Samuel Schmidt

UPS Workflow Improvement Web App

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While at UPS, I worked on a web app used in their Worldport package sorting facility. This app automates a great deal of the part-time supervisor's day.

The app was built using a traditional ASP.NET stack - HTML/CSS/JavaScript, C#, and MS SQL Server. I contributed to all pieces of the application. I was also in charge of managing all database changes and reduced our team's deployment issues to zero.

• Employer: UPS
• Dates: 2016-Present
• Service: Full-Stack Development

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Unreleased Beer Discovery Web App

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I am in the process of developing a node.js web app that allows users to discover local beer and breweries tailored to their tastes. I believe that supporting local businesses is important, so the app uses the user's location (or an input location) to search for beers and breweries nearby. The app uses resources such as the Brewery DB API to make recommendations that align with user inputs.

• Dates: 2016-Present
• Service: Web Development

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Disney Mobile Maintenance App

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While working at Disney, I worked on several different projects. The largest was a native Windows app which is now used on-site at Walt Disney World to perform maintenance on animatronic figures. Maintenance workers use the app on Windows tablets to wirelessly monitor the status of dozens of animatronic components. Attractions I affected include Frozen Ever After at Epcot and Rivers of Light and Disney's Animal Kingdom.

• Employer: The Walt Disney Company
• Dates: Winter-Spring 2016
• Service: Native App Development and Documentation

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AERSMine

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I worked with a senior developer at Cincinnati Children's Hospital to revamp and develop AERSMine, a data mining web application. The app allows users to analyze drug, reaction, and indication (symptom) information from millions of patient reports. Medical professionals and data scientists use the app to explore unknown drug and patient relationships. Our AERSMine manuscript was published by Nature Biotechnology in May 2016.

• Employer: Cincinnati Children's Hospital Medical Center
• Dates: 2013-2015
• Service: Front-End Development

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March Madness Bracket Generator

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A friend and I started a side project during the 2016 NCAA Men's Basketball tournament. We wanted to see how well we could match a the 64-team bracket using a probabalistic algorithm. Some of our work can be seen at this Github repository. We used our algorithm (colloquially known as "log2") to correctly predict the winner of both 2016 and 2017 March Madness tournaments!

• Dates: Spring 2016

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A Massively Parallel Algorithm for Cell Classification Using CUDA

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My Master's Thesis at the University of Cincinnati used various Machine Learning approaches to highly optimize a cell classification algorithm. I parallelized a Gaussian Bayesian classifier using CUDA, a GPU computing platform developed by NVIDIA which allows thousands of computing threads to work together to speed up complex mathematical calculations. This classifier has been proven to be highly accurate for this type of problem. My parallel algorithm improved on the serial version by over 700%.

Dates: 2013-2015

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