Alexander Nikitin
Machine learning & Mathematics & Programming-
Machine learning for health group @ Aalto University
Finnish Center for Artificial Intelligence
Email: firstname.lastname(at)aalto.fi
GitHub: @AlexanderVNikitin
Twitter: @NikitinAlexV
Blog: Medium
Status
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.
Publications
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:https://doi.org/10.1145/3397481.3450682.
Preprint | ACM
Open Source
Time Series Generative Modeling (TSGM). GitHubSpatio-temporal graph dataset of COVID-19 distribution over the US. GitHub
Talks
Graph kernels via SPDE framework, BayesComp 2023. SlidesTeaching
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.
Supervision
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.