Jesse Tao

[email protected] | (970) 310-9988 | Denver, CO |https://github.com/jesseptao

I am an electrical engineer turned data scientist that strives to improve the communities around me through my passion in programming and math. Using a variety of web development and data analysis tools has allowed me to collect and store geographical data, build applications and generate automated pipelines that have actionable insights. By combining my various technical skills, I am able to create innovative tools with a positive impact that seamlessly translate to the real world.

SKILLS

Languages: C, C++, C#, MATLAB, Verilog, Assembly, Python, SQL, NoSQL, HTML, CSS, PHP, Javascript, Git

Packages: Scikit-learn, Pandas, Numpy, NLTK, BeautifulSoup, Tensorflow, OpenCV

Data Visualization: Seaborn, Matplotlib, Folium

Modeling: Statistical Modeling, Regression Models, Classification Models, Time Series Forecasting

PROJECTS

Spotify Recommender, General Assembly

Created a recommender model for Spotify that recommends new songs from new and less known artists based on what a user previously listened to. Compared songs using their audio files by transforming the audio file into MFCCs and taking the cosine similarity as well as calculating pairwise distances between songs using Spotify API's audio features call. Designed a web application so users can get recommendations as well as give feedback on the recommendations.

COVID-19 Vaccine Distribution, General Assembly

Worked with 3 other students in my cohort to predict the number of COVID-19 cases in each county in California using LSTM, ARIMA, and SARIMA models to improve vaccine distribution by allocating more vaccines to counties where outbreaks are predicted to happen. Built a web application with an interactive map and table to display the results of our findings.

Subreddit Analysis, General Assembly

Used various classification models and NLP in order to determine if a post came from either the Pokémon Go subreddit or The Silph Road subreddit to see if The Silph Road subreddit contains language that is more commonly used by veteran players of Pokémon players.

Ames, Iowa Real Estate Analysis, General Assembly

Predicted housing prices in Ames, Iowa by exploring a dataset with over 80 housing features. Engineered features such as one-hot encoding and interaction terms to build a linear regression model to accurately predict home prices and help prospective home buyers determine if they are paying a fair price for their house. Winner of Kaggle competition in my cohort with lowest root mean squared error for predicted home values.

EXPERIENCE

TaoPokéMap – Web Development Lead            August 2018-Present

Tao Innovation Labs           Fort Collins, CO

• Designed program to determine precise geographic location of desirable Pokémon that cannot be

done using the native Pokémon Go app

• Wrote Swift code to collect geographic coordinates of Pokémon in real-time
• Built/maintained MySQL database that contains live feed of Pokémon locations
• Built/maintained website using PHP, HTML, CSS, JavaScript allowing users to view Pokémon locations on a map
• Designed a Discord bot using Node.js to send notifications to users with Google Maps links

• Crowd funded over $15K to support website operations

Pulse – Firmware Engineering Lead               August 2017-May 2018

CU Engineering Senior Design Project            Boulder, CO

• Collaborated with team of 4 other senior engineering students to develop a low cost wearable

device to help visually impaired individuals detect eye-level objects

• My role in this project:

• Built firmware to control time of flight rangefinders and vibration motors using an

STM32F4 microcontroller

• Implemented existing image recognition model created with Tensorflow and OpenCV

on a Raspberry Pi

• Improved range of view of visually impaired individuals by over 50%

EDUCATION 

University of Colorado Boulder (CU)                                                                  Boulder, CO

Bachelor of Science, Electrical and Computer Engineering, GPA: 3.2/4.0                 August 2014- May 2018

General Assembly

Data Science Immersive Remote           November 2020-February 2021

• Strengthened data science skills in a 12 week immersive educational program covering: Python, exploratory data analysis, data cleaning, data visualization, regression models, classification

models, web scraping, APIs, NLP, advanced supervised learning, unsupervised learning, SQL,

time series analysis, data engineering, deep learning, and Bayesian statistics.

• Analyzed, cleaned, and visualized large data sets using Python. Used modeling and machine

learning techniques on these data sets to create projects that were presented to non-technical

and technical audiences.