Sean Wade

Data Science · Machine Learning · Mathematics

I am a passionate AI researcher who lives at the intersection of CS, math, and statistics. I love to tinker and prototype new ideas. I work in everything from autonomous drone computer vision to large scale data pipelines/ML.


AI Platform Engineer

  • Developed enterprise solutions for ML development lifecycle (development, production, monitor)
  • Extended Azure SDK to incorporate open source MLflow projects to pipelines
  • Helped develop prototype for HoloLens 2 augmented reality virtual assistant
June 2019 - Present

Data Scientist

  • Lead team in research and development of recommender system for Disney World
  • Integrated many big data and real time processing tools like Hadoop, Spark, Kafka and NiFi
  • Created live dashboards to monitor and visualize large data pipelines and algorithms
January 2018 - June 2018

Computer Vision / Machine Learning Engineer

Loveland Innovations
  • Used drone imaging to construct 3D models of buildings and homes
  • Created novel algorithms to segment 3D models, detect damage, and identify roof features
  • Applied and scaled machine leaning research to industry
March 2017 - January 2018

Research Intern

MIT Lincoln Labs
  • Researched within Lidar and active optical systems
  • Created pipeline to process massive multidimensional point clouds
  • Implemented and analyzed many clustering and machine learning techniques
June 2016 - August 2016

Software Engineer

EyeTech Digital Systems
  • Developed mathematical models to highly improve eye tracking algorithms
  • Optimized embedded computer vision algorithms with over 10x speedup
April 2016 - June 2016


Brigham Young University

Masters of Science
Computer Science - Machine Learning / AI
August 2017 - Present
Bachelor of Science
Applied Mathematics
January 2014 - April 2017

Reinforcement Learning

Currently researching the combination of control theory and reinforcement learning. In addition looking at choosing decision policies over adaptive temporal horizons.


Researched machine learning techniques for survival analysis, disease prediction, and cost analysis in healthcare. Applied new methods for training gradient boosted trees and recurrent neural networks to large healthcare datasets. Also developed high dimensional disease embeddings based on the field of natural language processing.

  • Forward Thinking: Building and Training Neural Networks One Layer at a Time
  • Code2Vec: Embedding and Clustering Medical Diagnosis Data

Graph Theory

Explored graph symmetries and automorphisms. This is used to model the redundancies and patterns of large scale networks, allowing for both forecasting and separation of dynamical systems.

All the Skills

Programming Languages & Tools
  • Deep Learning Libraries (Tensorflow, PyTorch, ...)
  • LOTS of data (Hadoop, NiFi, ...)
  • Data visualization (web dashboards, jupyter notebooks, ...)


  • National Merit Scholar
  • 2015 & 2017 Distinguished Undergraduate in Mathematics
  • MIT Innovator Finalist
  • BYU Data Science Competition