Curriculum Vitae
Below is a detailed CV. If you want to download a traditional CV, click the icon below.
Research
Research interests:
– Numerical linear algebra
– Tensor networks
– Non-convex and Riemannian optimization
– Machine learning
Generally I’m interested in tensors and numerical linear algebra with a focus on applications to machine learning. Currently I’m working on developing a streaming sketch algorithm for tensor trains. This will make it feasible to compute tensor train decompositions of very large tensors in a distributed setting.
On the weekends I like to study topics in data science, bioinformatics and scientific computing to broaden my knowledge. I do this by either taking online courses, reading text books, or doing small programming projects. For the latter I usually write blog posts on this website.
Publications and preprints
Streaming Tensor Train Approximation August 2022
Joint work with Daniel Kressner and Bart Vandereycken
TTML: tensor trains for general supervised machine learning March 2022
Joint work with Bart Vandereycken
Recovering data you have never seen April 2021, published in The Science Breaker
On certain Hochschild cohomology groups for the small quantum group April 2021, published in Journal of Algebra
Joint work with Nicolas Hemelsoet
A computer algorithm for the BGG resolution November 2019, published in Journal of Algebra
Joint work with Nicolas Hemelsoet
Parallel 2-transport and 2-group torsors October 2018
Higher Gauge Theory February 2018 (master thesis)
Open source contributions
Work experience
March 2018–December 2022 (expected):

PhD student at University of Geneva.
I was working in pure mathematics from 2018 until early 2020, when I switched research direction to applied math. Over the past few years a significant fraction of my time is spent writing research code in Python, both numerical code and code for computer algebra. I spend about 20% of my time teaching. I also spend about 20% of my time studying to broaden my knowledge about data science and scientific computing, either by doing online courses, reading text books, or doing small programming projects.
May 2021–present:

Senior Scientific Editor at The Science Breaker.
The goal of this journal is to make the core ideas behind published scientific research accessible to a wide audience to foster interest in science. It is also an excellent and informal way for scientists to get a flavor of the research and scientific methods of very different fields. As an editor I propose new articles and edit them to make them easier to read for laypersons.
2014-2016:

Teaching assistant at Utrecht University.
I was a teaching assistant for four different courses during my time as a student at Utrecht.
Education
2021/02
– Neuroscience and Neuroimaging Specialization, at Coursera.
2020/09
– Genomic Data Science Specialization, at Coursera.
2019/08
– Advanced Machine Learning Specialization, at Coursera.
2016-2017
– Masterclass Geometry, Topology and Physics, at University of Geneva.
2015-2018
– Masters degree Mathematical Sciences, at Utrecht University (cum laude, GPA 4.00).
– Honors degree “Utrecht Geometry Center”, at Utrecht University.
2012-2015
– Bachelor degree Mathematic, at Utrecht University (cum laude, GPA 4.00).
– Bachelor degree Physics and Astronomy, at Utrecht University (cum laude, GPA 4.00).
Skills
Programming languages
Advanced
– Python
Intermediate
– LaTeX
– Mathematica
– C/C++
Beginner
– R
– SQL
Tools
Armadillo,
Bash,
CVXPY,
Cython,
Docker,
Linux,
Networkx,
NumPy,
Pandas,
PyTorch,
Sagemath,
SciPy,
Tensorflow,
Windows
Languages
C2 (native) Level
– Dutch
– English
B1 Level
– French
A2 Level
– Japanese
– Russian
– Spanish
Mathematical expertise
I have a wide background in pure and applied mathematics, and I feel comfortable with research-level mathematics in the following areas:
Applied mathematics:
– Bayesian statistics
– Computer vision
– Convex optimization
– Inverse problems
– Machine learning
– Multivariate statistics
– Neural networks
– Non-convex optimization
– Numerical linear algebra
– Quantum computing
– Riemannian optimization
– Signal processing
– Tensor networks
– Time series analysis
Pure mathematics:
– Algebraic topology
– Category theory
– Deformation quantization
– Differential geometry
– Fiber bundles
– Homological algebra
– Lie groupoids / algebroids
– Lie theory
– Moduli spaces
– Operads
– Poisson geometry
– Tensor / monoidal categories