Software Developer Engineering

ERIK LEWIS

** ******** **** *, ********** AB, T*K 5Z5

+1-403-***-****

****.*.*****@*****.***

github.com/TheELewis

linkedin.com/in/erik-lewis-0baab213b

**Academics**

BSc, COMPUTER ENGINEERING University of Alberta 2016-2020 Favourite Courses:

• Advanced machine learning, Software engineering, Object oriented software design, Advanced computer interfacing and Digital logic design, Non-procedural programming languages. Tools and Languages

**Languages**

Proficient in :

- Python, C, C++ C#, Java,

Experienced With :

- MATLAB, Lisp, Prolog, SQL

**Tools**

Proficient in :

- Git, Docker, UNIX Systems, Jenkins

Experienced With :

- PandaS, SciKit-learn, REST API

**Work Experience**

GENERAL DYNAMICS (GDMS-C) Software Engineering Intern

June–December 2019

• Led a virtual testing Agile team as part of the larger ‘EvO’ project to alleviate a severe hardware testing bottleneck for the whole program.

• Built a tool to create large, simulated radio networks capable of scaling to 10 times the size of target and testing 100% faster than hardware.

• Optimised hardware build and test Jenkins pipelines which sped up testing by 50%.

PATCHING ASSOCIATES (PAAE) Software Engineering Intern

April–September 2018

• Developed a test suite for a webtool that completely removed the 2 to 3 day overhead for testing.

• Created an automated report writing tool that sped up report turn around time by 500%.

• Wrote a wrapper for an outdated API to allow easy automation of an administrative software (CRM).

CTRL-V Host & Internal Software Developer

April-September 2017

• Developed a tool to convert AutoCAD and SketchUp files into interactable virtual reality spaces.

• Built a simple VR sandbox for testing game physics, player mobility options, and object collisions.

**Projects**

CHESSMATE github.com/W20CapstoneProject/ChessMate

• Open source 5DOF robotic arm programmed to play chess on an RFID enabled game board.

• Exacting precision achieved with custom inverse kinematics library controlling 5 stepper motors.

• Modular software design allows for high degrees of expandability and extensibility.

PREDICTING MORTALITY IN GERIATRIC TRAUMA PATIENTS USING ENSEMBLE LEARNING View on LinkedIn

• Worked with local surgeons and Dr. Russel Greiner to develop models that can predict likelihood of death in trauma patients based on EMS assessment.

• Created a model with classification accuracy of ~92% using ensemble learning.

• Digitized and interpreted 6000 data entries with 175 features, then trained models on cleaned data.

**Hobbies**

• Earned multiple medals at a provincial level in basketball, and track & field during high school.

• Performing live music and jamming with other musicians.

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