Gatsby Starter Procyon

Cristóbal Silva

ML Engineer @ Landing AI

Electrical Engineer, Universidad de Chile

cristobal _at_



For a complete up-to-date list, check out my Google Scholar

Conference Papers


Initialising Kernel Adaptive Filters via Probabilistic Inference

Iván Castro*, Cristóbal Silva*, Felipe Tobar

Best Paper Award

We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially.
* These authors contributed equally


Rapid Multivariate Resource Assessment

Fabián Soto, Mauricio Garrido, Gonzalo Díaz and Cristóbal Silva


We developed a tool integrating different visualizations and algorithms in order to make the geological resource assessment process faster. We focused mainly in three aspects: a software responsive in terms of usability, a software with high performance capabilities in terms of parallel and distributed execution of geostatistical algorithms, and a software that allows to re-execute the same resource assessment task in different time scales with little overhead for the user.

Undergraduate Thesis (spanish)


Modelamiento Semántico del Entorno de un Robot utilizando información RGB-D

Cristóbal Silva

Departamento de Ingeniería Eléctrica,
Universidad de Chile

The main contribution of this work is a review of Probabilistic Graphical Model literature and its applications on Computer Vision tasks, specifically Semantic Segmentation. In particular, this work serves as a guide on how to model images as undirected graphs and use a general purpose solver for optimizing the outputs of a classification model without additional data.

Presentations / Teaching Material

The following presentations were prepared for class lectures and reading groups.


Trends in ML

In this short series, I talked about two modern Deep Learning architectures:


Introduction to Deep Learning

Three lectures (in spanish) that were given for Bayesian Automatic Learning in Universidad de Chile:

Teaching Assistant

I have been Teaching Assistant for the following courses at Universidad de Chile

  • Probabilistic Machine Learning (2016)
  • Bayesian Automatic Learning (2016)
  • Robotics (2015)
  • Information Technologies (2015)
  • Communication Principles (2014)
  • Analysis and Design of Electrical Circuits
    (2013, 2014)

Most of my work in these courses consisted on developing teaching materials, evaluations and solution guides. For Machine Learning related courses, I had a major role in designing the course syllabus and class slides with the professor (Bayesian Automatic Learning), as well as creating programming assignments for the students (Probabilistic Machine Learning).

Summer Schools

Independent work I did after finishing my undergraduate thesis, mostly for fun


A Summary of Capsule Networks with an Application on FashionMNIST

Cristóbal Silva



Analysis of MRF for Semantic Segmentation of Indoor Scenes

Cristóbal Silva