REFERENCES

COMPUTER VISION (CV)

Industry: Hi-tech - company size: <200

Project content:

Entwicklung effizienter Bildverarbeitungsalgorithmen einschließlich Implementierung, Testen und Dokumentation, Vollständige Portierung des internen Buildsystems von Make nach CMake, Weiterentwicklung und Pflege bestehender Algorithmen, insbesondere Optimierung und Parallelisierung, Cross-Plattform Entwicklung für Windows, macOS, Linux sowie Embedded-Geräte.

Technologies & methods:

C, Doxygen, Bildverarbeitung, HALCON, Qt, Python, Caffe, Make, CMake, Jenkins, Perl, OpenMP, OpenCL, SIMD, SSE, AVX

Industry: IT - company size: <100

Project content:

Forschung und Entwicklung eines Verfahrens zur fälschungssicheren, eindeutigen Erkennung von Krakelee-Mustern auf Basis von Bildmerkmalen und Oberflächenstrukturen.

Technologies & methods:

C++, Python, Boost CMake Qt OpenGL VTK OpenCV Doxygen FlyCapture libdc1394 OpenMP

Industry: Hi-tech - company size: <200

Project content:

Development and optimization of computer vision algorithms for machine vision. Mainly in the areas deep learning, identification (Bar-/Datacode), machine learning, and core image processing functionalities (Filters, Thresholds, Segmentation, Computational Geometry, etc.). Rewrite of the 2D visualization. 3rd-Level customer support for a wide range of complex 3D and 2D computer vision and machine learning problems.

Design and improvements of the build, test and CI infrastructure. Refactoring, modularization and redesign of a large legacy C codebase. Cross-platform development for Windows, Linux, macOS, and embedded devices. Porting the machine vision library to Android.

Technologies & methods:

C, cuDNN, cuBLAS, Intel MKL, Skia, C++, CMake, Jenkins, Scrum, Kanban

Industry: Automotive - company size: <4.500

Project content:

Design, development, and implementation of a real-time traffic sign recognition system for an Android/iOS app (ACoDriver).

Technologies & methods:

OpenCV, Java, C/C++ Neural Networks, Local binary patterns, Boosting

Industry: Automotive - company size: <10.000

Project content:

Improving lane detection and lane-departure warning system for ADAS. Training and tuning the algorithms to work with hard road scenarios. Team achieved >96% availability (92% required).

Technologies & methods:

image processing, image features, pattern recognition, object detection and tracking, OpenCV, Python, C++, Tensorflow, Keras, CNN, SVM

Industry: Start-Up - company size: <50

Project content:

Development of a mobile application for video life blogging with VR and AR elements. Face/body detection and tracking up to 4m with smart-phone camera. Image stitching to panorama from video frames.

Technologies & methods:

image processing, image features, pattern recognition, object detection and tracking, decision trees, OpenCV, Python, C++, Matlab, Tensorflow, Keras, DNN, CNN

Industry: Mechanical engineering - company size: <3.500

Project content:

Industrielle Bildverarbeitung, Prototypenentwicklung und Algorithmenentwicklung im Bereich Industrielle Bildverarbeitung – automatische Konturenerkennung, Implementierung einer semi-automatischen Korrekturfunktion.

Technologies & methods:

C++, OpenCV, Konturenerkennung, Graph Cut

Industry: Start-up - company size: <35

Project content:

Algorithmenentwicklung zur Gesichtserkennung: Bildvorverarbeitung, Gesichtsdetektion, Analyse der Textureigenschaften, Identifizierung, Verifizierung; Optimierung: – Parameteranpassung mit numerischen Methoden, Normalisierung ungleichmäßiger Beleuchtung an Gesichtsbildern.

Technologies & methods:

C++, OpenCV, MATLAB, Max-Flow Min-Cut, Graphentheorie, Extraktion von grob- und fein-skalierten Merkmalen, Segmentierung, Principal Component Analysis (PCA), Logistic Regression, Linear Regression, Gradient Descent

Industry: Automotive - company size: <10.000

Project content:

ADAS – Algorithmenentwicklung und Codeoptimierung im Bereich Fahrerassistenzsysteme / Laserscanner. Prototypenentwicklung und Algorithmenentwicklung im Bereich Fahrerassistenzsysteme – Kamerakalibrierung, Fahrspurerkennung, Implementierung von real time PC-basierten Prototypen im Fahrzeug.

Technologies & methods:

C++, MATLAB, Python, Jupyter Notebooks, Keras, Tensorflow, Convolutional Networks,Bayesian Networks, Tracking, Klassifizierung

Industry: Med-tech - company size: <10.000

Project content:

3D Reconstruction from Multiple views in Matlab, C++.

