About Me

me

My first name is Thomas, but most people call me by my middle name – Andy. I’m currently a third-year PhD student supervised by Max Welling at the University of Amsterdam. My work is focused on probabilistic generative models inspired by observations and theories from neuroscience. My current interests broadly include: developing unsupervised methods for learning approximately equivariant & invariant representations, exploring the computational benefits of topographically organized representations, and improving techniques for efficiently training deep latent variable models.

Find my full C.V. here

Find my publication list on Google Scholar

Education (click to expand)

Ph.D. Machine Learning (2018 - expected 2022) University of Amsterdam Supervisor: Max Welling
Focus: Probabilistic Generative Models, Approximate Equivariance, Biologically Inspired Algorithms
M.S. Computer Science (2015 - 2017) University of California San Diego Supervisor: Garrison Cottrell
Thesis: Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing
B.S. Computer Science w/ Honors (2011 - 2015) California Institute of Technology Supervisor: Yasser Abu-Mostafa

Experience

Intel Nervana AI Lab (2016 - 2018)
  • Deep Learning Data Scientist (Sept. 2017 - Sept. 2018)
  • Algorithms Engineer Intern (June 2016 - June 2017)
Data Science for Social Good (Summer fellow 2015)
Lyve Minds Inc. (Analytics Engineering Intern Summer 2014)
  • Developed supervised learning algorithm for automatic editing and summarization of user generated handheld video based on predicted level of interest.
California Institute of Technology (Undergraduate Researcher 2012)

Teaching

Master’s Thesis Supervision

As Teaching Assistant

  • Leren (Bachelor’s Machine Learning)
  • Machine Learning 2 (Second Year Master’s)
  • Data Visualization (D3.js)

Personal

Privately, I enjoy cooking (working on my vegan tacos), running, and playing with my gymnastics rings. I was also an organizing member of the Inclusive AI group at the UvA whose goal is to reduce harmful bias (both algorithmic and human) in the field of machine learning. Please feel free to email me if you have any questions!

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