Lucas Sas Brunschier
lucassas@live.de
+49-151-46665909
Freising, Germany
About me
My name is Lucas Sas Brunschier and I am a Computer Science master’s student at the HAW-Landshut university in Germany.
I’m primarily interested in machine learning (especially artificial neural networks) and the systems programming language Rust.
I am currently writing my master’s thesis on video surveillance anomaly detection at EFS TechHub GmbH.
I love to explore new technologies and while doing so learn to use them.
That’s what makes it easy for me to adapt to new environments or challenges.
Primary Skillset
- 🐍 Python (TensorFlow, Torch, Pandas, NumPy, OpenCV, …)
- 🦀 Rust
- 👾 Linux
- 💀 C++
- 💾 (Non-) Relational Databases
- CI/CD (GitLab)
- AWS (especially ML utilities)
- ⚛️ React (JS)
- 📱 iOS/Android Development
SAE Institute Munich
Bachelor of Arts in Interactive Animations
HAW Landshut
Bachelor of Science in Computer Science (Informatik)
HAW Landshut
Masters Degree in Computer Science (Informatik)
Working Student at the International Office (HAW-Landshut)
System Migration with Python
I helped the International Office at HAW-Landshut to automate parts of the existing infrastructure
into a new system used for processing applicants using Python.
There I was specifically responsible for automatically extracting and migrating data out of PDFs
into a new Database.
Internship at Siemens
Mandatory Internship
At my mandatory full-time internship at Siemens in Munich Perlach, I worked on:
- Performance optimization of artificial neuronal networks for embedded devices
- Extending an already existing Angular Front-End
- Machine learning specific AWS cloud services (SageMaker)
- AWS infrastructure
Working Student (Bachelor/Master) at Siemens
Machine Learning on Embedded Devices
Working on artificial neuronal network optimization and MLOps workflows with Kubeflow and GitLab CI. I took part in the IoT@Siemens Conference 2022 in Nürnberg as a speaker.
Working Student at e:fs TechHub
Collaboration with EFS for my Master's Thesis
I wrote my Master’s Thesis on video anomaly detection on the vehicular surveillance video domain at e:fs TechHub in the context of the SAVeNoW research project.
IoT@Siemens2022 Conference Speaker
Improving the ML-Ops lifecycle with EdgeFunnel powered by GitLab CI.
KI Fabrigk Conference Speaker
Talk on anomaly detection techniques applied to traffic surveillance cameras.
Exhibitor at Smart City Expo World Congress Barcelona 2022
I represented the SAVeNoW research project as part of a team at the Bayern Innovativ booth at SCEWC Congress in Barcelona.
Hacking of Industrial Embedded Devices
Student Project at Siemens
The objective of this student project was to penetration test Siemens SICAM devices. We were successful in finding multiple severe vulnerabilities in the target system. As a result, Siemens published an advisory of our findings.
Feedback App for High Tech Industrial Machines
Student Project at GROB
Web & mobile app using Onsen UI and Spring Java backend. I was especially responsible for:
- Docker infrastructure
- Web App
- Development environment (CI/CD)
BeeXXcellent - Predicting the Behavior of Bees based on Air Quality
Student Project at eXXcellent Solutions
The vision of the project was to use the open luftdaten.info (now known as sensor.community) API to track air quality data (temperature, air pollution, humidity, …) over a period of time. This data can then be used to train a machine learning model that should infer bee behavior based on data from bee hives equipped with multiple sensor types.
In this project I was especially responsible for:
- Data processing with Python (and mostly pandas ❤️)
- Setting up a recurrent artificial neuronal network using Python and PyTorch
- Infrastructure and Deployment using Kubernetes
- High performance time series database management (TimescaleDB)
- Development environment (GitLab CI/CD)
Security Analysis of Exposure Tracking Apps
Mandatory two Semester Student Project