Technologies & methods:

Matlab, C++, OpenCV, OpenMP

Industry: Marketing - company size: <300

Project content:

Building a prescriptive model for advertisement text (with revenue uplift 7%) – conducted feasibility study for user recommendations based on recurrent neural networks.

Technologies & methods:

Python, TensorFlow, RNN, Machine & Deep Learning

Industry: Marketing - company size: <300

Project content:

Text Mining: implementation of a new, scalable item based collaborative filtering algorithm for 150 million events per day.

Increase in click through rate by 10% compared to previous implementation – constructed real time streaming pipeline to transform newspaper text into vector format for about 20 texts per second being transformed, inserted and updated each day, implemented a fully automated model retraining pipeline.

Technologies & methods:

Python, Spark, TensorFlow, Machine & Deep Learning, Big Data

Industry: Banking - company size: <1.200

Project content:

Design and implement a Data Science Stack for Text Mining.

Technologies & methods:

Regression, Classification and Apache Spark, Recurrent Neural Networks, Keras, Hadoop

Industry: Media - company size: <5.500

Project content:

Topic modeling: Use LDA-Algorithm to model the topics of German news
Doc2Vec: Use the topic model to transform each document to a topic vector
Similar documents detection: Use the topic model to transform the corpus to a topic matrix and search similar documents using the corpus-topic matrix
Keyword identification: Build a keyword extractor to identify keywords from German news

Technologies & methods:

Python, Gitlab, Jmeter, SonarQube, Docker, Rancher

Industry: E-Commerce - company size: <1.000

Project content:

Analyze the text of product names, descriptions, etc. to improve search quality, develop novel POS and entity tagger and build a prediction model of the product categories by training a feedforward neural network.

Technologies & methods:

Python, NLTK, Sklearn, machine learning, Neural Networks

Industry: Insurance - company size: <1.200

Project content:

Web-mining & Text-analytics: crawl websites, apply text analytics techniques to extract information.
Natural language processing for German: POS-tagging and stemming based on statistical inference; topic and sentiment analysis of the news.

Technologies & methods:

R, R-Shiny, IBM Watson

Industry: Retail - company size: <4.000

Project content:

Development of reliable, robust and scalable machine learning systems and platforms, created 360° customer insights data platform that feeds data into several downstream systems including a DMP for online advertising, designed a privacy-preserving data science platform for marketing departments of 8 group companies to comply with changes in privacy laws and managed its implementation, developed solutions to create a common latent feature representation from heterogeneous internal and external data sources using novel deep learning techniques. Conducted internal “business development” to promote group wide use of services and data pools.

Technologies & methods:

Python, R, Hadoop, Spark, Pandas, Scikit-Learn, Keras, TensorFlow, PyMC3, R-Studio, PostgreSQL, MySQL, Vertica, Greenplum, Jupyter, Anaconda, Git, Jenkins

Industry: Gaming - company size: <1.000

Project content:

Full life-cycle data science project management and machine learning system development. Professionalized development processes in start-up environment and replaced failure-prone systems with reliable and maintainable solutions, redesigned a marketing attribution system and increased accuracy through new predictive models and by connecting external data sources, helped to save significant affiliate marketing expenses by providing fraud management team with novel fraud detection algorithms, developed a robust and accurate model for customer lifetime value prediction that became part of the main Business.

Intelligence ETL pipeline and supported automated and manual decision making processes in several departments.

Technologies & methods:

Python, R, Hive, Pandas, Scikit-Learn, Keras, TensorFlow, PostgreSQL, MySQL

Industry: Pharma - company size: <250

Project content:

Quantitative quality assessment of the company’s next generation sequencing (NGS) laboratory for in vitro diagnostic (IVD) accreditation, utilizing machine learning for collaboration projects and product innovation (eg, cancer subtype classication), designing a new data model, including genomic variants and cancer type taxonomy, as basis for research and products, conceptualizing and performing statistical analysis of high-dimensional observational data (“big data”).

Technologies & methods:

R, Python, SQL, Matlab, Java, machine learning, text mining, statistical optimization

Industry: Telecommunication - company size: <9.000

Project content:

Geo-Location: Home location detection and occupational segments identification, Movement patterns for different segments, Visualization of geolocation and relevant information.

