Education
Bachelor of Engineering in Electrical Engineering
Expected Graduation: May 2026
GPA: 3.7
- Science Engineering Equity Development (SEED) Scholar. SEED Scholarship is a full-tuition scholarship given to only 10 students every year.
- Ignatian Award – Leadership and Academic Excellence
- University Honors Program
Experience
University of Detroit Mercy, Detroit Michigan
College of Engineering and Science Ambassador
September 2022 – Current Date
- Presented engineering programs and student projects to over 500 prospective students at outreach events, increasing engagement and interest in the university’s STEM offering.
- Hosted shadowing sessions for prospective students, providing comprehensive insight into academic life and student resources.
Ford Motor Company, Redford, Michigan
Automation and Controls Engineering (ACE) Intern
May 2025 – July 2025
- Integrated Allen-Bradley and Pepperl+Fuchs encoders with Rockwell PLC. Configured hardware/software, modified ladder logic, and validated encoder values to ensure proper functionality.
- Established a TCP connection between Rockwell and Siemens PLCs. Updated logic to support communication, including configuring IP addresses and port numbers, in both Siemens TIA and Rockwell Studio 5000.
- Verified component traceability of a rear axle at the Sterling Axle Plant, by checking 2D marks present on components on the plant floor, and online in a component traceability software.
- Assigned and verified over 2000 unique IP addresses for devices on the trim segment for the Oakville Assembly plant. Ensured correct and non-duplicate allocation to match network capacity
Digital Manufacturing Systems (DMS) Controls Engineering Intern
May 2024 – July 2024
- Developed a 16-character random label generator in Siemens TIA, streamlining label testing, and reducing label creation time by 30%.
- Created Add-on Instructions and User Defined Data Types in Rockwell Studio 5000, facilitating the seamless data transfer between multi-vendor PLCs ensuring accurate data mapping.
- Designed a FactoryTalk Studio View screen to monitor and verify data transfer between multi-vendor PLCs, improving troubleshooting and data accuracy.
- Developed a tool testing sheet that standardized and automated test data analysis for multi-vendor tools, improving readability and accuracy of performance metrics.
Manufacturing Technology Development Intern
May 2023 – July 2023
- Tested and evaluated edge device management solutions, contributing to the selection of high-performing devices for operational deployment.
- Replicated Siemens TIA screens in Rockwell FactoryTalk Studio View, ensuring consistent UI across platforms, enhancing system compatibility and user experience.
- Conducted CPU and Network performance tests using iPerf and Stress-ng to simulate real-world conditions, evaluating device performance under heavy data loads.
DTE Energy, Detroit, Michigan
Summer Youth Internship Program – Pipeline Safety and Regulatory Compliance
June 2021 – August 2021
- Created comprehensive work instructions for an Excel project, involving document usability and reducing errors in data retrieval for employees.
- Transferred and organized data from over 600 scanned PDF documents into SharePoint, renaming files and adding metadata, resulting in a 70% reduction in time required to locate specific documents.
Projects
Electronic Systems Course:
ELEE 3550 Short Distance Sonar
Developed a sonar system using Arduino Mega, and ultrasonic sensor, and LEDs for proximity indication. Programmed the system using Simulink. Ultra sonic sensor was used to obtain the distance to an object, while the LEDs indicated the proximity of the object to our system. Various testes resulted in a 98% accuracy of our system functionality.
Circuits and Electronics Course:
ELEE 2510 Autonomous Robot
Designed and created circuits, code, and chassis for an autonomous robot that navigated an obstacle course with 93% accuracy using IR and optical sensor systems to respond to its environment.
C++ Programming Course:
CSSE 1710 Machine Learning Research
Applied machine learning to analyze predictive factors for academic success, uncovering key correlations between certain factors and a student’s academic performance.
Skills
Technical
Programming
Language
- SolidWorks
- Ladder Logic
- Siemens TIA Portal
- Rockwell FactoryTalk Studio View
- MATLAB
- Multisim
- Simulink
- C++
- Ladder Logic
- System Verilog
- Assembly
- Structured Text
- Siemens SCL
- English – Native proficiency
- Spanish – Native proficiency