Key skills: Big Data Analysis, Machine Learning, Software Development

Work experience

Researcher – PhD Candidate 11.2014 – 08.2019
Technische Universität Dortmund
␣ Big Data Analysis at the LHCb Experiment at CERN.
␣ Employ machine learning to extract tiny signals hidden under petabytes of noise.
␣ Statistical modelling of measured distributions.
␣ Extensive use of high-performance and grid computing.
␣ Technical documentation in the form of peer reviewed papers and internal analysis documentation.
␣ Long term stays at CERN (October to December 2015 and October 2016 to March 2017).
␣ Supervision of Bachelor- and Master-students: Definition of projects, every day mentoring and reviewing theses.
␣ Regularly lecturing about Python, C++, remote computing and several physics courses.
␣ Frequently presenting in national and international meetings, workshops and conferences.
␣ Administration of several high performance Linux servers with about 500 processor cores and 3 GPUs.

Education
PhD (Dr. rer. nat.) in experimental particle physics 11.2014 – 08.2019
Technische Universität Dortmund
␣ Thesis: Probing the Standard Model of Particle Physics with rare B decays.
␣ Machine learning, in particular binary classification.
␣ Active member of an international collaboration of more than 1000 scientists.
␣ Together with 3 data scientists from Yandex Moscow performing the first particle physics measurement employing
the company’s proprietary machine learning algorithm ”MatrixNet” published in JHEP 03 (2017) 001.

Master of Science in Physics 10.2013 – 10.2014
Technische Universität Dortmund, Focus: Particle Physics and Data analysis
␣ Thesis: Analysis of the Rare Decay B+ → π
+µ
+µ
−.
␣ Precision measurement of the at that time rarest discovered decay of a B particle.
␣ Results published in JHEP 1510 (2015) 034.

Bachelor of Science in Physics 10.2009 – 10.2013
Westfälische Wilhelms-Universität Münster / Technische Universität Dortmund
␣ Bachelor thesis on implementation and evaluation of Goodness-of-Fit-Tests for extended maximum likelihood
fits in the context of data analysis at the LHCb-Experiment.

Allgemeine Hochschulreife 06.1999 – 06.2008
Gymnasium Georgianum Vreden
␣ Examination subjects: Math, Physics, Social Sciences and English.

Skills
Languages
␣ German: Mother tongue.
␣ English: Fluent, working language for meetings, conferences and publications.
␣ Italian: B1 level (CEFR) through university courses.

Programming and Scripting Languages
␣ Python:
␣ C++:
␣ Unix Shell:
␣ SQL (Postgres):

Data analysis (since 2013)
␣ Training, optimisation and application of machine learning algorithms with frameworks such as scikitlearn, SciPy, XGBoost, LightGBM and Keras.
␣ Efficient data handling and manipulation with Pandas and NumPy.
␣ Statistical modelling of distributions with RooFit.
␣ Data visualisation with Matplotlib, Seaborn and ROOT.
␣ Hypothesis testing to estimate model quality or set limits on decay probabilities that are too small for
the experimental sensitivity.
␣ Collaborative work and unit testing using version control systems such as git.

DevOps (since 2015) 
␣ Development, testing and deployment of containerised data analysis environments with Singularity
through Gitlab CI/CD.
␣ Set up and administration of a small cloud running Rancher to deploy services such as Gitlab and
NextCloud in Docker containers.
␣ In a small team of PhD students: Acquisition, set up and maintenance of servers with in total about
500 CPU cores and 3 GPUs.
␣ Use ansible to comfortably maintain software and configuration of many independent servers in an
easily reproducible and consistent way.

Presentations at Conferences
␣ First Measurement of the Differential Branching Fraction and CP Asymmetry of the B+
Decay. DPF 2015, Ann Arbor, Michigan, USA
␣ Search for very rare decays of beauty mesons into four muons, LHC Days 2016, Split, Croatia