Erlangen, Bavaria, Germany
๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฎ๐ฑ ๐๐ | ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ ๐๐ ๐ฝ๐ฒ๐ฟ๐ ๐ถ๐ป ๐๐ฒ๐ป ๐๐ | ๐ง๐ฒ๐ฐ๐ต ๐ช๐ฟ๐ถ๐๐ฒ๐ฟ โข ๐ณ+ ๐๐ฒ๐ฎ๐ฟ๐ of industry experience ๐ฑ๐ฒ๐๐ถ๐ด๐ป๐ถ๐ป๐ด, ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด, and ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด ML and AI solutions that drive ๐ฏ๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ฎ๐น๐๐ฒ โข Expertise in ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐๐, ๐ฐ๐ผ๐ป๐๐ฒ๐ฟ๐๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐, and ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐๐ถ๐๐ถ๐ผ๐ป solutions โข Skilled in breaking down ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐ ๐๐ ๐๐ผ๐ฝ๐ถ๐ฐ๐ for ๐ฑ๐ถ๐๐ฒ๐ฟ๐๐ฒ ๐ฎ๐๐ฑ๐ถ๐ฒ๐ป๐ฐ๐ฒ๐ โข Published ๐ฎ๐๐๐ต๐ผ๐ฟ on ๐ง๐ผ๐๐ฎ๐ฟ๐ฑ๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ โข Active ๐๐ต๐ผ๐๐ด๐ต๐ ๐น๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ sharing insights on LinkedIn and X โข Experienced in leading ๐บ๐๐น๐๐ถ๐ฑ๐ถ๐๐ฐ๐ถ๐ฝ๐น๐ถ๐ป๐ฎ๐ฟ๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ across various industries The views expressed on this platform are my own and may not necessarily reflect the views of my company or any of its officials.
During my time at Siemens Healthineers, I worked to develop a Quality Assurance (QA) Cookbook for Radiotherapy (RT) customers in MRI. My roles and responsibilities included: - Coordinated across customers and stakeholders to address pressing concerns of quality assurance of MR in RT - Liaised between business lines (Radiation Oncology, Computed Tomography (CT), Magnetic Resonance (MR)) - Extensive literature review on existing quality assurance procedures for MR in RT - Conducting experiments with phantoms on MR scanners - Writing, editing, and compiling the QA cookbook for publication
As part of my university internship, I worked in the Computer Vision Group at the Pattern Recognition Lab at my university. I worked on five computer vision projects under the supervision of different researchers. These projects included: - object detection - face recognition - handwriting analysis and writer identification - license plate identification The projects were implemented using Python, Matlab, and several Python libraries (such as Scikit-learn, Scipy, OpenCV, Theano and Numpy).
I worked as a Machine Learning Engineer for predictive analysis of time-series data in my role in Fraunhofer: - Conducted literature review on predictive maintenance of switchboards to build robust deep neural networks for timely fault diagnosis - Developed Machine Learning solutions using Keras, Tensorflow, and SKLearn in Python to provide predictive solutions in supply chain services - Implemented predictive models such as deep neural networks, ARIMA, and linear regression models on time series data.