Andreas Freund, Developer in Seattle, WA, United States
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Andreas Freund

Verified Expert  in Engineering

Data Scientist and Developer

Location
Seattle, WA, United States
Toptal Member Since
October 5, 2022

Andreas holds a PhD in applied math and has shown through his research and work experience a passion for using scientific computing to drive data-informed, impactful results. He wrote a thesis in the field of computational fluid dynamics and published two papers in high-impact journals as the first author. Most recently, Andreas has analyzed remote diagnostic data in the trucking industry to improve the performance of diesel engines.

Portfolio

PACCAR
Microsoft Excel, Tableau, SQL, Snowflake, Amazon Web Services (AWS)...
Mathematical Sciences Publishers
Linux, LaTeX, Writing & Editing, Shell Scripting, Vim Text Editor, SSH
University of Washington
Linux, Shell Scripting, Server Management, SSH, SQL, Java, Technical Support...

Experience

Availability

Part-time

Preferred Environment

Linux, Git, Python, Vim Text Editor, Jupyter Notebook

The most amazing...

...project I've worked on was my PhD thesis, in which I used math, physics, data, and programming to develop a new way to simulate droplets in turbulence.

Work Experience

Senior Data Analyst

2021 - PRESENT
PACCAR
  • Developed over ten Tableau dashboards connected to remote diagnostic data, used by engineers throughout the organization to identify problem trucks and overall truck performance trends.
  • Created a model that forecasts KPI and how it is affected by potential investments into the business. The model is used by management to inform significant budget decisions.
  • Automated complex data-aggregation tasks in Snowflake SQL.
Technologies: Microsoft Excel, Tableau, SQL, Snowflake, Amazon Web Services (AWS), Data Science, Data Analysis, Data Visualization, Python, Git, Data Engineering, Artificial Intelligence (AI), Forecasting, Jupyter Notebook, Machine Learning, NumPy, Linux

Production Editor

2012 - 2021
Mathematical Sciences Publishers
  • Edited papers for grammar, readability, and consistency and typeset them in LaTeX to the highest typographical standards to prepare for publication in top math journals.
  • Developed processes and tools to improve editors' efficiency, such as Vim macros and a shell script to automatically populate missing bibliographical data using the Crossref API.
  • Interfaced directly with authors to ensure they were satisfied with the formatting of their papers and worked with them to implement requested changes and corrections.
Technologies: Linux, LaTeX, Writing & Editing, Shell Scripting, Vim Text Editor, SSH

Systems Administrator

2016 - 2020
University of Washington
  • Maintained seven high-performance Linux servers used by graduate students and faculty in the applied mathematics department to perform cutting-edge research in climate modeling, mathematical finance, and mathematical biology.
  • Provided training to the department's students on various scientific computing topics, such as shell scripting, SSH and the Linux command line, common Python packages, and maintaining an academic website.
  • Oversaw the transition to a new auto-grading system used by instructors to evaluate Python code submissions for classes of over 300 students.
Technologies: Linux, Shell Scripting, Server Management, SSH, SQL, Java, Technical Support, Technical Training, Python, HTML, Docker, High-performance Computing (HPC), Scientific Computing, MATLAB, Git, Amazon Web Services (AWS)

Graduate Research Assistant

2015 - 2020
University of Washington
  • Added machine-learning capabilities to legacy Fortran/C++ code to speed up fluid simulations by a factor of 16.
  • Post-processed large fluid-simulation datasets on the order of 100 GB, using Python to visualize important fluid phenomena and perform analysis to make new scientific discoveries.
  • Assisted in teaching a graduate-level course on computational fluid mechanics, including designing and grading exams and homework assignments, holding office hours, and giving lectures on using ANSYS Fluent.
Technologies: C++, Python, Artificial Intelligence (AI), HDF5, Research, Big Data, Computational Fluid Dynamics (CFD), University Teaching, ANSYS, High-performance Computing (HPC), Numerical Methods, Scientific Computing, Fortran, Keras, SciPy, NumPy, Subversion (SVN), Linux, Server Management, LaTeX, Writing & Editing, Vim Text Editor, Data Science, Data Analysis, Data Visualization, Machine Learning, Parallel Programming, MPI, Neural Networks, Deep Learning

Large-eddy Simulation of Droplet-laden Isotropic Turbulence Using Neural Networks

https://doi.org/10.1016/j.ijmultiphaseflow.2021.103704
A method I created to decrease the computational cost of performing certain fluid simulations. When performing these simulations on a coarser computational grid, additional terms must be modeled to account for the loss in resolution. This is a well-studied problem in the turbulence of a single fluid but is less understood when simulating droplets. I developed a machine-learning model of these additional terms that appear at the droplet surface using neural networks in Keras. I then augmented an existing Fortran flow solver to allow it to use this model by linking it to the C++ TensorFlow libraries. This research became the second half of my PhD thesis and was published in a high-impact journal.

Wavelet-spectral Analysis of Droplet-laden Isotropic Turbulence

https://doi.org/10.1017/jfm.2019.515
A method I created to better analyze how kinetic energy is stored at and transferred between different physical scales in turbulence-containing droplets. This involved processing and analyzing hundreds of gigabytes of data from numerical simulations. This method uncovered novel physical phenomena that were used to improve simulations of such flows in a later project. This research became the first half of my PhD thesis and was published in a high-impact journal.
2014 - 2020

PhD in Applied Mathematics

University of Washington - Seattle, Washington, USA

2014 - 2016

Master's Degree in Applied Mathematics

University of Washington - Seattle, Washington, United States

2010 - 2014

Bachelor's Degree in Applied Mathematics

University of California, Berkeley - Berkeley, California, United States

Libraries/APIs

NumPy, Keras, SciPy, MPI, HDF5

Tools

MATLAB, LaTeX, Vim Text Editor, Tableau, Git, Subversion (SVN), Microsoft Excel

Languages

Python, SQL, Snowflake, Fortran, C++, Java, HTML

Paradigms

High-performance Computing (HPC), Data Science, Parallel Programming

Platforms

Linux, Amazon Web Services (AWS), Jupyter Notebook, Docker

Other

Research, Computational Fluid Dynamics (CFD), Numerical Methods, Scientific Computing, Numerical Analysis, Linear Algebra, Writing & Editing, Data Analysis, Data Visualization, Machine Learning, Big Data, Shell Scripting, Server Management, SSH, Technical Support, Technical Training, Neural Networks, Artificial Intelligence (AI), Forecasting, Deep Learning, Big Data Architecture, University Teaching, Data Engineering, ANSYS

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