Sharmila Sivalingam

I am a Master's student in Electrical and Computer Engineering (ECE) at Northeastern University, Boston. I have hands-on experience in FPGA design, hardware-software integration, and machine learning. I specialize in using advanced technologies to optimize performance, reduce inefficiencies, and create cutting-edge solutions.

From designing a multi-speed, direction-controllable vehicle using FPGA Basys3 and Xilinx Vivado to developing a Python tool for memory optimization that reduced peak memory usage by 2 GB - I am passionate about applying my knowledge to solve real-world challenges. My work on Myocardial Infarction detection with deep learning models has been published at the IEEE DISCOVER 2023 conference, further proving my ability to bridge the gap between hardware and software.

Additionally, my experience in Verilog, VHDL, SystemVerilog, UVM, and various EDA tools enables me to design, simulate, and verify complex systems precisely. I thrive in collaborative environments, having worked on projects in agile frameworks, and always eager to learn new technologies and improve my skills.

I am excited about the opportunity to contribute my expertise to innovative hardware and software development projects. Feel free to reach out if you'd like to learn more or discuss potential collaborations.

I'd gladly share my resume to help you understand how my skills can contribute to your team!

• Validated Ford’s diagnostic protocols to ensure compliance with OEM fault detection and system requirements. • Performed system-level diagnostic test procedures in C++, using unit tests to verify system behavior. • Used Synopsys and TRACE32 to analyze ECU faults, debug memory issues, and trace runtime errors. • Identified root causes of software defects and prioritized fixes in an Agile Scrum environment, improving defect resolution time by 20%. • Verified diagnostic communication over UDS and CAN protocols to ensure correct fault reporting and data accuracy.

Northeastern University, Boston, MA Master of Science in Electrical and Computer Engineering Expected May 2025 graduation
GPA: 3.7/4 
Concentration: Hardware and Software for Machine Intelligence Coursework: Computer Architecture, Compilers, Computer Hardware Security, Simulations and Performance Evaluation