I am currently an Assistant Professor in the Data Science department at Eurecom since September 2019. Previously I was a research scientist at the Chair for the Methods of Machine Learning in the University of Tuebingen (Oct 2018 - Aug 2019), and at the Probabilistic Numerics Group in the Max Planck Institute for Intelligent Systems (Sep 2017 - Sep 2018), working with Prof. Philipp Hennig. Prior to this, I was a postdoc (Apr 2016 - Aug 2017) and a PhD. student (Apr 2013 - Mar 2016) at Institute of Statistical Mathematics, working with Prof. Kenji Fukumizu.
I am currently working on the following research topics:
- Kernel methods and Gaussian processes in machine learning
These are statistical learning methods that make use of positive definite kernels. See our recent review paper on the connections and equivalence between the two approaches.
- Machine learning for computer simulation
Machine learning methods, in particular those based on kernels and Gaussian processes, are very useful in enhancing the power of computer simulation. They enable automatic parameter tuning and model selection, as well as quantification of various kinds of uncertainties.
- Computer simulations for real world problems
Current projects include simulations for evacuation planning and emulations for tsunami early warning.
Please contact me if you are interested in any of the above projects.
Eurecom, Data Science Department, Office 429
Campus SophiaTech, 450 Route des Chappes, 06410 Biot, France
Email: motonobu.kanagawa [CHAN] eurecom.fr (Please replace [CHAN] by @)
We have significantly updated our preprint "Counterfactual Mean Embeddings", now containing several new results (Mar 2020).
We are organizing Workshop on Functional Inference and Machine Intelligence at EURECOM on 17 - 19 Feb 2020.
Our paper "Simulator Calibration under Covariate Shift with Kernels" has been accepted for publication at AISTATS 2020 (Jan 2020).
A new preprint "Simulator Calibration under Covariate Shift with Kernels" has now been available (Oct 2019).
Our paper ""Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models" has been accepted for publication in Machine Learning (Oct 2019).
Our paper "Convergence Guarantees for Adaptive Bayesian Quadrature Methods" by Kanagawa and Hennig has been accepted for publication in NeurIPS 2019 (Sep 2019).
I have moved to Eurecom and started working as an Assistant Professor! (Sep 2019)
Our paper "On the positivity and magnitudes of Bayesian quadrature weights" by Karvonen, Kanagawa and Särkkä has been accepted for publication in Statistics and Computing (Aug 2018).
A new preprint "Convergence Guarantees for Adaptive Bayesian Quadrature Methods" by M. Kanagawa and P. Hennig (27 May 2019)
Our paper "Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings" by Kanagawa, Sriperumbudur and Fukumizu has just been accepted for publication in Foundations of Computational Mathematics (30 Oct 2018)
Editorial Board Member of Journal of Machine Learning Research (JMLR)
TPAMI, Machine Learning, Statistics and Computing, JMLR, etc.
AISTATS(2016,2019, 2020), ICML (2015,2017,2018,2019,2020), NeuIPS (2015,2016,2017,2018,2019,2020)
Antibes, September 2019
Tuebingen, April 2019