IBISC (Computer Science, Bio-Computing and Complex Systems)  EA 4526 is the STIC laboratory of the Université de Paris Saclay - Université d'Evry, ensuring visibility in this field on the site, meeting the challenges of digital and interdisciplinary research, particularly in Genomics. It is the result of the merger of two laboratories: the LAMI (UMR 8042) and the LSC (FRE 2494). The laboratory is governed by a director, Nazim AGOULMINE, and a deputy director, Lydie NOUVELIERE.

The IBISC laboratory is made up of 4 teams (AROB@S, COSMO, IRA2, SIAM) whose activities are divided into two scientific areas: STIC & SMART SYSTEM and STIC & LIVING SYSTEMS. Without covering all the research activities within them, each axis focuses on an application area which is respectively : Drone & Vehicle, and Personalized and Precision Medicine.

• STIC & SMART SYSTEM
  

Research in this area focuses on the design of autonomous, intelligent systems. The notion of a system refers to fleets of the road or air vehicles, robots, distributed and communicating software, and services, or intelligent hardware components equipped with interacting sensors. What these devices have in common is that they are made up of many interacting entities, with autonomous decision-making powers, all coordinating their actions to achieve a common goal. The controlled design of such complex systems poses two major questions: one relating to design methods and the other to optimizing their collective and global functioning, while taking into account fluctuations in their environment. These questions are leading to the exploration of new approaches combining methods and theories from different scientific fields: automatic control, algorithms, and formal methods. Applications are particularly aimed at the Drone and Vehicle fields.

• STIC & LIVING SYSTEM
  

This interdisciplinary research covers a broad spectrum of biological issues at different scales of the living world: analysis of biological or biomedical data and signals, modeling of biological systems, learning surgical procedures, and personal assistance. Research focuses on the development of theoretical frameworks, algorithmic methods, and platforms to meet these challenges. In data analysis and systems biology, we use statistical learning models, algorithms for structure prediction, and formal models and methods for analyzing network dynamics. For the learning of surgical gestures and robotic assistance to people, we are developing systems combining techniques for analyzing signals from various sensors and for decision-making. These applications are particularly aimed at the field of personalized and precision medicine.