Python Software Engineering & Web Development

Christopher Earl

SOFTWARE ENGINEER

781-***-**** adcgj4@r.postjobfree.com

Education

University of Massachusetts Amherst Amherst, MA

B.S. COMPUTER SCIENCE, GPA: 3.1 Graduation date: May 2021 Goal & results oriented, willing to utilize strong skills in critical thinking and problem solving for the internship position. Primary objective is to apply skills learned in college to design intuitive, practical, industry-quality software. Courses

Artificial Intelligence Machine Learning Algorithms Software Engineering Search Engines Web Programming Differential Equations Skills

Languages: Fluent Python, Java, HTML/CSS, Javascript Languages: Familiar C, C++, C#, SQL

Technical Git source control, Tensorflow (Python), Sci-Kit Learn, Anaconda, MongoDB, MS DOS, LaTeX, Microsoft Office Suite Foreign Language Japanese: Partial working fluency Soft Skills Analytical, Results-driven, Professional, Creative Projects

ItWorks!

(CS320) Sept-Dec 2019

• Web based survey creation and management tool created for Ultimate Software

• Managers and employees can create, schedule, and manage surveys on the platform using a company login

• Integrated infographics for data, generated using SurveyJSandChartJS

• Created by group of 9 developers over course of one semester

• Built using ReactJS, and MongoDB

Page-Rank

(CS446) February 2020

• Program which determines quality of a document or webpage, based on the number of references

• Utilizes the Google PageRank formula. Optimized for efficiency, the algorithmwasdesignedtocomputelargenumbersofsourcesrapidly

• Able to rank over 1.4 million sources in under a minute on average hardware

• Built using Python 3.8

Composer Recognition AI

(PERSONAL) December 2019 to current

• AI project to improve on baroque/classical/romantic era composer recognition via improved supervised learning models

• Employs more degrees of music theory, such as chord progression and ornamentation recognition, to improve on accuracy of previously attempted models

• Uses transition matrixes and Markov chains to predict sequences of notes, and recognize signature styles

• Plans to expand into unsupervised learning, utilization of neural networks, and other genres of music

• Built using Python 3.8, and utilizes Tensorflow Magenta. (Work in progress) Interests

• Machine-learning, AI, Neural-Networks

• Practical application of mathematics and theoretical computer science

• Analyzing, creating, and explaining art using mathematics and computers

• Music theory and neo-romantic music composition

• Philosophy, and its applications to creating a better life-style

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