CS Professor at the Center for Technological Advancement (CDTec) in the Federal University of Pelotas (UFPel). PhD and Master Degrees in Computer Science (from UFRGS). Lecturing at the Computer Science, Computer Engineering undergrad courses and the Graduation Program in Computer Science.
Machine Learning. Computational Social Science. Data Analysis. Web Development.
We aim at using the popular Microsoft Kinect device to passively capture biometric data other than face recognition and use it for robust person identification. We are currently looking into using anthropometric information, both from static and dynamic scenarios (walks, gestures).
Product of my PhD thesis, Memetic Networks are a set of search heuristic based on a model of how people consume and communicate information. It uses the concept of memes and how they are exchanged, combined and modified through communication and reasoning over a social network. While initially proposed as a model to experiment with network properties and their effect on problem-solving performance, Memetic Networks were shown to be competitive with other search heuristics in some optimization tasks. This project is partly funded by FAPERGS and CNPq.
We aim at creating a priority inbox for Twitter by learning the users' preferences and automatically ranking incoming tweets, surfacing those considered important. Moreover, we consider ways to inject relevant tweets from not-followed users (or even external sources) into one's stream. The challenge is leveraging social attributes and other meta-information while making the best use of a very limited amount of textual information. This project was funded by Bolsa Pesquisa UOL and is largely inactive since then (Twitter has been rolling some of these features to the end users).
MS level at PPGC-UFPel (typically on second semesters). This course provides an introduction to Machine Learning, common algorithms, validation methodologies and a bit of computational learning theory. It is very project-oriented, requiring students to implement a few and test several algorithms on different scenarios. Students must know beforehand some popular programming language (Python is preferred) or an appropriate environment (R, Matlab). Knowledge of probability and statistics is a plus.
Offered to Computer Science and Computer Engineer students at UFPel. It covers data structures (B-Trees, Tries, Hash Tables, Graphs), algorithm design (dynamic programming, greedy algorithms, divide and conquer) and analysis methodologies.
MS level at PPGC-UFPel (typically on first semesters). This course provides an overview of the main concepts and techniques behind Artificial Intelligence. Exhaustive and heuristic searches, knowledge representation, multi-agent systems, evolutionary computation, learning. Students are required to have extensive knowledge of some popular programming language, algorithms and data structures, as this course is very project-oriented.