Jan Sosulski
Education
Doctorate
PhD in Computer Science (Dr. rer. nat.), University of Freiburg.
In
my dissertation Rapidly
Adapting Machine Learning
Methods for Brain-computer
Interfaces I demonstrated how leveraging properties of neurological data increases the sample
efficiency of machine learning algorithms for EEG-based brain-computer interfaces (BCIs).
This reduced the amount of required training data and even enabled a new unsupervised approach, eliminating
the need for a calibration phase for the BCI user. Grade: summa cum laude.
2017 - 2023
Master
M.Sc. Computer Science, University of Freiburg.
Thesis: Online Stimulus Parameter Optimization during BCI Experiments, specialization area: cognitive
technical systems. Final grade: 1.3.
2014 - 2017
Bachelor
B.Sc. Information Systems, Duale Hochschule Baden-Wuerttemberg (DHBW) Mannheim.
Thesis: Exception Handling in Integration Systems based on BPMN, specialization area: software engineering.
Final grade: 1.6.
2011 - 2014
Professional experience
Kenbun IT AG
AI Engineer / Consultant
I trained, deployed and analyzed robust speech recognition models in various domains. Notably in very noisy
settings, where little training data is available and with specialized vocabulary, e.g., medical, maintenance
and air traffic communication. I also developed reference implementations to support deployment.
Most used technologies for training and analyses: Python, PyTorch (Lightning), NVIDIA NeMo and for deployment:
Rust, ONNX, Kubernetes.
2022 - present
University of Freiburg
Research Assistant
Work included the development of novel sample efficient machine learning methods for EEG data and their
implementation in Python and Matlab, supervision of one Bachelor and five Master Theses, and conducting EEG
experiments in collaboration with the University Medical Center Freiburg.
Most used technologies: MATLAB
and Python (esp. sklearn, numpy, mne).
2017 - 2022
SAP SE (Germany and Canada)
Dual Student
Implementation of storage planning and middleware algorithms.
Most used technologies: ABAP, JavaScript, Java.
2011 - 2014
Awards
Dissertation
For my dissertation I received the "Förderpreis Künstliche Intelligenz", which is awarded by
the "Neue Universitätsstiftung Freiburg" for outstanding theses in the field of artificial intelligence.
2024
Best poster
At the "8th Graz BCI Conference" I received the best poster award for my paper "Spatial
filters for auditory evoked potentials transfer between different experimental conditions".
2019
Skills
Python
Mostly used in the context of traditional machine learning (numpy, sklearn) and deep
learning
(PyTorch).
10 years
Rust
In a professional context used for neural network deployment/inference, signal preprocessing
and graph algorithms. In a hobby context used for small tools, embedded projects and a bread baking helper
program.
6 years
MATLAB
Used for EEG signal processing, live BCI/EEG experiments and machine learning.
3 years
R
Primarily used to devise exercises for information systems students.
2 years
JavaScript
Mostly used as a hobby for servers/daemons that do something with my sports data or as a
backend/UI to show some sensor readings.
2 years
C++
Really only used for some image processing exercises and in the context of deep learning
with the Caffe framework.
1 year
Other
Linux, git, fish, bash, ... and the usual stack of tools.
15 years
Hobbies
Riding some kind of bike
Started with mountain bikes, was too scared for actual trails, switched to road biking, was
too annoyed by all the cars, settled on gravel biking (for now...).
Running
External observers might think I'm on a leisurely stroll, but I really am trying...
Baking bread
Yes, I am one of those guys that had to start baking sourdough bread during COVID. In any
case, I like multi-step recipes and wrote some planning software so I don't have to get up in the middle of the
night just because my dough needs me.
Dancing
Mostly ballroom and latin.
Embedded development
Don't really care what it is, as long as it somehow measures something or otherwise
interacts with the physical world.