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Giuseppina Carla Gini
Politecnico di Milano
Milano, Italy

After graduating with a degree in physics from the University of Milano, Giuseppina Gini specialized in computer science as a post-doc fellow and worked on different artificial intelligence projects at the Politecnico di Milano (1972-1976). From 1976 to 1987, she held an assistant professor position at Politecnico di Milano, as well as various appointments as a visiting scholar and research assistant at Stanford University (California, USA) (in the Artificial intelligence Laboratory of the Computer Science Department and in the NMR Laboratory of the Medical School) and at SRI. Since 1987, she has been an associate professor at Politecnico di Milano, Faculty of Computer Engineering.

Gini has written and edited two books, and has authored about 200 refereed papers in scientific journals, books, and conference proceedings. Among other professional services, she organized and chaired the Symposium on Predictive Toxicology (Stanford, March 1999) for the American Association of Artificial Intelligence, and the AI&Math special session on Knowledge Exploration in Predictive Toxicology (January 2000).

She has been a partner in 16 international research projects (for NATO and the EU), and the coordinator of an EU project devoted to the development of new expert system methods in predictive toxicology. Moreover, she has directed seven national research projects.

Her main areas of research are knowledge representation and reasoning, with an emphasis on algorithms, biologically inspired solutions, hybrid systems, and computational efficiency. The main application areas in which she focuses her work are spatial and visual reasoning, human-machine interaction, and data mining. Over the course of her career, she has developed languages, simulators, and planners. In addition, she has cooperated with many European research centers over the past 15 years on various projects related to toxicity modeling, predictive systems, data mining, and in silico models.

Gini has been a reviewer for Computing Reviews since 1985, and has over 60 published reviews.

	Reviews by Giuseppina Carla Gini

	Online learning for network resource allocation Salem T., Salem T. ACM SIGMETRICS Performance Evaluation Review 50(3): 20-23, 2023. Type: Article Network resource allocation is still a challenge in many scenarios where fast services need to be provided in changing and unpredictable situations, for example, the ones encountered in streaming data applications....

	Intelligent robot: implementation and applications Duan F., Li W., Tan Y., Springer International Publishing, New York, NY, 2023. 302 pp. Type: Book (978-9811982521) Developing intelligent robots is a difficult task. Even the act of defining an intelligent robot is hard. However, according to the book’s subtitle--implementation and applications--the authors aim to develop robot applicati...

	Should robots have rights or rites? Kim T., Strudler A. Communications of the ACM 66(6): 78-85, 2023. Type: Article Imagining a future where robots routinely interact with people has raised debate about whether robots deserve special treatment, and eventually rights. Instead of discussing the legal status of robots, the authors propose a view rooted in Confucia...

	Deep transfer learning in human-robot interaction for cognitive and physical rehabilitation purposes Aqdus Ilyas C., Rehm M., Nasrollahi K., Madadi Y., Moeslund T., Seydi V. Pattern Analysis & Applications 25(3): 653-677, 2022. Type: Article Despite the huge number of publicly available images, emotion recognition for real applications suffers from a lack of relevant images. This challenge is considered here, where the task involves interpreting the facial expressions of patients with...

	Deep learning based single sample face recognition: a survey Liu F., Chen D., Wang F., Li Z., Xu F. Artificial Intelligence Review 1(1): 1-26, 2022. Type: Article Deep learning (DL) methods, and in particular convolutional neural networks (CNNs), provide the most used and most effective face recognition systems. However, when “each identity has only a single sample available for training,” perfo...

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