Mostafa Samir

Developed cross-platform mobile applications using Flutter and Dart, emphasizing robust UI/UX design and seamless API integration. Managed deployment processes to both Google Play and the App Store.

Completed a comprehensive data science program, focusing on data preprocessing, visualization, and machine learning with Python. Gained hands-on experience in predictive modeling and clustering projects using Pandas, NumPy, Matplotlib, and Scikit-learn.

Participated in a summer training program focused on machine learning, covering supervised and unsupervised learning techniques. Developed expertise in feature engineering and model evaluation through hands-on projects using Scikit-learn and TensorFlow.

Completed specialized training in AWS Machine Learning, utilizing Amazon SageMaker, Lambda, and Glue for scalable deployments. Focused on optimizing TensorFlow and PyTorch models within cloud environments.

Completed training in Cisco Networking, gaining proficiency in configuring and managing routers, switches, VLANs, OSPF, EIGRP, and ACLs. Acquired hands-on experience in designing and managing secure network infrastructures.

Trained as a Digital IC Design Engineer, working on multiple projects involving circuit design, simulation, and verification. Utilized industry-standard tools such as Tanner, VCS, Cadence, and Questa, with a focus on layout and functional simulation.

Developed two recommendation systems using KNN and Cosine Similarity with Transformers, adhering to the full ML lifecycle. Deployed a Flask-based web application integrated with Node.js for ad and search recommendations.

Developed a car price prediction model using regression analysis, incorporating regularization techniques like Lasso and Ridge. Utilized cross-validation and hyperparameter tuning to ensure model robustness and statistical significance.

Designed and simulated a half-adder circuit using Tanner EDA tools, performing binary addition to produce sum and carry outputs. Conducted SPICE simulations to verify functionality and analyzed performance metrics like propagation delay and power consumption.

Designed a full adder circuit and verified its functionality using Quartz and Questa tools, implementing binary addition of three inputs. Utilized Quartz for schematic design and simulation, and Questa for comprehensive testbench design and verification, including assertions and coverage analysis.

Implemented core machine learning algorithms from scratch in Python, including linear regression, logistic regression, k-means, and decision trees. Focused on understanding underlying principles such as gradient descent, loss functions, and decision criteria.

Created a text classification project utilizing Naive Bayes, implementing preprocessing techniques like tokenization and stopword removal. Applied feature extraction methods such as Bag of Words and TF-IDF for tasks including spam detection and sentiment analysis.

Developed a cross-platform mobile application using Flutter for personalized business recommendations, integrating location-based suggestions and user reviews. Optimized the app for seamless deployment and performance on both Android and iOS platforms.

Designed and simulated a programmable ring oscillator circuit using Cadence tools, focusing on circuit schematic creation and functional verification. Performed SPICE simulations to analyze performance metrics and ensure design specifications were met.

Supported the GDG tech community by actively organizing events and facilitating knowledge-sharing sessions. Engaged in networking activities with fellow developers to foster collaboration and growth.

Contributed to the IEEE AI committee by advancing AI innovation through organizing initiatives and sharing valuable insights. Fostered collaboration among AI experts and enthusiasts within the community.

Grade: Excellent

Activities and societies: Recommendation System for Businesses, Graduation project, Excellent

Completed a Bachelor’s Degree in Computer Science. The graduation project focused on a Recommendation System for Businesses, which received an excellent grade.

Completed a Bachelor’s Degree in Science and Education. This program provided a foundation in scientific principles and educational methodologies.

Completed a 265-hour program on AI, machine learning, deep learning, and NLP. Engaged in hands-on projects like sentiment analysis and object detection using Python and TensorFlow.

Completed an OOP course, learning design principles for scalable and maintainable code. Implemented OOP in real-world scenarios, creating custom data types and managing complex systems.

Completed a 44-hour course on Data Structures and Algorithms with Python. Covered arrays, linked lists, stacks, queues, graphs, sorting, searching, and Big-O notation, with practical coding challenges.

Completed a mathematics course covering calculus, linear algebra, and algebra. This enhanced analytical and problem-solving skills essential for technical fields.