• 2 years of experience with a variety of predictive modeling tools such as: neural networks, convolutional neural networks, Monte Carlo, boosting, random forest, PCA, and independent component analysis

  • Ability to work under tight deadlines after working in a fast-paced machine learning start-up as a data scientist (machine learning researcher)

  • Applied statistical analysis in a variety of fields such as: mobile apps, Li batteries, cancer research, statistical physics, corrosion, nanotechnology, industrial steel manufacturing, and electron microscopy

  • Proficient at programming in Python (3+ years), R, SQL, Git, AWS and working in a Linux environment

  • Published a Monte Carlo study on atomistic simulations and the work was accepted to the high-impact nanotechnology journal ACS Nano winning me $2000 USD from the University of Notre Dame’s contest


Masters of Applied Science (Thesis)                                                                                        

  • Canadian Centre for Electron Microscopy, McMaster University

  • Using convolutional neural networks to automate classification in electron spectroscopy

  • Li-ion battery project collaboration with Tesla Motors (Publication)

Bachelors of Materials Science and Engineering (B.Eng)                              

  • McMaster University, Canada

  • Graduated summa cum laude

  • Minor in sustainable & environmental engineering practices

  • Additional minor in chemistry



Email me (see Contact) for an up to date resume.