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.


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)

Selected articles and publications

(Full list at )

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.