Lukas Scheucher

• As the main engineer, I built our backtesting tool, visualization dashboard as well as our backend on GCP.
• Active engagement with customers, resolving their issues and improving documentations.
• I have had the opportunity to actively help shape the direction of the company.

• Working as a freelance engineer on projects focusing on data-science and backend engineering.
• Thanks to my broad background, I’ve been helping early-stage startups building up their MVPs, setting up their backend and CI/CD pipelines, improving their code, etc.
• Working with Toptal, A.team and other platforms.

• I was accepted into the 2022 cohort and spent 2 months working with dedicated individuals on blockchain/web3 ideas.
• Unfortunately, I did not find the right co-founder/idea.

• Using data analysis on production logs to improve reliability across Google. Used tensorflow, Go and Apache flume
• Came up with an lead an successful internal project combining data analysis and visualization.
• Google X “the moonshot factory”: Data analysis on an experimental wearable device. Owned whole Python codebase and training pipeline.

• Worked as an applied machine learning engineer, mainly on computer vision for autonomous driving.
• Real time object detection, Model development, training, selection, compression and testing.

• Uncertainty quantification in physical simulations using bayesian methods and machine learning.
• Design optimization under uncertainty.
• Collaborative development of a C++ research code (Full CI/CD pipeline).
• Visualization of complex simulation output using Paraview, Plotly, D3.js, …
• Held several positions as teaching assistant.

• Implemented gradient computation in a C++ fluid dynamics code
• Application: Parametric shape optimization of flexible wings.
• Audited Stanford lecture series on machine learning by Andrew Ng.

Uncertainty quantification in physical simulations using bayesian methods and machine learning

Design optimization. Collaborative development of a C++ HPC codebase.

Majored in Computational Engineering and High Performance Computing.

Visiting Researcher at Stanford University

Overall Grade 1.6. Final theses 1.0.

Majored in Mechanical Engineering

Overall Grade 1.4. Final theses 1.0.

• Total of 16 professional certifications including
  • Blockchain Developer Nanodegree - Udacity
  • 5-part deep learning specialization - Deeplearning.ai
  • Decentralized finance - Duke University
  • Full list on LinkedIn