I am Assistant Professor at the Mechatronics group at the Escola Politecnica, Universidade de Sao Paulo. This group is an inter-disciplinary effort to research in Automation, Robotics, Artificial Intelligence and Industrial Management (among other things!). I have a background on Electrical Engineering, and do most of my research in Computer Science issues.

I am currently working on three different lines of research; these lines mostly reflect my past and present interests.

First, I explore the theory of sets of probabilities. There is not a single, stable name for this theory: some people use "theory of imprecise probabilities"; others say "theory of credal sets", or "Quasi-Bayesian theory", or "theory of lower expectations", or ... several other names. I believe this theory is the right tool to model statistical uncertainty, and will ultimately be the unifying foundation for inference and decision-making. To know more about the theory, you can look at my explanation of it, and also look at web sites for the First and the Second Symposia on Imprecise Probabilities and Their Applications (which I co-organized) and the Imprecise Probabilities Project (which I co-edit). As a result of the First ISIPTA, a book and two special issues were edited:

- G. de Cooman, F. G. Cozman, S. Moral, P. Walley (editors).
*Proceedings of the First Symposium on Imprecise Probabilities and Their Applications*, ISBN 90-805034-1-X, Ghent, Belgium, 1999. - F. G. Cozman, S. Moral (editors).
Special issue: Reasoning with Imprecise Probabilities,
*International Journal of Approximate Reasoning*, vol. 24, n. 2/3, 2000. - F. G. Cozman, S. Moral (editors).
Special issue: Models for Imprecise Probabilities
and Partial Knowledge,
*International Journal of Uncertainty, Fuzziness and Knowledge-based Systems*, vol. 8, n. 3, 2000.

I work both on foundational and algorithmic issues (with emphasis on the later). Basically, I'm interested in efficient algorithms to obtain posterior quantities; a big part of the work can be grasped through the papers

- F. G. Cozman.
Credal networks,
*Artificial Intelligence Journal*, vol. 120, pp. 199-233, 2000. - F. G. Cozman.
Computing posterior upper expectations,
*International Journal of Approximate Reasoning*, vol. 24, pp. 191-205, 2000. - F. G. Cozman.
Calculation of Posterior Bounds Given Convex Sets of
Prior Probability Measures and Likelihood Functions,
*Journal of Computational and Graphical Statistics*, vol. 8(4), pp. 824-838, 1999.

- F. G. Cozman.
Computing Posterior Upper Expectations,
*First International Symposium on Imprecise Probabilities and Their Applications (ISIPTA)*, pp. 131-140, Ghent, Belgium, June/July, 1999.

- F. G. Cozman.
Irrelevance and Independence Relations in Quasi-Bayesian
Networks,
*XIV Conference on Uncertainty in Artificial Intelligence*, pp. 89-96, Madison, Wisconsin, United States, July 1998. - F. Cozman.
Robustness
analysis of Bayesian networks with
local convex sets of distributions,
*Proc. Thirteen Conference Uncertainty in Artificial Intelligence*, Rhode Island, 1997.

In connection with credal networks, I have developed algorithms for the JavaBayes system, where I explore robust inferences with credal networks, using both local and global perturbations. I'm also interested in concepts and properties of irrelevance/independence connected to the theory of sets of probabilities. The following papers look at some basic concepts in this area:

- F. G. Cozman.
Separation
Properties of Sets of Probability Measures.
*XVI Conference on Uncertainty in Artificial Intelligence*, pp. 107-115, San Francisco, California, July 2000. - F. G. Cozman.
Irrelevance and Independence Axioms in Quasi-Bayesian Theory,
*European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)*, London, England, published in Symbolic and Quantitative Approaches to Reasoning with Uncertainty, A. Hunter e S. Parsons (eds.), pp. 128-136, Springer, July, 1999.

I have also pursued some different directions, looking at the problem of sequential-decision making associated with observations, and also exploring the possibility of learning convex sets of probability from data:

- F. Cozman and E. Krotkov.
*Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem*, Proc. Twelfth Conference Uncertainty in Artificial Intelligence, pp. 186-193, 1996.

- F. Cozman and L. Chrisman. Learning Convex Sets of Probability from Data, Technical Report CMU-RI-TR-97-25, Robotics Institute, Carnegie Mellon University, Pittsburgh, 1997.

Second, I am quite interested in graphical models for uncertainty modeling, particularly Bayesian networks. I develop the JavaBayes system, a general purpose inference engine for graphical models; the engine can generate posterior probabilities and expectations for probabilistic models represented as directed acyclic graphs. The system is distributed freely (under the GNU license) in the spirit of fostering teaching and research. JavaBayes is now used in many university and research labs around the world. A summary is:

- F. G. Cozman.
The JavaBayes system,
*The ISBA Bulletin*, vol. 7, n. 4, pp. 16-21, 2001 (invited publication without referreing process).

- F. G. Cozman.
Generalizing Variable Elimination in Bayesian
Networks,
*Proceedings of the IBERAMIA/SBIA 2000 Workshops (Workshop on Probabilistic Reasoning in Artificial Intelligence)*, pp. 27-32, Editora Tec Art, São Paulo, Brazil, 2000.

