Senior Software Engineer
Graduated from Iowa State in 2004 with a degree in Computer Science. Moved to Des Moines and has been doing .NET development ever since.
In 2016, I decided to test out Georgia Tech's online masters program and couldn't have been happier. I graduated Fall 2018 with a Master's in Computer Science with a machine learning specialization. Since that time I have gone on to get recognized as a Google Developer Expert in Machine Learning and given presentations to audiences all across the globe.
Currently, I am employed by Microsoft as a Software Engineer working in the Azure Active Directory Data Governance team.
Area of Expertise
Do you want to learn how to get started with machine learning using Google's world class cloud? Do you want to start a personal project and just need the first push? In this talk, I will work through the entire lifecycle of getting a machine learning model to production. I will start with setting up the AI Platform, next I will start training the model, and finally, I will deploy the model for production usage.
Do you want to get started with data science without fighting crypto fanatics for GPU cards? Do you want to see what Azure has to offer for training/deploying models? In this talk, I will walk through the whole process. I will start with setting up the job, I will show how to connect to your workspace, I will then submit the job for training, I will walk through validating the model, and finally I will deploy the model
I will discuss a project that I created in my final semester of graduate school that covered Reinforcement Learning. It starts with the basics, introduces the Q-Learning algorith, and continues with recent updates and finishes with Google DeepMind's latest update the Double Deep Q Network used to defeat a number of Atari games.
While I can't make my Raspberry Pi hunt down John Conner I am able to have it detect when a person walks in front of the camera and in this talk I will walk through how I was able to set up an image detection model on a Raspberry Pi using TensorFlow.JS that is able to detect when I walk into room.
Have you seen cool image recognition projects and you want to learn how they were created? Are you a hobbyist that wants a cool project using a Raspberry Pi? Are you new and want a place to start? If you are any of these, this talk will be perfect.
In this talk, I will walk through using a trained neural network deployed to a Raspberry Pi to detect objects in a picture.
I will cover the ins and outs of a Jupyter Notebook. I will show how it is the perfect resource to teach coding or other code related tasks. I will show how I was able to use successfully proven learning styles (competency-based and problem-based) to teach machine learning.
Do you want to know how to add machine learning to your application? Do you have a trained model and want to add it to an Android application? If so, this talk is for you. I will walk through how to convert a TensorFlow/Keras model to TensorFlow Lite. I will then show how to add that model to your Android application.
Have you wanted to know how to add machine learning to your projects? Have you wanted to push your trained model to the cloud?
In this talk, I will cover how to take a trained TensorFlow/Keras model and host it in Azure.
Do you want to test all of your Entity Framework queries without setting up a test database? Do you want to test your API without leaving Visual Studio? Do you want to add integration tests to your CI build without adding another library? Do you want to "cheat" and hit 95%+ code coverage? In this talk, I will walk through using the WebApplicationFactory (Microsoft.AspNetCore.Mvc.Testing) library to spin up your API in memory. I will then show how to add an in-memory data connection. Finally, I will pull it all together and show a full integration test.
I am sure you have seen countless presentations about training a machine learning model but have you ever seen one that explains what to do next? You know, actually getting to use the model on something other than your personal computer? This is the talk that takes that final step.
I will discuss using TensorFlow.js to deploy your model to your web site like any other JS file. I will then get a little more fancy and show how you can take advantage of the scale of the Google Cloud and deploy it to the AI Platform.
Senior Software Engineer