In addition to doing research in Machine Learning, Mikko Honkala leads the Machine Learning Methods and Applications group, which belong to the AI Research Laboratory in Bell Labs Solutions Research. We are based mainly on Espoo, Finland.
Our group consists of some of the best machine learning and artificial intelligence talent in Finland with a wide range of machine learning expertise, from theoretical to practical skills.
Our projects have different time scales, from developing novel machine learning methods for long term, e.g., in differential privacy, all the way through medium-term research on robotics, reinforcement learning and wireless AI.
Our way of working includes collaborating with domain experts, while focusing on the machine learning part of the collaboration projects. Very good examples of this are the DeepRx and DeepTx projects, which are done in tight collaboration with Bell Labs Radio Systems Research Finland among others. Most of our projects have both long- and short-term deliverables and we are always seeking opportunities to create the biggest impact either through business units, IPR or publications.
Ph. D. (Tech) degree in Computer Science, Aalto University Espoo Finland (formerly Helsinki University of Technology)
M.sc. (Hon.) degree in in Computer Science, Aalto University Espoo Finland (formerly Helsinki University of Technology)
Mikko acts as reviewer in many publications in IEEE and ACM, including e.g. transactions on wireless communications, NeurIPS, ICML, ICLR.
Selected articles and publications
Mikko's publications are available at his Google Scholar page.
A selection of publications:
Semi-supervised learning with ladder networks
A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko
Advances in Neural Information Processing Systems, 3546-35542015
Bidirectional recurrent neural networks as generative models
M Berglund, T Raiko, M Honkala, L Kärkkäinen, A Vetek, JT Karhunen
Advances in Neural Information Processing Systems, 856-864, 2015
M. Honkala, D. Korpi, and J. M. J. Huttunen, “DeepRx: Fully convolutional deep learning receiver,” IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3925–3940, Jun. 2021.
D. Korpi, M. Honkala, J. M. J. Huttunen, and V. Starck, “DeepRx MIMO: Convolutional MIMO detection with learned multiplicative transformations,” in Proc. IEEE International Conference on Communications (ICC), Jun. 2021.
J. Pihlajasalo, D. Korpi, M. Honkala, J. M. J. Huttunen, T. Riihonen, J. Talvitie, M. A. Uusitalo, and M. Valkama, “Deep learning based OFDM physical-layer receiver for extreme mobility,” in Proc. Asilomar Conference on Signals, Systems, and Computers (ASILOMAR), Nov. 2021. IEEE
J. Pihlajasalo, D. Korpi, M. Honkala, J. M. J. Huttunen, T. Riihonen, J. Talvitie, A. Brihuega, M. A. Uusitalo, and M. Valkama, “HybridDeepRx: Deep learning receiver for high-EVM signals,” in Proc. IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sep. 2021.
J. M. J. Huttunen, D. Korpi, M. Honkala, “DeepTx: Deep Learning Beamforming with Channel Prediction,” IEEE Transactions on Wireless Communications. 2022