Spokane-Coeur d'Alene Area
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About

I am a Data Scientist and Data Science manager with professional experience working in…

Articles by Tyler

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Experience & Education

  • Itron, Inc.

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Volunteer Experience

  • ParaSport Spokane Graphic

    Athlete, Mentor

    ParaSport Spokane

    - Present 5 years 11 months

    Health

  • Co Organizer

    PyData Denver

    - 9 months

    Science and Technology

    Helping organize the once-monthly MeetUp for data scientists in the Denver area. https://www.meetup.com/PyData-Denver/

Publications

  • White Paper: Using Machine Learning to Improve Demand Response Forecasts

    Comverge, Inc

    The utility industry, like many industries, is currently undergoing a data revolution. Metering infrastructure is dramatically improving, data storage costs are plummeting, and computing power is still following Moore’s famous law of exponential improvement. Yet many utilities are unable or unsure about how to take advantage of this modern-day gold. We are tackling ways to turn this flood of data into insights that promise to help our customers improve forecasting of demand response (DR)…

    The utility industry, like many industries, is currently undergoing a data revolution. Metering infrastructure is dramatically improving, data storage costs are plummeting, and computing power is still following Moore’s famous law of exponential improvement. Yet many utilities are unable or unsure about how to take advantage of this modern-day gold. We are tackling ways to turn this flood of data into insights that promise to help our customers improve forecasting of demand response (DR) control events. This will increase efficiency, help with targeting, and ultimately improve utilities’ bottom lines.

    See publication

  • Communicating data science: An interview with a storytelling expert

    No Free Hunch (Kaggle blog)

    "To kick off this series on communicating data science, I interview Tyler about how he uses his skills in data visualization and effective reporting to collaborate and influence in his career. His advice to those who are talented at rising to the top of Kaggle's leaderboard, but need help finding their voice when it comes to communicating the insights in their ensemble? Read extensively outside of your domain and listen to stand-up comedy!"

    See publication

  • Honors Thesis: Modeling Airflow Around a Racing Wheelchair

    The University of Arizona Honors College

    A thesis submitted to the Honors College in partial fulfillment of the bachelors degree with honors in engineering mathematics.

    See publication

Patents

  • Electric vehicle distributed energy resource management

    Issued US12330523B2

    A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and…

    A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and generate, based on the reconciled EV charging information and the EV telematics data, models for at least one of continuous EV load prediction, electrical vehicle supply equipment (EVSE detection), and/or optimization for at least one of aggregated load, load per feeder, or maximum revenue for time-of-use tiers.

    Other inventors
    See patent

Projects

  • Kaggle - How Much Did It Rain II

    This is my work for the Kaggle: How Much Did it Rain? II competition.

    I completed this as part of the University of Washington Professional and Continuing Education's Data Science Certificate class #3 of 3. This is the final project.

    See project

  • Denver B-Cycle 2014 Ridership

    This is my final project for University of Washington's Methods for Data Analysis class, course #2 of 3 in the Data Science Certificate program. This project looks at public data from the Denver B-cycle program, which is merged with distance data from Google Maps and weather data from forecast.io.

    See project

  • Data Visualization with D3.js Final Project

    This visualization is an animation that shows that people in most countries increased their daily caloric consumption, as well as average body mass index (BMI), between the years 1990 and 2007. The visualization shows each country as a "bubble" on a scatterplot, where the size of the bubble is related to that country's Gross Domestic Product (GDP) per person. From the chart, it is apparent that in general the richest countries have the highest daily consumption, as well as the highest BMI, and…

    This visualization is an animation that shows that people in most countries increased their daily caloric consumption, as well as average body mass index (BMI), between the years 1990 and 2007. The visualization shows each country as a "bubble" on a scatterplot, where the size of the bubble is related to that country's Gross Domestic Product (GDP) per person. From the chart, it is apparent that in general the richest countries have the highest daily consumption, as well as the highest BMI, and a careful eye may discern that the richest countries have increased their consumption the most over the 18 years. In comparison, the poorest countries, especially those in Sub-Saharan Africa, appear to still be struggling with food supply, and may be getting left behind as the richer countries become more and more overweight.

    See project

  • Identifying Fraud from Enron Email

    The goal of this project was to use machine learning to identify persons of interest (POIs) in the Enron corporate fraud case. We were given a dataset with 146 data points (i.e. "people"), each of which has 21 features. The features were financial features (such as salary, bonus, and stock options) and email features (such as number of messages sent, messages sent to POIs, and number of messages received). Of the 146 people in the data set, there were 18 POIs. Using machine learning to identify…

    The goal of this project was to use machine learning to identify persons of interest (POIs) in the Enron corporate fraud case. We were given a dataset with 146 data points (i.e. "people"), each of which has 21 features. The features were financial features (such as salary, bonus, and stock options) and email features (such as number of messages sent, messages sent to POIs, and number of messages received). Of the 146 people in the data set, there were 18 POIs. Using machine learning to identify the POIs is useful because of complexity of the data set. It allows us to try to find patterns to detect POIs. In this way, we can create a model that then may help us identify POIs from new data -- if a new person and their data are sent through the model, the model can then identify whether that new person may be a POI or not.

    See project

  • MongoDB Final Project

    This report details my acquisition, cleanup, and exploration of the OpenStreetMap.org map data for the majority of Summit County, Colorado, to include Vail in Eagle County. I focused on recreational items listed within the map, rather than focusing as heavily on addresses, business, and other amenities. In large part this was due to the fact that I found that the map was fairly complete with respect to listing mountain peaks, ski pistes, and ski chair lifts. Some slight cleanup was required…

    This report details my acquisition, cleanup, and exploration of the OpenStreetMap.org map data for the majority of Summit County, Colorado, to include Vail in Eagle County. I focused on recreational items listed within the map, rather than focusing as heavily on addresses, business, and other amenities. In large part this was due to the fact that I found that the map was fairly complete with respect to listing mountain peaks, ski pistes, and ski chair lifts. Some slight cleanup was required. However, the map was very incomplete with respect to listing businesses and other amenities.

    See project

  • The Incredible Word Oracle

    The word prediction app originally built for the Coursera/Johns Hopkins University Data Science Capstone project.

    See project

  • Calculating Calories Burned During a Workout

    Calculate Your Workout Calories

    The Calories Burned Calculator, available at the Shiny Apps Page estimates the number of calories that the user burned during a workout.

    User enters Gender, Age, and Weight (lb or kg).
    If workout was steady-state, user enters Duration and Average Heart Rate, and the resulting calories burned appears.
    If workout was multi-stage (legs of varying intensity), user can use “Add Leg” feature.
    Calculator tracks total elapsed time for workout and…

    Calculate Your Workout Calories

    The Calories Burned Calculator, available at the Shiny Apps Page estimates the number of calories that the user burned during a workout.

    User enters Gender, Age, and Weight (lb or kg).
    If workout was steady-state, user enters Duration and Average Heart Rate, and the resulting calories burned appears.
    If workout was multi-stage (legs of varying intensity), user can use “Add Leg” feature.
    Calculator tracks total elapsed time for workout and total calories burned during workout.
    Displays a chart of Calories Burned vs. Elapsed Time.
    Displays table of stats for user.

    See project

  • Rpubs Projects

    - Present

    RPubs project directory for various projects I have done in R.

    See project

Test Scores

  • GRE

    Score: 337/340

    Verbal: 169/170, Quant: 168/170, Writing: 4.5/6.0

Languages

  • English

    Native or bilingual proficiency

  • French

    Elementary proficiency

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