Alexander Nikitin

Machine learning & Mathematics & Programming
Machine learning for health group @ Aalto University
Finnish Center for Artificial Intelligence

Email: firstname.lastname(at)
GitHub: @AlexanderVNikitin
Twitter: @NikitinAlexV
Blog: Medium


I work on probabilistic and human-in-the-loop machine learning. In particular, I am interested in Gaussian processes, generative models, and interactive methods. My research is driven by applications, and I continuously work with technology companies and help them to implement my findings. Application-wise, I am interested in telecommunications, biology, and health.


Daniel Augusto de Souza, Alexander Nikitin, S. T. John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João P. P. Gomes, Diego Mesquita, and César Lincoln Mattos. Thin and Deep Gaussian Processes. 2023. Accepted to NeurIPS.
Preprint | Code

Alexander Nikitin, Letizia Iannucci, Samuel Kaski. TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series. 2023. Submitted.
Preprint | Code

Nikitin, Alexander, and Samuel Kaski. 2022. "Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations." In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3682-3690.
Preprint | ACM | Poster | Presentation

Nikitin, Alexander V., S. T. John, Arno Solin, and Samuel Kaski. 2022. Non-separable spatio-temporal graph kernels via SPDEs. In International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR.
Preprint | PMLR | Poster | Code

Alexander Nikitin and Samuel Kaski. 2021. Decision Rule Elicitation for Domain Adaptation. 26th International Conference on Intelligent User Interfaces. Association for Computing Machinery, New York, NY, USA, 244–248. DOI:
Preprint | ACM

Open Source

Time Series Generative Modeling (TSGM). GitHub
Spatio-temporal graph dataset of COVID-19 distribution over the US. GitHub


Graph kernels via SPDE framework, BayesComp 2023. Slides


Machine learning: Advanced Probabilistic Mehtods (2021-2023), TA, Aalto University,
Bayesian Data Analysis: Global South (2021), TA, Aalto University,
Principles of Algorithmic Techniques (2020), TA, Aalto University.


Jesse Hämäläinen (BS, Aalto University), Attributed Graph Clustering with Graph Neural Networks, 2022,
Letizia Iannucci (BS, Aalto University), Improving the Accuracy of Time Series Classification through Data Augmentation, 2021,
Vinh Nguyen (BS, Aalto University), Towards Deep Learning in Predictive Maintenance, 2020.

Community Service

I am a reviewer for NeurIPS, AISTATS, IEEE Transactions on Pattern Analysis and Machine Intelligence, EMNLP, and other ML and data mining conferences and journals.