Picture of Yoan Miche

Yoan Miche

Espoo, Finland
CyberSecurity Research Team Head


D.Sc. in Applied Computer Science from Aalto University, Finland, and INPG, France: "Developing Fast Machine Learning Techniques with Applications to Steganalysis Problems"

M.Sc. (Research) in Telecoms (ENSIMAG Telecom, INPG, France) and Machine Learning (Aalto University, Finland)


Yoan is a former academic who decided to join Nokia Research (now Nokia Bell Labs) in 2014 to work directly on industry specific problems. Prior to joining Nokia Bell Labs, he was a postdoctoral researcher at Aalto University for 4 years, working mainly on applications of Machine Learning to (cyber-)security.

He currently leads the Cybersecurity research team of Nokia Bell Labs Finland.

Research Interests

  • Artificial Intelligence
  • Machine Learning
  • Network Security
  • Statistical & Data Sciences

Selected Articles and Publications

(Full list at https://scholar.google.fi/citations?hl=en&user=zeb71zcAAAAJ )

Miche, Y., Oliver, I., Ren, W., Holtmanns, S., Akusok, A., & Lendasse, A. (2017). Practical estimation of mutual information on non-Euclidean spaces (pp. 123–136). Presented at the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer, Cham.

Miche, Y., Oliver, I., Holtmanns, S., Kalliola, A., Akusok, A., Lendasse, A., & Björk, K.-M. (2016). Data anonymization as a vector quantization problem: Control over privacy for health data (pp. 193–203). Presented at the International Conference on Availability, Reliability, and Security, Springer, Cham.

Miche, Y., Ren, W., Oliver, I., Holtmanns, S., & Lendasse, A. (2019). A framework for privacy quantification: measuring the impact of privacy techniques through mutual information, distance mapping, and machine learning. Cognitive Computation, 11(2), 241–261.

Roshan, S., Miche, Y., Akusok, A., & Lendasse, A. (2018). Adaptive and online network intrusion detection system using clustering and extreme learning machines. Journal of the Franklin Institute, 355(4), 1752–1779.

Laaki, H., Miche, Y., & Tammi, K. (2019). Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery. IEEE Access, 7, 20325–20336.

Akusok, A., Björk, K.-M., Miche, Y., & Lendasse, A. (2015). High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access, 3, 1011–1025.

Akusok, A., Miche, Y., Björk, K.-M., & Lendasse, A. (2019). Per-sample prediction intervals for extreme learning machines. International Journal of Machine Learning and Cybernetics, 10(5), 991–1001.

Atli, B. G., Miche, Y., Kalliola, A., Oliver, I., Holtmanns, S., & Lendasse, A. (2018). Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space. Cognitive Computation, 10(5), 848–863.

Cambria, E., Huang, G.-B., Kasun, L. L. C., Zhou, H., Vong, C. M., Lin, J., et al. (2013). Extreme learning machines [trends & controversies]. IEEE intelligent systems, 28(6), 30–59.

de Souza Junior, A. H., Corona, F., Barreto, G. A., Miche, Y., & Lendasse, A. (2015). Minimal learning machine: a novel supervised distance-based approach for regression and classification. Neurocomputing, 164, 34–44.

Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., & Lendasse, A. (2009). OP-ELM: optimally pruned extreme learning machine. IEEE transactions on neural networks, 21(1), 158–162.

Miche, Y., Van Heeswijk, M., Bas, P., Simula, O., & Lendasse, A. (2011). TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing, 74(16), 2413–2421.

Ren, W., Miche, Y., Oliver, I., Holtmanns, S., Bjork, K.-M., & Lendasse, A. (2017). On Distance Mapping from non-Euclidean Spaces to Euclidean Spaces (pp. 3–13). Presented at the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer.


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