Alejandro Calle-Saldarriaga

Alejandro Calle-Saldarriaga

Would describe myself as an Statistician or data scientist, whatever flavour you prefer. See CV section for full bio. Research interests: FDA, Robust/nonparametric statistics, statistical learning.

Talks and presentations

A homogeneity test based on depth-depth plots for functional data

July 17, 2019

Conference talk, Universidad del Norte. XXIX Simposio Internacional de Estadística, Barranquilla, Colombia

Abstract: One of the classic concerns in statistics is determining if two samples come from the same population, i.e., homogeneity testing or two-sample testing. In this paper, we propose a homogeneity test in the context of Functional Data Analysis, adopting an idea from multivariate data analysis: the data depth plot (DD-plot). This DD-plot is a generalization of the univariate Q-Q plot (quantile-quantile plot). We propose some statistics based on these DD-plots, and we use bootstrapping techniques to estimate their distributions. We simulate our test’s finite-sample size and power, obtaining better results than other homogeneity tests proposed in the literature. Finally, we illustrate the procedure in samples of real heterogeneous data and get consistent results.

Functional data analysis: An introduction

September 17, 2017

Conference talk, Universidad EAFIT. Days of applied science: Big data, Medellín, Colombia

A short introduction to Functional Data Analysis in the context of Big Data, given at EAFIT’s days of applied science.

A set theory formalization

July 27, 2017

Seminar talk, Universidad EAFIT. Logic and Computation seminar., Medellín, COlombia

Abstract: Set theory has been is one of the most important fields for the foundations of mathematics, so having a formalization (i.e. a translation of its axiom and theorems into some proof assistant) is desirable. In this talk, we present a set theory formalization of the Z axioms and some theorems using Agda. Slides

A non-learning premise selection algorithm for Apia

September 14, 2016

Seminar talk, Universidad EAFIT. Logic and Computation seminar, Medellín, Colombia

Abstract: Automatic theorem provers need to recieve a reasonably small number of premises in order for them to be able to prove a given conjecture with limited processor time. In large theories this is not always possible, as many irrelevant clauses are added to the premises. In order to solve this problem, premise selection algorithms have emerged in the past few years, some using non-learning methods and others using learning ones. Our goal in this project is to implement a non-learning premise selection algorithm for Apia, in order to further link the interactive theorem prover Agda with Authomatic theorem provers. Slides