significance of Distance in Machine Learning. (external link)

Conference lecture

• Ketil Malde; Pekka Parviainen; Hyeongji Kim (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. (external link)

Conference poster

• Hyeongji Kim; Changkyu Choi; Michael Christian Kampffmeyer et al. (2024). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)
• Hyeongji Kim; Changkyu Choi; Michael Christian Kampffmeyer et al. (2024). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)

See a complete overview of publications in NVA.

List of my publications can be found in my google scholar profile.