Bruno Castro da Silva

Associate Professor

Institute of Informatics
Federal University of Rio Grande do Sul (UFRGS)
(UFRGS has once again been ranked #1 university in the country!)

Av. Bento Goncalves 9500 - Campus do Vale - Bloco IV
Office: 225/43424 (72)
Porto Alegre, Brazil, 91501-970

bsilva@inf.ufrgs.br



I am an associate professor at the Institute of Informatics at the Federal University of Rio Grande do Sul (UFRGS), in Brazil.

My research interests lie in the intersection of machine learning, reinforcement learning, optimal control theory, and robotics, and include the construction of reusable motor skills, active learning, efficient exploration of large state-spaces, and Bayesian optimization applied to control.

Here is my curriculum vitae and my Curriculo Lattes.



Prior to being a professor at UFRGS, I was as a postdoctoral associate at the Aerospace Controls Laboratory at MIT LIDS. I received my Ph.D. in Computer Science from the University of Massachusetts, working under the supervision of Prof. Andrew Barto, in 2014.

I completed my Masters Degree in Computer Science in 2007 under the supervision of Prof. Ana Bazzan at the Federal University of Rio Grande do Sul, in Brazil. I completed my B.S. in Computer Science cum laude at that same university in 2004.

I have worked, in different occasions from 2011 to 2018, as a visiting researcher at the Laboratory of Computational Embodied Neuroscience, in the Istituto di Scienze e Tecnologie della Cognizione, in Rome, developing novel control algorithms for a humanoid robot.

In the Summer of 2014 I worked at Adobe Research, where I developed large-scale optimization techniques for the construction of high-performance features for digital marketing optimization.

From 2011 to 2015 I collaborated with Prof. Victor Lesser on the problem of designing organizationally adept agents and on coordinating learning through emergent distributed supervisory control.

Some publications

  1. Ramos, G. O.; da Silva, B.C.; Bazzan, A.L.C.
    Learning to Minimise Action Regret in Route Choice.
    Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017). Sao Paulo, Brazil, 2017.

  2. Grunitzki, R.; da Silva, B.C.; Bazzan, A.L.C.
    A Flexible Approach for Designing Optimal Reward Functions.
    Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017). Sao Paulo, Brazil, 2017.

  3. Thomas, P.S.; da Silva, B.C.; Dann, C.; Brunskill, E.
    Energetic Natural Gradient Descent.
    Proceedings of the 33rd International Conference on Machine Learning (ICML 2016). New York, USA, 2016.

  4. Garant, D.; da Silva, B.C.; Lesser, V.; Zhang, C.
    Context-Based Concurrent Experience Sharing in Multiagent Systems. [paper+supplemental material]  [extended abstract].
    Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017). Sao Paulo, Brazil, 2017.

  5. Stefanello, F.; da Silva, B.C.; Bazzan, A.L.C.
    Using Topological Statistics to Bias and Accelerate Route Choice: preliminary findings in synthetic and real-world road networks.
    Proceedings of the 9th International Workshop on Agents in Traffic and Transportation (ATT) @ the
    25th International Joint Conference on Artificial Intelligence (IJCAI 2016).
    New York, USA, 2016.

  6. da Silva, B.C.; Konidaris, G.; Barto, A.G.
    Active Learning of Parameterized Skills.
    Proceedings of the 31st International Conference on Machine Learning (ICML 2014). Beijing, China, 2014.

  7. da Silva, B.C.; Baldassarre, G.; Konidaris, G.; Barto, A.G.
    Learning Parameterized Motor Skills on a Humanoid Robot.   [video].
    Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014). Hong Kong, China, 2014.

  8. da Silva, B.C.; Konidaris, G.; Barto, A.G.
    Learning Parameterized Skills.
    Proceedings of the 29th International Conference on Machine Learning (ICML 2012). Scotland, 2012.

  9. da Silva, B.C.; Barto, A.G.
    TD-Δπ: A Model-Free Algorithm for Efficient Exploration.
    Proceedings of the 26th Conference on Artificial Intelligence (AAAI 2012). Canada, 2012.

  10. da Silva, B.C.; Barto, A.G.; Kurose, J.
    Designing Adaptive Sensing Policies for Meteorological Phenomena via Spectral Analysis of Radar Images.
    Technical Report UM-CS-2012-006, Department of Computer Science, University of Massachusetts Amherst. USA, 2012.

  11. Corkill, D.; Zhang, C.; da Silva, B.C.; Kim, Y.; Zhang, X.; Lesser, V.
    Biasing the Behavior of Organizationally Adept Agents.
    Proceedings of the 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013). USA, 2013.

  12. Bazzan, A.L.C.; Oliveira, D., da Silva, B.C.
    Learning in Groups of Traffic Lights.
    Journal of Engineering Applications of Artificial Intelligence. 2010.

  13. Bazzan, A.L.C.; da Silva, B.C.
    Distributed Constraint Propagation for Diagnosis of Faults in Physical Processes.
    Proceedings of the 6th International Joint Conference On Autonomous Agents And Multiagent Systems (AAMAS 2007). USA, 2007.

  14. da Silva, B.C.; Basso, E.W.; Bazzan, A.L.C.; Engel, P.M.
    Dealing with Non-Stationary Environments using Context Detection.
    Proceedings of the 23rd International Conference on Machine Learning (ICML 2006). USA, 2006.

  15. da Silva, B.C.; Basso, E.W.; Bazzan, A.L.C.; Engel, P.M.
    Improving Reinforcement Learning with Context Detection.
    Proceedings of the 5th International Joint Conference On Autonomous Agents And Multiagent Systems (AAMAS 2006). Japan, 2006.

  16. da Silva, B.C.; Oliveira, D.; Basso, E.W., Bazzan, A.L.C.
    Adaptive Traffic Control with Reinforcement Learning.
    Proceedings of the 4th Workshop on Agents in Traffic and Transportation (ATT 2006). Japan, 2006.

  17. da Silva, B.C.; Bazzan, A.L.C.; Oliveira, D.; Lopes, F.; Andriotti, G.
    ITSUMO: an Intelligent Transportation System for Urban Mobility.
    Lecture Notes in Computer Science. Springer-Verlag, 2004.

  18. Almeida, L.; da Silva, B.C.; Bazzan, A.L.C.
    Towards a physiological model of emotions: first steps.
    Proceedings of the 2004 AAAI Spring Symposium Series. USA, 2004.