Artificial Intelligence R+D

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About us

We do applied Artificial Intelligence

Our mission

  • To develop scientific and technical research by applying AI techniques to problems in diverse application areas
  • To contribute to the development of highly qualified human resources
  • To transfer knowledge and technology to public and private institutions

Research & Development Lines


Data Science

Data science involves all the stages from preparing the data, cleansing, statistical analysis and elaboration of predictive models, using Machine Learning techniques, among others. We focus on all sort of applications, including industry, network security, earth sciences (forest analysis, volcanoes, glaciers, etc), health, food, agriculture, livestock, to name a few.

  • Statistical analysis
  • Machine Learning
  • Big Data
  • Applications to network security, earth sciences, health, food, agro, etc.


We apply combinatorial as well as continuous optimization techniques to find optimal or good suboptimal solutions in complex search spaces. Applications are diverse: industry (logistics, warehousing, product design, production planning and execution), finance, strategy (scenario evaluation, resource optimization), planning, etc.

  • Local and global optimization
  • Single-objective or multiple-objective problems
  • Industry, planning, finance, etc.
  • Genetic Algorithms, Simulated Annealing, Basin-hopping, AI-Planning, etc.

Intelligent Agents

Intelligent agents are entities with autonomy, proactiveness, reactivity and intelligence. They can interact with each other and with human beans in complex systems. We use them in many applications such as optimization, monitoring, distributed decision making, when robustness or redundancy is required, to automate negotiation or making trade-offs between conflicting objectives, etc.

  • Autonomous reactive and proactive agents
  • Agents with built-in artificial intelligence (reasoning, optimization, prediction, etc.)
  • Applications: distributed decision making, distributed action execution, negotiation, critical systems that require robustness.


Forest detection in satellite signals through Machine Learning

The careful processing of data obtained from satellite sensors could provide useful information about different natural and artificial phenomena related to the Earth. The forest detection and the study of different areas of the Earth's surface is useful in different problems such as desertification identification, forest health analysis, flood simulation, etc. Some techniques have been proposed that provided vegetation or forestation masks based on satellite data, presenting different problems like the need for human intervention to correct the classification, or lack of flexibility. In this project these problems are addressed by applying Machine Learning algorithms for automated forest detection using satellite data. The proposed approach is validated through experiments using Digital Surface Models, optical and thermal spectral firms, and forest masks obtained from SRTM, Landsat-8 and JAXA projects in several regions of the world.


DNS attacks detection through Deep Learning

The Domain Name Service (DNS) is a central piece of the Internet. This importance, along with its lack of security, have turned it into a common target for different malicious behaviors like the use of Domain Generation Algoriths (DGA) to command and control a set of infected nodes, or Tunneling techniques to avoid restrictions of the system administrator. This project exploits the lexicographic characteristics present in normal domains, DGA and Tunneling through a multi-class Deep Learing model, based on Convolutional Neural Networks (CNN), capable of capturing combinations of characters that are important to discriminate malicious from non-malicious domains. The research is centered not only on the development of the neural network, but also its implementation for the detection of network traffic in real time.


Recommending malicious behavior

The recognition of malicious behavior in network traffic requires a big amount of resources, both human and computational. During the last few years, several approaches have been applied to try to alleviate the tasks of recognition and analysis. These approaches aim at making the task of netork security personnel easier, thus improving their ability to detect these behaviors and trying to increase the automation level of the process of malicious behaviors analysis and recognition. To this end, this project studies recommendation systems and their application to assist in the identification of threats to security in the network. In the labeling stage the goal is to assist in building a behavior model with a set of patterns compiled by experts using visual tools. Additionally, in the learning stage, supervised learning algorithms will be researched, to assist in the process of recommending labels for the traffic under analysis.




Carlos Catania (Harpo)


PhD in Computer Science (UNICEN, Argentina). Free software activist.

Interests: Machine Learning applied to navigation systems, agriculture, livestock, network security, distributed systems, information networks.


Martín Marchetta


PhD in Engineering (National University of Cuyo, Argentina). Master Design Global (Université de Lorraine, France).

Interests: Machine Learning, Intelligent Agents, Metaheuristics, Optimization, Innovation, Industrial Applications


Raymundo Forradellas (Kike)


PhD in Artificial Intelligence (Polytechnic University of Madrid, Spain).

Interests: Applied Artificial Intelligence, Planning and Scheduling, Logistics and Industrial Systems.


Gabriel Caffaratti

PhD Student

B.S. in Information Systems (National University of Technology). PhD student (Computer Science program, UNICEN, Argentina).

Interests: Artificial Vision, Remote signal processing, Data Analysis, Machine Learning, Deep Learning.


Jorge Guerra

PhD Student

B.S. in Computer Science (University of La Habana, Cuba). PhD student (Computer Science program, UNICEN, Argentina).

Interests: Machine Learning, Data Analysis, Visualization, Network Security, Sequence Analysis.


Franco Palau

Undergrad Student

Undergrad Student (B.S. in Mechatronics, National University of Cuyo, Argentina).

Interests: Machine Learning, Data Analysis, Embedded Systems, Mobile Robotics.



Facultad de Ingenieria
Universidad Nacional de Cuyo
Centro Universitario. Parque Gral. San Martín
Mendoza. Argentina.


++54-261-4135000 ext. 2128

Email Address

More info

You could also be interested in our code repository and publications.

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