Dr. Thomas Camminady's CV

• Email: job@camminady.dev
• Location: Bonn, Germany
• LinkedIn: camminady
• GitHub: thomascamminady

Professional Summary

Accomplished algorithm developer and applied mathematician with a Ph.D. in Applied Mathematics, specializing in sensor fusion, data analysis, visualization, and machine learning. Proven track record in designing and deploying scalable machine learning solutions that drive user engagement and operational efficiency. Adept at translating complex research into production-ready code, excelling in collaborative, cross-functional environments.

Education

Karlsruhe Institute of Technology, Dr. rer. nat. in Applied Mathematics

• Oct 2017 – Jan 2021
• Karlsruhe, Germany
• Thesis: Theory, models, and numerical methods for classical and non-classical transport.

RWTH Aachen University, M.Sc. in Computational Engineering Science (CES)

• Oct 2013 – Mar 2015
• Aachen, Germany
• Selected Courses: Parallel Computing, Computational Differentiation, Uncertainty Quantification, Combinatorial Problems in Scientific Computing.
• Thesis: Theory and application of numerical methods for fractional diffusion equations.

RWTH Aachen University, B.Sc. in CES

• Oct 2009 – Sept 2013
• Aachen, Germany
• Selected Courses: Computational Fluid Dynamics, Aerodynamics, Calculus of Variations, Partial Differential Equations.
• Thesis: Improvement of the aerodynamic shape optimization by adjoint methods in an MDO process.

Experience

Wahoo Fitness LLC, Data Scientist & Algorithm Developer

• Mar 2021 – present
• Remote from Germany
• Launched a machine learning–driven training recommendation engine for athletes, enhancing personalized workout strategies.
• Developed robust backend APIs using Python and Pydantic, serving thousands of users via AWS.
• Collaborated with cloud engineers and frontend developers to integrate machine learning models into production systems.
• Applied time series analysis, sensor fusion, uncertainty quantification, and machine learning for rapid prototyping and scalable solutions.
• Analyzed and visualized large-scale, distributed user data using pandas, NumPy, Plotly, and SQL.
• Automated C-code generation for low-level hardware sensors (GNSS, barometer, gyroscope) using Matlab.
• Operated within an agile, fully remote team (US and EU) using Jira and Confluence.

Center for CES & Steinbuch Centre for Computing, Scientific Staff

• Apr 2015 – Mar 2021
• Aachen & Karlsruhe
• Conducted research in the field of kinetic theory, numerical mathematics, optimization, and machine learning.
• Pioneered reinforcement learning methods to optimize numerical algorithms in CFD, resulting in significant computational improvements.
• Optimized research software on KIT's HPC cluster via parameter studies using OpenMP.
• Served as teaching assistant and substitute lecturer for modules in the mathematics and CES programs.

Festival de Théorie, Summer School on Plasmas

• June 2017 – July 2017
• Aix-en-Provence, France
• Implemented magnetic field derivatives into a Fortran DG-MHD research code.
• Actively participated in seminars and workshops in the field of plasmas.

Center for CES, Student Research Assistant

• Jan 2010 – Mar 2015
• Aachen, Germany
• Assisted with teaching duties for a variety of mathematics and computer science modules.
• Developed GPU-based Monte Carlo simulations using CUDA.

EADS Cassidian, Internship with Bachelor's Thesis

• Oct 2012 – Apr 2013
• Manching, Germany
• Performed numerical simulations with in-house tools and the adjoint code of the German Aerospace Center (DLR).
• Automated UAV airfoil shape optimization using mesh adjoints.

Projects

• LeTourDataSet

• alltime athletics python

• traffic-balve

• dicopal

Scientific Outreach

Collaborator in the Computational and Mathematical Modeling Program (KIT University). Developed educational programs that demonstrate the importance of mathematical modeling and machine learning for real-world applications to high-school and entry-level university students. Authored publications in mathematical didactics aimed at integrating mathematical modeling into the German Abitur curriculum.

Skills

• Python: 8 years of experience: Expert in object-oriented and type-annotated Python, employing advanced design paradigms for robust development and testing. Experience with NumPy, SciPy, pandas, matplotlib, and scikit-learn.
• MATLAB: 6 years of experience: Development of scientific simulation tools and visualizations. Includes working with MuPAD, Simulink, and Optimization Toolbox.
• Data Visualization: 10+ years of experience: Expert in creating interactive data visualizations and dashboards using Altair, Plotly, Matplotlib, D3.js, and Observable Plot to present complex data in an accessible way. This includes publication-ready visualizations, interactive visualizations for explorative analysis, and development of deployable dashboards.
• Software Development: 10+ years of experience: Version control (Git, Github), CI/CD, modern testing frameworks, proficiency with UNIX systems, AWS (Lambda), Jira, and Confluence.

Publications

Mathematische Grundlagen der Künstlichen Intelligenz im Schulunterricht

• Sarah Schönbrodt, Thomas Camminady, Martin Frank
• Mathematische Semesterberichte 69 (1), 73-101, (2022)

Theory, models, and numerical methods for classical and non-classical transport

• Thomas Camminady
• Dissertation (2021)

Ray Effect Mitigation for the Discrete Ordinates Method Using Artificial Scattering

• Martin Frank, Jonas Kusch, Thomas Camminady, Cory D. Hauck
• Nuclear Science and Engineering, Vol. 194, No. 11, pp. 971–988 (2020)

Vorschlag für eine Abiturprüfungsaufgabe mit authentischem und relevantem Realitätsbezug

• Sube, Maike, Thomas Camminady, Martin Frank, Roeckerath, Christina
• Modellierungskompetenzen – Diagnose und Bewertung, Springer Berlin Heidelberg, pp. 153–187 (2020)

Ray effect mitigation for the discrete ordinates method through quadrature rotation

• Thomas Camminady, Martin Frank, Kerstin Küpper, Jonas Kusch
• Journal of Computational Physics, Vol. 382, pp. 105–123 (2019)

Highly uniform quadrature sets for the discrete ordinates method

• Thomas Camminady, Martin Frank, Jonas Kusch
• Proceedings of the International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, pp. 25–29, (2019)

A spectral Galerkin method for the fractional order diffusion and wave equation

• Thomas Camminady, Martin Frank
• International Journal of Advances in Engineering Sciences and Applied Mathematics, Vol. 10, No. 1, pp. 90–104 (2018)

A new high-order fluid solver for tokamak edge plasma transport simulations based on a magnetic-field independent discretization

• Giorgiani, G., Thomas Camminady, Bufferand, H., Ciraolo, G., Ghendrih, P., Guillard, H., Heumann, H., Nkonga, B., Schwander, F., Serre, E., Tamain, P.
• Contributions to Plasma Physics, Vol. 58, Nos. 6–8, pp. 688–695 (2018)

Nonclassical particle transport in heterogeneous materials

• Thomas Camminady, Martin Frank, Edward W. Larsen
• Proceedings of the International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering (2017)

The equivalence of forward and backward nonclassical particle transport theories

• Edward W. Larsen, Martin Frank, Thomas Camminady
• Proceedings of the International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering (2017)

Theory and application of numerical methods for fractional diffusion equations

• Thomas Camminady
• Master's Thesis (2015)

Improvement of the aerodynamic shape optimization by adjoint methods in an MDO process

• Thomas Camminady
• Bachelor's Thesis (2013)