Software Engineer

Brendan Furtado

203-***-****

! ***********@*****.***

Ï Pasadena, CA

è linkedin.com/in/brendan-furtado-55b26b139/

¥ github.com/brendanfurtado

EDUCATION Master of Computer Science (MCS) Mar. 2021 - Present Arizona State University

Bachelor of Arts in Computer Science Sep. 2016 - Dec. 2020 McGill University, Montreal, Quebec

EXPERIENCE Program Lead iScano Inc.

May 2020 - Present Remote

• Co-founder in startup that aims to democratize Point Cloud data and LiDAR-based workflows in the construction industry.

• Created a workflow that processes 3D point cloud data. Downsampled point cloud data is then used for client deliverables such as 3D models, mesh, and BIM. Software Development Intern Interactive Brokers (IBKR) Jun. 2019 - Aug. 2019 Greenwich, CT

• Utilized Chaos Engineering practices to simulate and document failure modes of Zookeeper Ensembles and Apache Kafka Clusters for a database restructuring project.

• Independently built a reusable suite of tests to validate client trading account access and account operations across different browsers and platforms using Selenium.

• Improved Automated Quality Assurance code base by refactoring code and fixing bugs through restructuring and creating new functions that follow design principles. PROJECTS Safe-Eat - A Full Stack Application w/ Cloud DB

• Application collects, stores and socializes restaurant reviews specific to how they manage clients with dietary and food allergy restrictions. Data is compiled through user reviews and their dietary profiles.

• Integrated authentication, restaurant searches, profiles, and a review system for au- thorized users.

• Incorporated a RESTful backend server for retrieving restaurant data using the Yelp API and Google Cloud Firestore for storing user data and reviews.

• Utilized: VueJS, Javascript, Cloud Storage (Firestore), NodeJS, Express, HTML/CSS Breast Cancer and Wine Classification

• Implemented from scratch Logistic Regression and LDA algorithms to classify breast cancer tumors and wine ratings.

• Performed hyperparameter experimentation to compare the accuracy and perfor- mance of the two models. Discovered that linear discriminant analysis methods ran more efficiently.

• Utilized: Python, Numpy, Pandas, Matplotlib

Image Classification MNIST Dataset

• Created Convolution Neural Nets to classify images on MNIST dataset.

• Competed in a interuniversity Kaggle competition with the University of Montreal.

• Utilized: Python, Keras, PyTorch, Numpy, Pandas, Matplotlib, Jupyter Notebook TECH

SKILLS

Languages: (proficient): Java, Python, R, SQL (familiar): C/C++. Web Development: VueJS, HTML5/CSS, NodeJS, ReactJS. Applications/Tools: AWS-EC2, Google Firebase, Vim, Git, NumPy, Pandas. INTERESTS Youtube Video Creation/Editing - ("Brute Force Programming"), guitar, writing, read- ing, modding video games.

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