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 unsupervised structured representation learning, inspired and supported by observations from neuroscience. In pursuit of this goal, during my PhD I have developed novel methods for probabilistic generative modeling which make use of biologically plausible mechanisms such as learned feedback connections and topographic organization to approximate otherwise analytically intractible solutions. In the long term, I hope to be able to answer the question of how transformations and invariances are learned and encoded in the brain, and further understand how the 2-dimensional structure of the cortical surface shapes how learning proceeds. More immediately, 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 AmsterdamSupervisor: Max Welling
Focus: Probabilistic Generative Models, Approximate Equivariance, Biologically Inspired Algorithms
M.S. Computer Science (2015 - 2017) University of California San DiegoSupervisor: Garrison Cottrell
B.S. Computer Science w/ Honors (2011 - 2015) California Institute of TechnologySupervisor: Yasser Abu-Mostafa
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)
- Paper: Experimental Realization of a Nonlinear Acoustic Lens with a Tunable Focus
- Gathered and analyzed waveforms from an acoustinc lens to determine optimal characteristics of interface materials.
Master’s Thesis Supervision
- Qinghe Gao: Modeling the Observed Domain-Specificity in the Visual Cortex using Topographic Variational Autoencoders (in submission)
- Samarth Bhargav: Geometric Priors for Disentangling Representations
- Noah van Grinsven:Spatio-Temporal Forecasting On Graphs With Incomplete Data
- Fiorella Wever: As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance
As Teaching Assistant
- Leren (Bachelor’s Machine Learning)
- Machine Learning 2 (Second Year Master’s)
- Data Visualization (D3.js)
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!