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.

Current Projects

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.


Behavior Classification of A Grazing Goat through Machine Learning

The importance of goat production has increased during the last decades all over the world, predominantly in countries with harsh environmental conditions. This is due, among other factors, to the fact that goats have numerous advantages over other domestic animal species, that allow them to adapt and maintain their production in hard climatic conditions. Argentina has approximately 4.8 million goats, of which about 52\% are distributed in the Monte Desert region. This project aims at building a classification system based on information provided by inertial sensors for identifying a goat's grazing behavior. In particular, the study of grazing behavior help to understand how animals use vegetation and would allow to adjust the herd management according to the availability and conditions of the natural grassland. The time that animals spend on activities such as grazing, rumination and resting, reflects the climatic and pasture conditions, and therefore is highly related to the productive performance of animals. Therefore, the knowledge generated by animal behavior studies acquire great importance since it can be used to improve the efficiency of animal production systems. The project is carried out together with the Argentine Dryland Research Institute (IADIZA).


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

Deputy head

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.


Jorge Guerra


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

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


Rodrigo Gonzalez


PhD in Control System Engineering (National University of San Juan).

Interests: Time Series Forecasting, Data Analysis, Machine Learning, Deep Learning, Information Fusion, Kalman filter.


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.


Tatiana Parlanti

PhD Student

B.S. in Mathematics (National University of Cuyo. PhD student (Computer Science program, UNICEN, Argentina). CONICET Fellow 2021 -26.

Interests: Complex Networks, Data Analysis, Machine Learning, Deep Learning.


Luciano Robino

PhD Student

BSc in Physics (Universidad Nacional de Cuyo, Instituto Balseiro). MSc in Physics (Universidad Nacional de Cuyo, Instituto Balseiro). PhD student (Computer Science program, UNICEN, Argentina). CONICET Fellow 2022-2027.

Interests: Data Analysis, Machine Learning, Deep Learning, Scaling and Scheduling, Distributed Systems.


Juan Manuel Romero

Undergrad Student

Undergrad Student (B.S. in Computer Science, National University of Cuyo, Argentina). EVC-CIN Fellow 2021-22

Interests: Machine Learning, Data Analysis



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


++54-261-4135000 ext. 2128

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