Felix Brunner
Machine status detection in industrial 3D printing based on infrared image data
Experience
Oct 2021 - Sep 2023
2 yearsMachine status detection in industrial 3D printing based on infrared image data
- Baseline: Client has infrared cameras installed inside production machines for manual monitoring.
- Goal: Automate the process monitoring to identify irregularities from live images.
- Activities: I contributed to this project as a machine learning developer and project coordinator:
- Guided systematic data collection and pre-processing for the machine learning algorithms.
- Defined the labeling process and implemented an interface to annotate the datasets.
- Programmed a visual deep learning algorithm to detect machine pollution in live production.
- Implemented data augmentation techniques to deal with machine heterogeneity.
- Supply a containerized model with API endpoints for deployment to the production machines.
- Coordinated and represented a five-person project team, prepared presentations and reports.
- Outcome: Provided a production-ready system to inform operators of potential faults in real time.
- Stack:
- PyTorch
- Convolutional neural networks
- docker
- git
- OpenCV
- FastAPI
- computer vision
- pytorch-lightning
- ipywidgets
Oct 2021 - Sep 2023
2 yearsQuality prediction based on time series data in a manufacturing context
- Baseline: Multitude of sensors collects time series data from production machines at high frequency.
- Goal: Identify patterns in the data linked to final product quality.
- Activities: I worked in this project as a machine learning developer and project coordinator:
- Analyzed and visualized large amounts of time series data.
- Explored, filtered and connected various data sources to construct consistent datasets.
- Conceptualized the data pipelines and modeling approaches.
- Implemented a machine learning system for quality prediction in a manufacturing context.
- Tested various predictive algorithms including statistical models and deep neural networks.
- Outcome: Derived qualitative insights to guide process engineers in optimizing production.
- Stack:
- PyTorch
- CNNs / ROCKET model
- data wrangling
- git
- time series classification
- explainable AI
May 2021 - Sep 2023
2 years 5 monthsBerlin, Germany
Machine Learning Scientist
dida Machine Learning
- Conducted deep learning research projects in computer vision and time series.
- Implemented commercial machine learning software in a manufacturing context.
- Developed data processing pipelines for the deployment into live production.
- Conceptualized application of large language models for question answering.
- Represented and organized a five-person project team.
- Prepared analyses and visualizations of large amounts of unstructured data.
May 2021 - Aug 2023
2 years 4 monthsAutomated question answering via retrieval of internal documents
- Baseline: Company employees manually search internal documentation for information.
- Goal: Integrate chatbot for question answering (QA) into company communication platform.
- Activities: I supported this project as a machine learning scientist:
- Created a demo for extractive QA based on a provided document.
- Developed proof-of-concept for automatic question answering (QA) with semantic search.
- Expanded processing pipeline to larger quantities of internal documents via automatic document retrieval and generative QA with large language models (LLMs).
- Informed the developers about recent advancements in natural language processing (NLP).
- Outcome: Made responsive chatbot for document-based extractive QA available to all employees.
- Stack:
- transformers
- haystack
- LLMs
- document retrieval
- semantic search
- question answering
- BERT
- git
- beautifulsoup4
May 2020 - Dec 2023
3 years 8 monthsLisbon, Portugal
Estimation and analysis of variance spillover networks for academic research
- Baseline: Existing network estimation methodology only works reliably on small datasets.
- Goal: Apply machine learning methods to analyze large datasets of economic and financial data.
- Activities: I realized this project as an econometrics researcher:
- Explored and compared statistical learning algorithms for multivariate forecasting.
- Acquired datasets from SQL databases and set up an automated pre-processing pipeline.
- Wrote object-oriented code to run cross-validated regularized machine learning algorithms.
- Conducted extensive statistical and econometric analyses to empirically analyze the results.
- Authored research papers that present the results at academic standard.
- Outcome: Published acclaimed paper in Quantitative Economics, follow-up papers under review.
- Stack:
- pandas / numpy
- time series forecasting
- scikit-learn
- python
- SQL
- glmnet
- econometrics
- vector auto-regression
- networkx
Sep 2017 - Feb 2022
4 years 6 monthsLisbon, Portugal
Teaching Assistant
Universidade Nova de Lisboa
- Supported teaching and grading of Master‘s courses in financial data analysis.
- Generated course materials, assignments and exams.
Jul 2015 - Nov 2015
5 monthsMunich, Germany
Quantitative Strategies Intern
risklab / Allianz Global Investors
- Provided data-driven research on fund performance and portfolio optimization.
- Prototyped tools for investment strategies in Matlab and VBA for Excel.
- Modeled time series with regularized regressions and scenario simulations.
Mar 2014 - Aug 2014
6 monthsMünster, Germany
Project Management Intern
zeb Consulting
- Prepared senior management presentations to guide an IT project in risk management.
Summary
Machine learning specialist with experience in commercial and academic projects. Main expertise lays in implementing AI projects in computer vision, time series, and NLP with the help of deep learning methods.
Languages
German
NativeEnglish
AdvancedFrench
ElementaryPortuguese
Elementary Education
Sep 2016 - Apr 2024
Nova SBE
PhD · Econometrics · Lisbon, Portugal
Sep 2014 - May 2016
Nova SBE
MSc · Finance · Lisbon, Portugal
Oct 2010 - May 2014
WU Vienna
BSc · International Business Administration · Vienna, Austria
Certifications & licenses
Data Science Bootcamp
Lisbon Data Science Academy
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