I enjoy building, testing, and debugging systems that bring hardware and software together.

• M.S. in Computer Engineering from California State University, Northridge
• Experience across embedded systems, FPGA/RTL design, hardware validation, and system integration
• Built hardware-software projects using Raspberry Pi, Embedded Linux, Python, Verilog, and SystemVerilog
• Comfortable with simulation, debugging, testing, and bringing engineering systems from development through validation

Programming: C, C++, Python, Java, SQL

Hardware & RTL: Verilog, SystemVerilog, FPGA, RTL Design, Digital Systems, SoC Design

Embedded Systems: Raspberry Pi, Embedded Linux, Camera Integration, Hardware/Software Integration, System Testing

Verification & Validation: RTL Simulation, Testbench Development, Waveform Debugging, Hardware Validation, System Integration Testing

Tools: Vivado, Xilinx ISE, Synopsys VCS, DVE, Git, Linux, MobaXterm

Data & Machine Learning: TensorFlow, Keras, Machine Learning, Data Analysis

Developed a low-cost wearable navigation-support prototype using a Raspberry Pi Zero 2 W, Camera Module V2, Python, and Embedded Linux. Implemented camera-based environmental analysis, audio feedback, calibration, testing, and standalone Linux service operation.

Designed and verified a 3×3 fixed-point matrix multiplication system using Verilog and SystemVerilog. Implemented Q-format arithmetic and validated functionality using Synopsys VCS, DVE, testbenches, and waveform debugging.

Implemented and simulated a 5-stage pipelined processor while exploring datapath design, control logic, instruction execution, and pipeline behavior.

Worked with FPGA and SoC development platforms using Verilog/SystemVerilog, Vivado, Xilinx ISE, simulation, hardware implementation, and Python-based hardware interaction.

Developed a transfer-learning image classification workflow using TensorFlow and Keras with VGG16, ResNet50, and EfficientNetB0 to classify blood-cell images across multiple classes.

Built and evaluated machine-learning models for vehicle price prediction using large automotive datasets, feature analysis, regression techniques, and model-performance metrics.

• Embedded and firmware development
• FPGA/RTL design and verification
• Hardware validation and system integration
• SoC and digital hardware development
• C/C++ and Python for engineering applications