Professor of Computer Science at the University of Lisbon. My professional interests, besides teaching, are focused on programming languages, Bayesian statistics, machine learning, and combinatorial game theory.
Main hobbies: programming, seeing movies, playing abstract games, and reading.
Favorite areas of thought: Computation, Probability, Ecology, Ethics, Epistemology, History.
Coding chops:
Advent of Code 485*
Project Euler 177
Leetcode 410 (118/227/65)
Topics in Python
Jupyter notebooks that range from Statistics and Mathematics to Programming, including:
Resampling - notes about permutation tests to estimate answers to probability problems and propose alternatives to several statistical tests
Optimization - brief notes about convex optimization and how to apply it with Python
Concatenative Programming - introduction to tacit programming and concatenative programming using Python high-order functions
Corecursion - introduction to codata and corecursion, and how to use it in Python
Functors & Monads - show implementations and use cases of Functors, Applicative Functors and Monads as Python interfaces
Probabilistic Programming - introduction to probabilistic programming
Differentiation - computing derivatives via symbolic differentiation, numerical differentiation and automatic differentiation
Topics in R
R markdowns dealing with lots of concepts from R itself, Bayesian Statistics & Machine Learning, including:
Combinatorics,
Fourier Transform,
Maximum Entropy,
Power Laws,
Noise,
Distributions,
Approximate Bayesian Computation,
A BUGS tutorial,
Convex Optimization using CVXR,
Heavy Tails,
Bayesian Decision Theory,
Circular Statistics,
Expectation-Maximization,
Laplace Approximation,
Statistical Computation and Simulation,
Variational Inference.
