Markus Heinonen graduated with a PhD in machine learning from University of Helsinki in 2013. He visited TeleCom ParisTech for a two-year postdoc period, and joined Aalto University in 2015. He is currently an Academy Research Fellow.
His research focuses on Gaussian processes, Bayesian deep learning, dynamical models and reinforcement learning with applications in bio- and chemoinformatics and robotics. He has 32 peer-reviewed publications with an H-index of 17. His publication record contains recent contributions to NIPS, ICML, AISTATS and ICLR.
He is a co-organizer of a Dagstuhl Seminar on “Continuous-time Deep learning”, which was postponed due to Covid. He is the lead scholar in the Academy of Finland BREAL consortium 2020-22: Bridging the Reality Gap in Autonomous Learning, and he was awarded the Academy research fellowship for 2020-24. He co-organizes the FCAI machine learning “coffee” seminar.
His recent research in 2020 contains works on developing more efficient reinforcement learning with deep Gaussian processes; on introducing new low-rank Bayesian neural networks with state-of-the-art performance; on optimising synthetic reactions with active learning [2]; and on proposing new kinds of convolutional spectral kernels [1]. He is one of the organizers in a Dagstuhl Seminar on “Continuous-Time Deep learning”, which was postponed due to Covid.
Sample of publications in 2020:
[1] Zheyang Shen, Markus Heinonen, Samuel Kaski. Learning spectrograms with convolutional spectral kernels. Proceedings 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 3826-3836, 2020.
[2] Sanni Voutilainen, Markus Heinonen, Martina Andberg, Emmi Jokinen, Hannu Maaheimo, Johan Pääkkönen, Nina Hakulinen, Juha Rouvinen, Harri Lähdesmäki, Samuel Kaski, Juho Rousu, Merja Penttilä, Anu Koivula. Substrate specificity of 2-Deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods. Applied Microbiology and Biotechnology, 104, pp. 10515-29, 2020.