Computer Vision 3D 2D Bildverarbeitung C++ Python KI Deep Learning Embedded Entwickler Mathematiker

6/2022 – 1/2023

Project: Bacteria Detection

Development of a bacteria detection project using microscopic images,
achieving 99% accuracy in detecting the location and number of bacteria.
- Developed and optimized computer vision algorithms using OpenCV techniques to detect bacteria and their numbers with high accuracy.
- Trained yolo5 deep neural networks to extract relevant bacteria bounding boxes using IoUs.
- Accelerated the AI on Jetson Nano by converting to TensorRT (CUDA) and developed the final C++ code interference with pre- and post-processors.
- Optimized the yolo network decoder from Python to C++ and programmed tensors directly on the GPU using CUDA C++.
- Reduced the size of the AI utilizing Knowledge Distillation techniques to achieve high performance processing on the Jetson Nano.

Tech Stack: Python, C++, C, Neural Networks, Machine Learning, Object Detection, yolo, OpenCV, Pytest, Pytorch, CUDA, Numpy, Matplotlib, TensorRT, Git, Jupyter Notebook, OOP, Linux/Bash, AWS, Sagemaker, PyTest, Unittest, Jetson Nano

Amazon Web Services (AWS), C++, CUDA, Git, Maschinelles Lernen, Neuronale Netze, Objektorientierte Programmierung (OOP), OpenCV, Python, PyTorch

1/2022 – 6/2022

Project: Breast Cancer Detection on MRI

Development of a neural network-based algorithm to detect, classify
and segment breast tumors in mammography X-ray images to improve
radiologists' performance in breast cancer screening.
- Used segmentation techniques to train Mask R-CNNs to detect the tumors.
- Improving the inferenc via pretraining of the Mask R-CNN with the bounding box mask of the Yolo detector. Then training on qualitatively better but few masked data.
- Integrated sensitivity and precision metrics, and achieved stateof-the-art results.
- Deployed the finished code on AWS endpoint for professional level breast cancer detection.

Tech Stack: Python, C++, Mask RCNN, Segmentation Networks, PyTorch, NumPy, Machine Learning, OpenCV, AWS, Pandas, Git, Jupyter Notebook, Unittest.

Amazon Web Services (AWS), C++, Git, Maschinelles Lernen, OpenCV, Pandas DataFrame, Python, PyTorch

5/2020 – 11/2021

Project: Fall Detection

Development of Fall detection Machine Learning Algorithms for a Smart Vision Assistant.
- Collect data of human skeleton in various fall scenarios via motion capture and skeleton detection algorithms.
- Balance the dataset with fall and non-fall scenarios to ensure that the machine learning algorithm can distinguish between normal and abnormal movements.
- Feature Extraction: relevant features extracted from skeleton data to represent the movement patterns: joint angles, joint velocities, and acceleration
- Data saved on AWS S3 bucket.
- Extracted features used to create matrices which were fed into a machine learning algorithm.
- Used AWS AutoML to find best ML model.

Tech Stack: Python, Scikit-learn, OpenCV, Pytorch, Image-Pose, AWS, Sagemaker, AutoML, AutoPilot, Cuda, Linux, Git, Pytest.


Project: Voice ReID, Voice to Text
Development of speech recognition system that can identify the speaker and transcribe the language from audio data.
- Audio data divided into 4-second segments sampled at 16kHz.
- Feature extraction techniques on audio data, such as MFCCs, Mel-scale spectrogram, chromagram, spectral contrast, and tonnetz, based on STFT, utilizing Kaldi.
- Utilizing Scikit-learn for feature clustering.
- Utilizing FFT (Fast Fourier Transformation) for denoising and feature extraction.
- Creation of VoiceReID custom model via PyTorch and TensorFlow.
- NLP algorithms for speech-to-text transcription, utilizing part-of-speech tagging and word sense disambiguation.
- Utilizing Gensim and NLTK for text summarization, tokenization, stemming, lemmatization, part-of-speech tagging, parsing
- Applied named entity recognition to identify named entities in text data and categorize them into predefined classes

Tech Stack: Python, pyannote, Kaldi, NLP, AWS Sagemaker, EC2, S3, Lambda, NumPy, TensorFlow, LSTM, Scikit-learn, NLTK, Spacy, Gensim, CoreNLP, Transformers, Hugging Face, Pandas, SQL, FastAPI, Git, Bash, VS Code, Unittest, Poetry.

Amazon Web Services (AWS), C++, Docker, Git, OpenCV, PyTorch, TensorFlow

1/2020 – 5/2020

Project: Box Optimization

Minimizing the amount of empty space in packages. This reduced not
only CO2 emissions but also transport costs. My solution achieved a 10% improvement in packing efficiency.
- Utilized data sampling, dimensionality reduction and data
approximation techniques to reduce the data size.
- Modelled the packages as matrices to rotate and modify easily via numpy.
- Developed evolutionary algorithms with efficient mutation and breeding methods as tensor operations on CUDA to have very fast parallel breedings.
- Benchmarked the results using MILP optimiser.

Tech Stack: Python, Numpy, Torch, Cuda C++, Pandas, SQL, Pytorch, Cuda, Cudnn, AWS, Sagemaker, Docker, Git, Jupyter Notebook, OOP, Clustering, NP-hard problems.

CUDA, Docker, Pandas DataFrame, PyTorch, SQL

1/2019 – 12/2019

Project: Invoice Recognition, OCR

Development of an OCR for invoice recognition software that checks
for invoice errors.
- Built an efficient SQL command search function to simplify the work.
- Used OpenCV to apply image recognition techniques.
- Implemented deep learning techniques for text recognition on invoices.
- Developed deep auto-encoders for data dimension reduction and noise reduction.
- Used Convolutional Neural Networks to improve logo recognition.

Tech Stack: Machine Learning, NLP, Python, Tensorflow, Numpy BigData, PyTesseract, Git, Bash, AWS, VPN, SQL.

Amazon Web Services (AWS), Maschinelles Lernen, Natural Language Processing (NLP), Python, TensorFlow, VPN (Virtual Private Network)