# Curriculum Vitae

Below is a detailed CV. If you want to **download a traditional CV**, click one of the two icons 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

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

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

**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.

**March 2018–present**:

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.

**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

**Beginner**

– C/C++

– R

**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

– Neural networks

– Non-convex optimization

– Numerical linear algebra

– Quantum computing

– Riemannian optimization

– Signal processing

– Tensor networks

**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