Technologies & methods:

Python (Pandas, Keras, nltk), Spark, R

Industry: Energy trading- company size: <750

Project content:

Modellierung von Finanzmarktrisiken

Technologies & methods:

Databricks on Azure, Spark Scala, Monte-Carlo-Simulation

Industry: Household appliances - company size: <2.500

Project content:

Aufbau einer Big-Data Analytics Abteilung durch Use Cases: Churn Analysis, B2B-Sales Optimization, Predictive Maintenance, Data quality.

Technologies & methods:

AWS, Spark, Scala, Sagemaker, TensorFlow, Keras, Python, RNNs

Industry: Electronic - company size: <2.400

Project content:

Design and development of a deep learning based sky/cloud classification system.

Technologies & methods:

Keras/Tensorflow, Python, AWS Cloud

Industry: Automotive - company size: <20.000

Project content:

Design and implement advanced CNN architectures and models based on a huge number of Images for Shop Floor Control. Evaluate Deep Learning Architectures, Performance Optimization of Deep Learning Models.

Technologies & methods:

Image segmentation, Image classification, Anomaly detection, Video Analysis, Tensorflow and Keras with GPU (NVIDIA), Hadoop, GPU, PyCharm

Industry: Retail - company size: <3.000

Project content:

Design, implement and optimize complex Machine Learning Systems for Online Marketing / eCommerce.

Technologies & methods:

Hive, Spark, Python, Deep Learning, K-Nearest Neighbors (KNN), SVM, Kernel SVM, Logistic Regression

Industry: Media - company size: <5.500

Project content:

Deep-Learning (NLP und Bild-/Videoverarbeitung)

Softwareentwicklung und Beratung für verschiedene Dienste einer Bild-Mining Software, Extractive Text Summarization / Image Captioning, (Human) Action Recognition, Object Detection.

Technologies & methods:

Python, TensorFlow, Kerasa, sk-learn, OpenCV, CNN

Industry: Hi-tech - company size: <200

Project content:

Entwicklung effizienter Bildverarbeitungsalgorithmen einschließlich Implementierung, Testen und Dokumentation, Vollständige Portierung des internen Buildsystems von Make nach CMake, Weiterentwicklung und Pflege bestehender Algorithmen, insbesondere Optimierung und

Parallelisierung, Cross-Plattform Entwicklung für Windows, macOS, Linux sowie Embedded-Geräte.

Technologies & methods:

C, Doxygen, Bildverarbeitung, HALCON, Qt, Python, Caffe, Make, CMake, Jenkins, Perl, OpenMP, OpenCL, SIMD, SSE, AVX

Industry: IT - company size: <100

Project content:

Forschung und Entwicklung eines Verfahrens zur fälschungssicheren, eindeutigen Erkennung von Krakelee-Mustern auf Basis von Bildmerkmalen und Oberflächenstrukturen.

Technologies & methods:

++, Python, Boost CMake Qt OpenGL VTK OpenCV Doxygen FlyCapture libdc1394 OpenMP

Industry: Hi-tech - company size: <200

Project content:

Development and optimization of computer vision algorithms for machine vision. Mainly in the areas deep learning, identification (Bar-/Datacode), machine learning, and core image processing functionalities (Filters, Thresholds, Segmentation, Computational Geometry, etc.). Rewrite of the 2D visualization. 3rd-Level customer support for a wide range of complex 3D and 2D computer vision and machine learning problems.

Design and improvements of the build, test and CI infrastructure. Refactoring, modularization and redesign of a large legacy C codebase. Cross-platform development for Windows, Linux, macOS, and embedded devices. Porting the machine vision library to Android.

Technologies & methods:

C, cuDNN, cuBLAS, Intel MKL, Skia, C++, CMake, Jenkins, Scrum, Kanban

Industry: Automotive - company size: <4.500

Project content:

Design, development, and implementation of a real-time traffic sign recognition system for an Android/iOS app (ACoDriver).

Technologies & methods:

OpenCV, Java, C/C++ Neural Networks, Local binary patterns, Boosting

Industry: Automotive - company size: <10.000

Project content:

Improving lane detection and lane-departure warning system for ADAS. Training and tuning the algorithms to work with hard road scenarios. Team achieved >96% availability (92% required).

Technologies & methods:

image processing, image features, pattern recognition, object detection and tracking, OpenCV, Python, C++, Tensorflow, Keras, CNN, SVM

Industry: Start-up - company size: <50

Project content:

Development of a mobile application for video life blogging with VR and AR elements. Face/body detection and tracking up to 4m with smart-phone camera. Image stitching to panorama from video frames.

