Nikita Klyuchnikov

Graduated from: MIPT, Skoltech (MSc) in 2016

Research interests: Machine Learning, Gaussian Process, Anomaly Detection, Bayesian Optimization, Knowledge Graphs

Thesis title: Multi-Fidelity Active Search

Publications:

• Gurina, E., Klyuchnikov, N., Zaytsev, A., Romanenkova, E., Antipova, K.,Simon, I., Makarov, V., Koroteev, D.(2019). Application of Machine Learning to accidents detection at directional drilling. Journal of Petroleum Science and Engineering.
• Klyuchnikov, N., Mottin, D., Koutrika, G., Müller, E., & Karras, P. (2019). Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging. In Kdd.
• Klyuchnikov, N., Zaytsev, A., Gruzdev, A., Ovchinnikov, G., Antipova, K., Ismailova, L., Muravleva, E., Burnaev, E., Semenikhin, A., Cherepanov, A. and Koryabkin, V. (2019). Data-driven model for the identification of the rock type at a drilling bit. Journal of Petroleum Science and Engineering, 178, 506-516.
• Klyuchnikov, N., & Burnaev, E. (2018).  Gaussian Process Classification for Variable Fidelity Data. arXiv preprint arXiv:1809.05143
• Baranov, A., Burnaev, E., Derkach, D., Klyuchnikov, N., Lantwin, O., Ustyuzhanin, A. & Zaitsev, A. (2017). IOP: Optimising the active muon shield for the SHiP experiment at CERN. Journal of Physics: Conference Series, 934, 012050.

Conferences:

Oral presentation and poster at KDD19, topic: “Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging”