Hello, my name is

Cole VanOphem

and I'm a software engineer.

About Me

I'm a software engineer with a passion for creating and developing software, especially in the FinTech space.

I have experience in backend web development with C# and Python, and am interested in branching out to new areas.

I'm always looking for new opportunities to learn and grow as an engineer, so please feel free to reach out.

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Education

University of Michigan - Ann Arbor

Bachelor of Science, Computer Science

August 2022 - present

EECS 482

Intro to Operating Systems

EECS 485

Web Systems

EECS 376

Foundations of Computer Science

EECS 370

Intro to Computer Organization

EECS 281

Data Structures and Algorithms

EECS 280

Programming and Intro Data Structures

EECS 497

Human-Centered Software and Design and Development

Experience

Software Engineer Intern

United Wholesale Mortgage

May 2023 - August 2023

  • Developed containerized C#, ASP.NET Core microservice to aggregate up to 1.48 million Jira fields per day from scheduled RESTful API calls.
  • Engineered microservice to support a machine learning model predicting and preventing code changes that could disrupt operations, mitigating the risk of service outages leading to lost loan origination.
  • Deployed to Azure via Docker containers and integrated with Orkes Conductor.
  • Collaborated in daily scrum meetings, sprint planning, and retrospectives within an agile team.
  • Regularly presented to the product owner, stakeholders, and architects, incorporating valuable feedback.

Team Lead

Michigan Hackers

May 2023 - present

  • Led weekly sessions to teach and review technical content, mainly focusing on data structures and algorithms.
  • Developed and presented materials covering effective career strategies.

Projects

WolvWealth

WolvWealth Homepage

Web application that provides users with a robust toolset for optimizing their investment portfolios.

Runner-Up for Capital One's Best Financial Hack Award at MHacks.

Technologies Used: React, TailwindCSS, Flask, SQLite

MichMoney

MichMoney Forex Page

Web platform that provides users with actionable insights into earnings calls and Forex opportunities.

Winner of Best Use of Digital Currency Award at SpartaHack

Technologies Used: React, TailwindCSS, Flask, SQLite

Thread Library

EECS 482

C++17 library to administer the creation, lifetime, and execution of concurrent threads in a multicore system.

Supports condition variables, mutexes, yields, joins, and interprocess interrupts (IPI).

Technologies Used: C++17, clang, CMake, Linux

Disk Pager

EECS 482

Multithreaded C++17 program to schedule concurrent disk requests using monitors.

Implemented thread-safe concurrent priority queue to manage disk requests based on shortest seek time first (SSTF) algorithm.

Technologies Used: C++17, Clang, CMake

Full-Stack Social Media Website

EECS 485

Social media site that allows users to share photos with others, and comment on posts.

Features dynamic user interface made with React, and RESTful API made with Flask enabling like, comment, post, authentication, and account management functionalities.

Technologies Used: React, JavaScript, Flask, Python, SQLite

SQL Clone

EECS 281

Program to emulate a basic relational database using STL containers and algorithms.

Implemented a subset of SQL commands to create and manipulate tables, including JOIN and conditional statements.

Technologies Used: C++11

Stock Market Simulator

EECS 281

Program to simulate buy and sell orders from multiple traders on multiple stocks in a market.

Utilized priority queues to match buyers and sellers. Implemented multiple data analysis modes to calculate performance of traders and stocks at end of day.

Technologies Used: C++11

3D Maze Solver

EECS 281

Program to solve 3-dimensional mazes with multiple types of obstacles.

Uses breadth-first search or depth-first search to find a path through the maze, given a start and end point.

Technologies Used: C++11

Piazza Post Classifier

EECS 280

Program to predict the topic of Piazza forum posts using a naive Bayes classifier.

Trained on data set of over 11,000 posts and achieved over 87% prediction accuracy while classifying posts.

Technologies Used: C++11