Christian Bock

Welcome! I'm a machine learning scientist interested in topological data analysis, time series analysis, the intersection of ML & healthcare, and the fundamentals of deep learning. I hold a Ph.D. in Machine Learning and Healthcare from ETH Zurich, where I worked under the supervision of Professor Karsten Borgwardt. Currently, I work as Applied Scientist at AWS.


2022/11/01 I started as Applied Scientist at AWS.
2021/12/01 I started a new position as Solution Architect at Machine Learning Architects Basel.
2021/10/18 I successfully defended my PhD!
2021/03/02 I am a student organizer of the ICLR workshop on Geometrical and Topological Representation Learning Link
2021/03/01 I co-organize the Clinical Machine Learning Track of the EPFL Applied Machine Learning Days Series Link
2021/03/01 Successfully finished my AWS internship with a manuscript on time series anomaly detection using state space Gaussian processes. arXiv link to follow.
2020/11/02 Excited to continue my research on time series at Amazon as an Applied Scientist Intern.
2020/10/01 I started the fourth year of my PhD!
2020/09/26 Our NeurIPS submission on "Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence" was accepted as a spotlight. Link
2020/09/02 I am happy to be part of the program committee of the "Topological Data Analysis and Beyond" workshop at NeurIPS 2020. Link
2020/07/22 I started blogging my summaries of the MLSS lectures. The first one is about causality. Link
2020/05/27 New preprint on time series imputation strategies. Link
2020/04/29 I was lucky enough to got selected for participation at the Machine Learning Summer School 2020! Link
2020/03/09 Our work on circulatory failure prediction in the ICU was published in Nature Medicine. Link
2019/11/16 Published a new blog post about our Wasserstein Kernel for Time Series. Link
2019/10/01 I started the third year of my PhD!
2019/10/01 Our Wasserstein-based subsequence kernel for time series has been accepted at the Optimal Transport in Machine Learning (OTML) Workhop at NeurIPS. Link
2019/08/09 Our Wasserstein-based subsequence kernel for time series has been accepted at ICDM. Link
2019/06/09 I published a blog article on persistent homology. Link
2019/04/21 Our topological extension of the Weisfeiler-Lehman algorithm for graph classification has been accepted for poster presentation and a short talk at ICML. Link
2019/03/17 I published my first blog article on S3M. Link
2018/12/18 Our work on Neural Persistence has been accepted for poster presentation at ICLR. Link
2018/10/01 I started the second year of my PhD!
2018/06/19 I presented our work on S3M at the Personalized Health Technologies Research Conference in Zurich. Link
2018/03/01 Our work on association mapping in biomedical time series (S3M) was accepted at ISMB and published in Bioinformatics. Link
2017/10/01 I started my PhD at ETH Zurich in Karsten Borgwardt's Group of Machine Learning and Computational Biology Link