While JavaBayes is a complete system, with graphical interface, parsers, etc, I've been investigating a system that is more geared towards the needs of embedded systems. The EBayes project is an effort to produce a lightweight Bayesian network engine that is appropriate to the growing market of embedded devices. Some preliminary tests are described at

- F. T. Ramos, F. Mikami, F. G. Cozman.
Implementação de Redes Bayesianas em Sistemas
Embarcados.
*Proceedings of the IBERAMIA/SBIA 2000 Workshops (Workshop on Probabilistic Reasoning in Artificial Intelligence)*, pp. 65-69, Editora Tec Art, São Paulo, Brazil, 2000.

Finally, I am interest in applying sensitivity analysis techniques (from the realm of robust Statistics) to Bayesian networks. I have been developing techniques that use the theory of sets of probabilities as a tool for the assessment of sensitivity in graphical statistical models. A preliminary effort is:

- F. G. Cozman.
Sensitivity and Robustness Analysis of Bayesian Networks,
*IV Simpósio Brasileiro de Automação Industrial*, pp. 251-255, São Paulo, São Paulo, Brazil, September, 1999.

Third, I really like to work with mobile robots and similar devices. I have always tried to spend some time building new friends out of motors and computers.

Right after my undergraduate course, I took a Master of Engineering in Brazil, and worked in the first brazilian mobile robot, called Ariel. We produced a complete system, from the mechanical structure to the planning software; the result was very impressive and we ended up showing it off in the Jornal da Globo (Brazil's second most important TV news source). Unfortunately, that material is not online. Here are two significant papers, perhaps of historic value:

- F. G. Cozman; P. E. Miyagi.
*Trajectory Controller for a Mobile Robot using Optimal Control*, XI Congresso Brasileiro de Engenharia Mecânica, 3:537-540, São Paulo, SP Brasil, 1991. - J. C. Adamowski; M. G. Simões; F. G. Cozman.
*Desenvolvimento de um Robô Móvel*, VIII Congresso Brasileiro de Automática, Belém, 1990; selected for IV Congreso Latinoamericano de Control Automatico, Puebla Mexico, 1990; also presented at IV Congresso Nacional de Automação Industrial, pp. 209-212, São Paulo, SP Brasil, 1990.

I worked, for two years, in the Lunar Rover project during my PhD years at Carnegie Mellon. My main contribution to the Lunar Rover project was the Viper system, a piece of technology that was used in the Atacama mission. The Viper system, estimates position from a stream of images, by matching images to a previously constructed map of the environment. The estimator builds an occupancy map for the position of the robot; the catch is that the occupancy maps actually represents a full density ratio familiy of distributions which generate both the estimates and the confidence on the estimates. The system is described at

- F. G. Cozman, E. Krotkov, C. E. Guestrin.
Outdoor Visual Position Estimation for Planetary Rovers,
*Autonomous Robots*, vol. 9, pp. 135-150, 2000.

- F. Cozman; E. Krotkov.
*Automatic Mountain Detection and Pose Estimation for Teleoperation of Lunar Rovers*, Proc. of the International Conference on Robotics and Automation, pp. 2452-2457, Albuquerque, New Mexico, 1997. Also published in*Experimental Robotics V*, Lecture Notes in Control and Information Sciences 232, pp. 207-215, Alicia Casals e Anibal T. de Almeida (eds.), Barcelona, Spain, June (15-18) 1997.

The vision algorithms developed for the Viper system reported in the following papers.

- C. Guestrin; F. G. Cozman; E. Krotkov. Fast Software Image Stabilization with Color Registration, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 19-24, Victoria, Canada, October, 1998.
- C. Guestrin; F. G. Cozman; M. G. Simões.
*Industrial Applications of Image Mosaicing and Stabilization*, Second International Conference on Knowledge-based Intelligent Electronic Systems, pp. 174-183, Adelaide, Australia, April 1998.

- R. Simmons; E. Krotkov; L. Chrisman; F. Cozman; R. Goodwin;
M. Hebert; L. Katragadda; S. Koenig; G. Krishnaswamy; Y. Shinoda; W.
Whittaker; and P. Klarer.
*Experience with Rover Navigation for Lunar-Like Terrains*, Proceedings of the Conference on Intelligent Robots and Systems (IROS), pages 441-446, 1995.

I also worked on a few other problems.

Some years ago I produced a line linker based on the Akaike Information Criterion (AIC), which was distributed in the net. That code is probably too old to be of interest, but the algorithm using the AIC may be of value; there is a tech report that describes it.

Another aspect of my work was the investigation of celestial data as a source of position estimates for mobile robots:

- F. Cozman; E. Krotkov.
*Robot Localization using a Computer Vision Sextant*, International Conference on Robotics and Automation, pages 106-111, Nagoya, Japan, May 1995.

And finally, another twist in this work was the study of atmospheric scattering as a clue for depth in outdoor environments; as far as I know, the first study of scattering in the context of image understanding.

- F. Cozman; E. Krotkov.
*Depth from Scattering*, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, June, 1997.

I have been interested for some time in the problem of calculating bounds for dynamical systems; I have since discovered a huge literature in this area, which I expect to be of great relevance for robust Statistics in the future. I have published some work on the specific topic of manipulating ellipsoidal models of error in Robotics.

- F. Cozman; E. Krotkov.
*Truncated Gaussians as Tolerance Sets*, Fifth Workshop on Artificial Intelligence and Statistics, Fort Lauderdale Florida, 1995.

fgcozman@cs.cmu.edu [Send Mail?]