Ricardo Matsumura Araujo
CS Professor at UFPel

About me

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. Data Science. Computational Social Science.

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Ricardo's Picture

Projects current and recent

  • Applications of Deep Neural Networks
    • Visual Computational Sociology, using urban images to infer sociological, epidemiological data
    • Text and image sentiment analysis
    • Cancer type classification from gene activation networks
    • Agricultural diseases diagnosis from images
  • Deep Neural Networks Theory and Limitations
    • Transfer Learning in Convolutional Neural Networks and Deep Reinforcement Learning
    • Improved kernel regularisers for Convolutional Neural Networks
  • Data Science
    • Student outcome prediction in distance learning courses
    • Missing link detection in researcher networks
    • Clustering researchers and suggesting project collaborations



Current Former
  • Pedro Ballester (undergrad)
  • Leonardo Ferreira (MSc)
  • Leandro Dias (MSc)
  • Lucas Nachtigall (MSc)
  • Marcelo Giesel (MSc)
  • Fabrício Ferreira (MSc)

Courses recent only

Machine Learning

Grad 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.

Algorithms and Data Structures II and III

Offered to Computer Science and Computer Engineer students at UFPel. Part II covers data structures (B-Trees, Tries, Hash Tables, Graphs), algorithm design (dynamic programming, greedy algorithms, divide and conquer) and analysis methodologies. Part III includes Probabilistic Algorithms, Complexity Classes, NP-Completeness, Approximate Algorithms, Public-Key Cryptography, Intro to Quantum Computing, Intro to Blockchain technology.

Foundations of Artificial Intelligence

Grad 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.

Introduction to Data Science

Offered to Computer Science and Computer Engineer students at UFPel. The course covers the basics of Data Science and its general workflow, including obtaining data, data cleaning, data visualization, inferential analysis, data modelling, machine learning and results communication.

Other activities

Programming Challenges

Programming Challenges are contests where teams of students compete to solve several challenges by programming. I have been coaching and helping organize regional sites for Maratona de Programação, part of the ACM ICPC, for over 6 years.

Maratona de Programação

Junior Company - Hut8

I helped founding in 2014 and am the current coordinator of the Hut8 - Computing Junior Company, a company working inside the university and operated by undergraduate students. The company provides service for local business and builds innovative products for the market. My role is to oversee general operations, help select and mentor profitable concepts. Spin-off companies include Indeorum and Hive.


CS Graduate Program

From 2015 to 2017 I acted as the CS Graduate Program Director at Federal University of Pelotas. I helped with the creation of our PhD course along with the Master course. In this role I oversaw 26 researchers and professors and over 100 students yearly.