I am a Recent Graduate, experienced Programmer with 4 years in software design at university level and 1 year at professional level, development, and integration. Proficient in Python, C++, and Java. Skilled in SDLC, Agile, Scrum, and Waterfall methodologies. Strong grasp of security, efficient code reuse, and meticulous software design. Accomplished in secure application design, data protection, and vulnerability assessment. A collaborative team player adept at working across functions. Well- versed in IT trends, including Cloud Computing. Eager to contribute to your company's success through innovative, secure, and efficient software development.
- Developed a heart arrhythmia detection device for my Senior Design Project in collaboration with the TAMIU Aires Incubatorship program.
- Utilized machine learning, including Convolution Neural Networks and Decision Trees, to predict arrhythmias.
- Integrated hardware components, including Raspberry Pi, AD8232 Sensor, MCP3008 ADC, and Raspberry Pi touchscreen.
- Created machine learning models using various algorithms.
- Addressed IoT security, focusing on DoS and DDoS attacks.
- Implemented advanced ML techniques for an Intrusion Detection System (IDS).
- Expertise in algorithms: SVM, XGBoost, Random Forest, and ANN.
- Optimized system with feature selection and scaling.
- Created an inheritance hierarchy bank account that a bank can use to represent
customers bank accounts using object-oriented principles and coded in Java in IntelliJ.
- Created classes that inherited from the superclass (base class) that had methods and
variables inherited from one class to another
- Real-Time Gesture Recognition: The project can identify and classify hand gestures in real-time, allowing for interactive applications.
- OpenCV Integration: OpenCV is utilized for capturing and processing video frames from a webcam or camera source.
- MediaPipe Hand Tracking: The MediaPipe library provides hand tracking capabilities, making it easier to locate and analyze hand gestures.
- Deep Learning Model: TensorFlow Keras is used to train and deploy a deep learning model for gesture classification.
- Customizable Gestures: The system can be extended to recognize custom hand gestures, making it adaptable for various use cases.
- Engineered a casino roulette game in C++, showcasing expertise in data structures and algorithms.
- Utilized queues, stacks, vectors, and linked lists, player bets and outcomes simulation, and dynamic
scoring systems.