Electrical Engineering Web Developer

Joseph Ross Muniz

915-***-**** : ************@*****.***

EDUCATION

Bachelors of Science in Electrical Engineering Graduated: May 2018

The University of Texas at El Paso (UTEP) Major GPA: 3.5/4.0

Bachelors of Arts in Creative Writing, Minor in Philosophy Graduated: December 2015 The University of Texas at El Paso (UTEP)

WORK EXPERIENCE

Web Developer September 2015 – Present

The University of Texas at El Paso – El Paso, TX

Developed the UTEP Music Library website where users may create a profile, checkout recorded music, books, and vinyl records, with the ability to track their late fees using the Ruby on Rails framework.

Full stack developer with experience in everything from database management, to front-end work all the way to production deployment using Docker and Rancher.

Currently creating Clerecia, a scholarly works database for UTEP built upon Ruby on Rails. Researchers can perform powerful custom searches for journals and articles.

Test Engineer Intern May 2016 – August 2016 Texas Instruments (TI) – Manchester, NH

Optimized test solution for a line of DC to DC converters, saving TI an estimated $160,000 per year.

Co-invented a programmable power supply to be used in conjunction with TI’s automated test equipment.

Designed an evaluation board using Altium to validate DDR memory terminators.

PROJECTS

Undergraduate Research – Seizure Prediction September 2016 – May 2018

Developed a recurrent neural network model to predict the onset of seizures in patients using electroencephalogram (EEG) data. The network was rated at 73% accuracy.

Spearheaded development of a mobile app and accompanying photoplethysmograph to record a patient’s cardiovascular activity and display it in real time.

Deep Learning – Emotion Recognition September 2017 – December 2018

Created a convolutional neural network capable of ascribing an emotion to a particular audio recording using Keras and Python.

Fused model with other emotion classifiers that rely on other types of input such as video and motion capture data to create an ensemble network to better classify displayed emotion.

Lockheed Martin – Autonomous Aircraft Inspection August 2017 – May 2018

Collaborated with many students to create a drone based system capable of autonomous aircraft inspection.

Employed an array of sensors such as LIDAR, strain gauges, and Zigbee networks in conjunction with a list of heuristics to avoid collisions between the drone and the aircraft that it is inspecting.

TECHNICAL SKILLS

Familiar with reinforcement learning, neural networks, and AI search and optimization algorithms.

Proficient with Python, Java, Ruby, and C/C++ with a working knowledge of Javascript and assembly.

Experienced with software and circuit design as well as their incorporation into embedded systems.

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