Technologies & methods:

image processing, image features, pattern recognition, object detection and tracking, decision trees, OpenCV, Python, C++, Matlab, Tensorflow, Keras, DNN, CNN

Industry: Mechanical engineering - company size: <3.500

Project content:

Industrielle Bildverarbeitung, Prototypenentwicklung und Algorithmenentwicklung im Bereich Industrielle Bildverarbeitung – automatische Konturenerkennung, Implementierung einer semi-automatischen Korrekturfunktion.

Technologies & methods:

C++, OpenCV, Konturenerkennung, Graph Cut

Industry: Start-up - company size: <35

Project content:

Algorithmenentwicklung zur Gesichtserkennung: Bildvorverarbeitung, Gesichtsdetektion, Analyse der Textureigenschaften, Identifizierung, Verifizierung; Optimierung: – Parameteranpassung mit numerischen Methoden, Normalisierung ungleichmäßiger Beleuchtung an Gesichtsbildern

Technologies & methods:

C++, OpenCV, MATLAB, Max-Flow Min-Cut, Graphentheorie, Extraktion von grob- und fein-skalierten Merkmalen, Segmentierung, Principal Component Analysis (PCA), Logistic Regression, Linear Regression, Gradient Descent

Industry: Automotive - company size: <10.000

Project content:

ADAS – Algorithmenentwicklung und Codeoptimierung im Bereich Fahrerassistenzsysteme / Laserscanner. Prototypenentwicklung und Algorithmenentwicklung im Bereich Fahrerassistenzsysteme – Kamerakalibrierung, Fahrspurerkennung, Implementierung von real time PC-basierten Prototypen im Fahrzeug.

Technologies & methods:

C++, MATLAB, Python, Jupyter Notebooks, Keras, Tensorflow, Convolutional Networks,Bayesian Networks, Tracking, Klassifizierung

Industry: Med-tech - company size: <10.000

Project content:

3D Reconstruction from Multiple views in Matlab, C++

Technologies & methods:

Matlab, C++, OpenCV, OpenMP

Industry: Hi-tech - company size: <750

Project content:

Multi-View ORB-SLAM.

Technologies & methods:

Python, C++, OpenCV, sk-learn, SLAM

Industry: Automotive - company size: <7.500

Project content:

Sensor Fusion in Autonomous Driving – Integration sensor fusion in CarMaker simulation environment.

Technologies & methods:

C++, OpenCV, OpenML, Segmantation

Industry: Gaming - company size: <1.000

Project content:

Full life-cycle data science project management and machine learning system development. Professionalized development processes in start-up environment and replaced failure-prone systems with reliable and maintainable solutions, redesigned a marketing attribution system and increased accuracy through new predictive models and by connecting external data sources, helped to save significant affiliate marketing expenses by providing fraud management team with novel fraud detection algorithms, developed a robust and accurate model for customer lifetime value prediction that became part of the main Business.

Intelligence ETL pipeline and supported automated and manual decision making processes in several departments.

Technologies & methods:

Python, R, Hive, Pandas, Scikit-Learn, Keras, TensorFlow, PostgreSQL, MySQL

Industry: Pharma - company size: <2.500

Project content:

Inventing a method to prioritize genomic variants for clinical decision support, optimization of predictive multivariate biomarker from phase II clinical study data (developing a regularized proportional hazard model), providing statistical methodology for analysis of adverse events spontaneous reporting data (FDA FAERS).

Technologies & methods:

R, Python, SQL, Matlab, C++, machine learning, text mining, predictive analytics, statistical optimization

Industry: Pharma - company size: <1.300

Project content:

Automated customer forecasting for workforce planning & customer satisfaction improvement.

Development of a machine learning model for Insurance Tenders to optimize inventory management, forecast sales of tenders, optimize the bycatch in pharmacies

Technologies & methods:

Python, Pandas, NumPy, TensorFlow, Machine Learning, statistical analytics

Industry: Retail - company size: <4.000

Project content:

Annual sales planning to optimize personnel deployment plan, inventory management, advertising campaign planning, bonus calculation.

Technologies & methods:

Python, R, R-studio, TensorFlow, machine learning, advanced analytics

Industry: Insurance - company size: <3.500

Project content:

Aktien-Vorhersage mittels Long-Short-Term-Memory Neural Networks.

Technologies & methods:

Python, LSTMs, SVM, KNN Regressor, Random Forest Regressor

Industry: Insurance - company size: <10.000

Project content:

Exploit data and machine learning methods to develop the next-gen recommendations products. Predictive modeling for churn, next best product, risk scoring and financial systems.

Technologies & methods:

Python, R, NoSQL, Hadoop