About Me

I'm Miguel González Duque, a mathematician from the Universidad Nacional de Colombia. I'm mostly interested in the intersection between probabilistic modeling and geometry.

I'm currently a PhD student at the Creative AI Lab at the IT University of Copenhagen, under the supervision of Prof. Sebastian Risi. We are developing systems that create and adapt game content to users. I have also started collaborating with Søren Hauberg's lab at DTU, working on generative modelling and differential geometry. I did a 6-month PhD Sabbatical at the Bosch Center for AI working with Leonel Rozo.

Feel free to contact me at miguelgondu(at)gmail(dot)com.

Some publications

Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds

Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour

We embed humanoid poses in hyperbolic space, and have the latent distances match a robotics taxonomy. Main work done by Noémie Jaquier.

arxiv

Mario Plays on a Manifold

Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg, Sebastian Risi.

We train 2-dimensional VAEs on Mario and Zelda and define a graph in latent space only where playable levels live. This lets us sample and interpolate safely.

arxiv - publication

Variational Neural Cellular Automata

Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran, Sebastian Risi.

We used an NCA as the decoder of a Variational Autoencoder. (Main work done by Rasmus Berg Palm; I only played a secondary role.)

arxiv - publication - code

Pulling back information geometry

Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg

We induced geometries in latent space by pulling back the Fisher-Rao metric.

arxiv - publication

Fast Game Content Adaptation Through Bayesian-based Player Modelling

Miguel González-Duque, Rasmus Berg Palm, Sebastian Risi.

We applied a Bayesian Optimization approach to dynamically adjusting difficulty.

arxiv - publication - code

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Miguel González-Duque, Rasmus Berg Palm, David Ha, Sebastian Risi.

We used Gaussian Processes and Bayesian Optimization to find good levels for one bot using a prior from another bot.

arxiv - publication - code

Learning a Behavioral Repertoire from Demonstrations

Niels Justesen, Miguel González-Duque, Daniel Cabarcas Jaramillo, Jean-Baptiste Mouret, Sebastian Risi

We conditioned a StarCraft2 bot on strategies and optimized for the best strategy. This image was made by Niels Justesen.

arxiv - publication

Neural Networks that express multiple strategies in the video game StarCraft 2

Miguel González-Duque.

My M.Sc. Thesis (Mathematics at UNAL-Med). During my first stay at ITU, I collaborated with Niels, Sebastian and Daniel Cabarcas on applying dimensionality reduction to strategies, and creating bots for StarCraft 2.

publication