Mariem Khedhiri

• Collaborate directly with clients to deliver practical analytical solutions using SAP SAC. • Combine technical and functional expertise to develop SAC solutions aligned with client expectations. • Translate functional requirements into technical designs and recommend optimized SAC solutions. • Work with live data models and import models based on BW queries and HANA calculation views / procedures. • Implement best practices for dashboards using analytic designs and live data models. • Stay up to date with new features released by SAP in SAC. • Produce technical documentation through Knowledge Base Articles for both internal and external use. • Provide real-time customer support through Expert Chat. Keywords: SAP Analytics Cloud, Analytics Designer, Knowledge Management, Technical & Functional Support, ServiceNow, Jira.

•Developed an innovative approach for epileptic seizure classification utilizing EEG channel selection and com-pressive sensing techniques. •Conducted data collection at a partner hospital using the SOMNOscreen Plus EEG 32 measurement system to gather EEG signals. •Implemented data preprocessing techniques in MATLAB, including data cleaning and artifact removal using Independent Component Analysis (ICA). •Designed and implemented a novel method for feature and EEG channel selection within a unified pipeline us-ing Python, Google Colab. •Developed a web interface using React.js and Flask to facilitate the use of the solution for the visualization and classification of seizures. •Deployed the developed methodology on an Raspberry Pi4 for real-time seizure classification and monitoring, demonstrating practical applicability. Key tools: MATLAB, EEGLAB , Python, Google Colab , React.js , Flask , Raspberry Pi4

•Developed a TensorFlow-based Convolutional Neural Network (CNN) for employee recognition and negative emotion detection. •Trained the model for facial recognition and enhanced it for emotion detection. •Implemented the solution with OpenCV for real-time image processing and utilized Jupyter notebooks for prototyping. •Conducted thorough testing to ensure accuracy, contributing to a reliable deep learning solution for employee well-being monitoring. Key Tools: Deep Learning, TensorFlow, Jupyter, OpenCV, Python

• Conducted Exploratory Data Analysis on the 'SampleSuperstore' dataset to identify areas for increasing profita-bility. • Created a hybrid model for stock price prediction using numerical analysis of historical stock prices, and senti-mental analysis of news headlines. Key Tools: Supervised and unsupervised learning, Clustering, Linear Regression

• Developed a robust communication protocol for microcontrollers utilizing STM32 and I2C, ensuring efficient data transmission. • Engineered a redundancy backup system to enhance reliability and fault tolerance in space applications, specifically implemented within a CubeSat. • Presented project findings and results to esteemed organizations such as JAXA and UNOOSA, highlighting technical proficiency and innovative contributions to the Tunsat1 Satellite Project Key Tools: STM32, I2C, CubeMX.