Schlagwörter
Skills
Machine Learning, Cloud Computing, NLP, OpenAI, GPT4, OCR,
Neural Networks, Computer Vision, Image Processing, Embedded,
Time Series Analysis
Programming
Python > 11 years
C++ & C > 6 years
Matlab > 8 years
Azure > 3 years
AWS > 4 years
Software & Tools
Scikit-learn, OpenCV, Numpy, Tensorflow, PyTorch, PyTesseract, Docker, Pandas, SQL, Spark, AWS Sagemaker, Linux, CUDA, C++, Git, Power BI
IoT & Embedded
Jetson Nano, ARM Cortex, AT- mega
Production Code
Unittest, Pytest, Poetry
Projekthistorie
Development of an LLM language assistant on the iPad for daily medical practice.
• The language assistant summarily stores the spoken information in the patient's file.
• Development of OCR on iPad for converting a photo into text, storing it in the patient's file.
• Interaction with patient documents via LLMs and spoken language.
• Developed LLM finetuning pipeline via Langchain Framework.
• Fine-tuned non-proprietary LLM models for specific use cases
• Developed solutions via attention based metrics how much the LLM is hallucinating, getting LLMs with much less hallucination
• Integrated LLM use cases in a playground of the customer by integrating LLMs in a AWS Docker Endpoint and giving access via REST API
for the connection with the Frontend
Deploy and optimize OpenAI services for a big customer. Cloud migration in Azure and Azure Cognitive Services. Deploy GPT4, advanced prompt engineering, GPT4 fast API computation with vectorized embeddings using ML. Project ongoing.
Tech Stack: NLP, Azure Cloud, OpenAI, GPT4.0, Python, Prompt Engineering, Gitlab CI/CD, Azure DevOps.
As a Senior Data Scientist, I led the development of a Smart Trunk Opener project (joint project of infineon and intive), which aimed to accurately predict kick gestures for hands-free trunk opening using the BGT24***** radar based on the Doppler effect and using Neural Networks on embedded devices.
- Developed algorithms to detect abnormal signals and validate them as kicks using Python and Matlab
- Created NN models for better predictions, and optimised them using TensorRT for efficient use on embedded devices.
- Improved data reception and pre-processing on the radar using the C programming language.
- Compiled complete production code as C code using Atmel Studio and deployed it on the embedded system.
Tech Stack: Python, C++, C, Neural Networks, NumPy, Pandas, Pytest, Pytorch, Linux, Bash, Git, Matlab, Ifxdaq (Infineon Data Acquisition), Matplotlib, Time Series analysis, ARM Cortex, ARM Socket, RTOS, ATmega, Atmel Studio.
Development of a bacteria detection project using microscopic images, achieving 99% accuracy in detecting the location and number of bacteria.
- 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 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.
Determination of the optimal trajectory for the robot arm. I have developed a custom neural network to solve this optimization problem using physically modeled helper computations.
- Conducted research on optimal control problems via neural networks.
- Generated simulated data based on classical mechanics, performed data processing and normalization, and converted
original Matlab files to Numpy.
- Constructed LSTM networks
- Enriched data with cubic splines and implemented objectoriented programming to produce productive and efficient
code.
Tech Stack: Neural Networks (Recurrent Nets), Supervised Learning, Python, C++, Pytorch, Cuda, Numpy, Matplotlib, Torchdiffeq, MLFlow, Tensorboard, Autograd, Integrate, Torchsummary, Scipy, Torchcubicspline, Git, Jupyter Notebook, OOP, Pytest.
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.
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.
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.
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.
Zertifikate
Reisebereitschaft
- Reisebereitschaft Europaweit
exali IT-Haftpflicht-Siegel (Sondertarif für Freelancermap-Mitglieder)
Das original exali IT-Haftpflicht-Siegel bestätigt dem Auftraggeber, dass die betreffende Person oder Firma eine aktuell gültige branchenspezifische Berufs- bzw. Betriebshaftpflichtversicherung abgeschlossen hat. Diese Versicherung wurde zum Sondertarif für Freelancermap-Mitglieder abgeschlossen.
Versicherungsbeginn:
10.03.2023
Versicherungsende:
01.04.2024