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Gini, Giuseppina
Politecnico di Milano
Milano, Italy
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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.

Date Reviewed  
- 10 of 128 reviews

  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...

Dec 27 2022  
   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...

Oct 17 2022  
  Detection and resolution of rumours in social media: A Survey
Zubiaga A., Aker A., Bontcheva K., Liakata M., Procter R. ACM Computing Surveys 51(2): 1-36, 2018.  Type: Article

Unverified information can spread on the web and influence public opinion before it is eventually verified as true or false. Such rumors eventually evolve during reiterated transmission, and often accompany fake news. The use of social...

Mar 30 2022  
  Dependable visual light-based indoor localization with automatic anomaly detection for location-based service of mobile cyber-physical systems
Liu Y., Chen X., Kadambi D., Bari A., Li X., Hu S., Zhou P. ACM Transactions on Cyber-Physical Systems 3(1): 1-17, 2018.  Type: Article

Indoor localization for mobile devices can employ beacons inserted in known positions in the environment. Light-emitting diodes (LEDs) are becoming popular beacons because of their simplicity; however, using the light for localization ...

Jul 2 2021  
  A survey on gait recognition
Wan C., Wang L., Phoha V. ACM Computing Surveys 51(5): 1-35, 2018.  Type: Article

Gait recognition is a biometric method that uses sensor data to recognize people based on body shape and walking styles. Gait data is acquired from video images, inertial sensors, or sensors in the environment. The possible uses are di...

Jun 8 2021  
  Probabilistic policy reuse for safe reinforcement learning
García J., Fernández F. ACM Transactions on Autonomous and Adaptive Systems 13(3): 1-24, 2019.  Type: Article

Human and robot planners seek safe and optimal action plans. Learning to adapt good examples, such as the ones provided by a teacher, is an effective way to speed up action planning and can be used to start reinforcement learning. When...

Dec 17 2020  
  Learning from human-robot interactions in modeled scenes
Murnane M., Breitmeyer M., Ferraro F., Matuszek C., Engel D.  SIGGRAPH 2019 (ACM SIGGRAPH 2019 Posters, Los Angeles, CA, Jul 28, 2019) 1-2, 2019.  Type: Proceedings

Usually robots are checked and tested in virtual environments before delivering them to the real world. In this poster, however, the robot is only virtual, and users interacting with it are also rendered in virtual reality....

Sep 2 2020  
  Visual SLAM and structure from motion in dynamic environments: a survey
Saputra M., Markham A., Trigoni N. ACM Computing Surveys 51(2): 1-36, 2018.  Type: Article

Reconstructing an environment’s 3D models is traditionally a computer vision problem, crucial for virtual reality (VR) applications and mobile robots that have to estimate the pose of the camera that moves with them. Well-kno...

Feb 26 2020  
  Industry-scale knowledge graphs: lessons and challenges
Noy N., Gao Y., Jain A., Narayanan A., Patterson A., Taylor J. Communications of the ACM 62(8): 36-43, 2019.  Type: Article

Many companies provide users with access to disparate services, from search to complex interactions, all of which need a large body of general and specific knowledge represented in knowledge graphs....

Feb 7 2020  
  Assessing neural network scene classification from degraded images
Tadros T., Cullen N., Greene M., Cooper E. ACM Transactions on Applied Perception 16(4): 1-20, 2019.  Type: Article, Reviews: (1 of 2)

Image processing and understanding human vision have shared interests and methods since the beginning of computer vision research. In recent years, deep learning and in particular convolutional neural networks (CNNs) have been applied ...

Nov 27 2019  
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