ML Engineer

github

Christine Straub  • Peerlist

Medical Image and Signal Processing

Libraries: Tensorflow, Keras, Scipy, Sklearn, OpenCV, Pillow, CUDA, cudNN,SQL, Pandas, matplotlib

Stack:
- Deep knowledge in biomedical engineering- Analytical method and statistical analysis of medical image/signals.
- Machine learning for image analysis (CNN/DNN)
- SQL as well as NoSQL database technologies

Key Skill:
- Computer Vision (Object Recognition)
- Medical image analysis
- Large-scale image processing - 2d-filtering
- Quality Analysis Pipeline

Objective: Use cutting-edge computer vision and machine learning approach (tools and algorithms) to develop a powerful, low-cost, novel technology that combines the identification of bacterial infection with the determination of the most effective antibiotic to treat it, enabling results that currently take days in just life-saving hours.

Image(the sensor data) processing and analysis algorithm development are at the core of this job and the key tasks that were required to contribute are:

Accomplishments:

- Developed and implemented image processing algorithms to convert raw sensor signals into colorimetric time series data with the highest signal integrity.
- Developed and implemented vision-based quality control tools to be deployed at manufacturing (built and deployed the custom object detection CNN model).
- Developed tools to analyze and visualize high-volume, high-dimensional data to find interesting trends and correlations.
- Maintained image processing and data analysis software stack.
- Developed analytical methods and statistical analysis for processing high-volume, temporal high-dimensional data streams generated in real-time from proprietary sensor technology.
- Developed an algorithm for decisions and measurements of multi-dimensional data streams.
- Provided proper documentation of analyzed results.

Medical Image and Signal processing ML for SpecificDiagnosti

Introducing the AI SQL Brain App – a revolutionary tool for effortless data exploration. Simply upload your CSV files and ask any question in natural language. Experience intelligent SQL processing for instant, insightful responses, democratizing data analysis for all.

AI SQL Brain

The motivation was to experiment with end to end machine learning project and get some idea about deployment platform like Streamlit. By prior detection & through proper medical treatment, adverse effects can be prevented. To help in early detection, technology can be used very reliably and efficiently. Using machine learning we have built a predictive model that can predict whether the patient is diabetes positive or not.

Diabetes Detection Machine Learning Streamlit App

This application leverages OpenAI's powerful text-davinci-003 language model to convert natural language prompts into SQL statements. With this tool, you can easily translate your verbal instructions or queries into structured SQL commands.