Rita Cucchiara

Outside of UNIMORE, Cucchiara leads several nationwide groups in Italy centered around Artificial Intelligence and Deep Learning. In 2016, she was elected President of the Italian Association of Pattern Recognition, Learning and Computer vision GIRPR (Gruppo di ricercatori italiani in pattern recognition) then renamed in CVPL Computer Vision, Pattern Recognition and Machine Learning Association. In 2015 she became an advisory board member of the Computer Vision Foundation. She has also been director of the CINI National Lab in Artificial Intelligence and Intelligent Systems. from 2018 to 2021. Since 2020 she is responsible of the NVIDIA AI TechicalCenter in Modena and director of the ELLIS unit of Modena in the network of European Labs in Learning and Intelligent Systems. Since 2021 Cucchiara is director of the Artificial Intelligence Research and Innovation Center (AIRI).

Cucchiara became an associate professor of computer architecture and computer vision in the Enzo Ferrari Department of Computer Science and Engineering at the University of Modena and Reggio Emilia (UNIMORE) in Italy in 1998. In 2005, she was promoted to Full Professor. Cucchiara has many leadership roles in UNIMORE including being the Dean of Regional Industrial Research Platform in Emilia Romagna from 2012 to 2016, and the Director of the Masters Program in Visual Computing and Multimedia Technology. Cucchiara is also Director of the AImage Lab, leading projects in areas such as autonomous robotics, deep learning for video surveillance, human behavioral analysis, and human-car interactions, computer vision and patterns recognition.

Cucchiara completed her PhD in 1992 and then became a research assistant at the University of Ferrara from 1993 until 1998. Her research here focused on building algorithms for shape and object detection for use in detecting fabrication defects. She used Gradient-based Hough Transform in order to detect shapes amidst both structured and unstructured noise. Cucchiara further developed a novel machine learning approach for features selection and shape detection where data-driven hypotheses are first generated about the presence of a target shape, and then a classifier is defined by a machine learning algorithm validates these hypotheses, such that learning is used both in defining the description language as well as the for defining the model.

Rita Cucchiara (born 1965) is an Italian electrical and computer engineer, and professor of computer architecture and computer vision in the Enzo Ferrari Department of Engineering at the University of Modena and Reggio Emilia (UNIMORE) in Italy. Cucchiara's work focuses on artificial intelligence, specifically deep network technologies and computer vision to human behavior understanding (HBU). She is the director of the AImage Lab at UNIMORE and is director of the Artificial Intelligence Research and Innovation Center (AIRI) as well as the ELLIS (European Labs of Learning and Intelligent Systems) Unit at Modena. She was founder and director from 2018 to 2021 of the Italian National Lab of Artificial Intelligence and intelligent systems of CINI. Cucchiara was also president of the CVPL GIRPR the Italian Association of Computer Vision, Machine Learning and Pattern Recognition from 2016 to 2018.

Cucchiara was born in Italy in 1965. She received her diploma in classical studies at Liceo Classico "San Carlo" in Modena, Italy in 1983 and then pursued her undergraduate education at the University of Bologna. She majored in electronic engineering and graduated magna cum laude in 1989. Following her undergraduate degree, Cucchiara pursued her graduate work at the University of Bologna, specializing in Computer Engineering and Parallel Architectures for Computer Vision. Her work focused on genetic models for the clustering problem in Image Analysis. Cluster analysis is important for object separation and identification, and is used to transform objects from the real image space to an n-dimensional feature space in order to find similarities between objects and group them. She addressed limitations to existing clustering in image analysis using a stochastic computational model which seeks the optimal solution to an object function. This algorithm is termed the Genetic Algorithm because of its similarity to evolution, where good solutions have higher "fitness" and are reproduced in order to achieve the most optimal object segmentation.