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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 111 reviews

  Scalable computational techniques for centrality metrics on temporally detailed social network
Gunturi V., Shekhar S., Joseph K., Carley K.  Machine Learning 106(8): 1133-1169, 2017. Type: Article

To analyze social networks, where social interactions change over time, special graphs and methods are needed. In this paper, social networks are represented as temporally detailed (TD) networks; the aim is to compute centrality metrics on them, b...

Feb 15 2018  
  A survey of imperatives and action representation formalisms
Srinivasan B., Parthasarathi R.  Artificial Intelligence Review 48(2): 263-297, 2017. Type: Article

What is an action? The agent observes two states at different times; if there is a change, then an action occurred. This implicit definition of action is adopted in this paper. At the beginning of artificial intelligence (AI), first-order logic wa...

Nov 20 2017  
  Solving the robot-world hand-eye(s) calibration problem with iterative methods
Tabb A., Ahmad Yousef K.  Machine Vision and Applications 28(5-6): 569-590, 2017. Type: Article

Calibration means building the transformation matrices between the robot end effector and the camera (call it Z), and between the robot base and the reference system (call it X). The mathematical expression is the matrix equation, in homogeneous c...

Nov 1 2017  
  Reactionless visual servoing of a multi-arm space robot combined with other manipulation tasks
Abdul Hafez A., Mithun P., Anurag V., Shah S., Krishna K.  Robotics and Autonomous Systems 911-10, 2017. Type: Article

Visual servoing is an effective control method for autonomous manipulators, allowing them to reach a target using visual feedback. Mobile manipulation addresses the problem of controlling arms mounted on mobile bases, where visual servoing is stil...

Jul 12 2017  
   Prediction of anti-cancer drug response by kernelized multi-task learning
Tan M.  Artificial Intelligence in Medicine 7370-77, 2016. Type: Article

Predicting the drug response of patients in cancer therapy can be obtained by models built on large datasets of in-vitro tests on cancer cell lines. Such data is complex, as different data types are involved: genes, cell lines, and anticancer drug...

Jun 7 2017  
  Target detection and tracking by bionanosensor networks
Okaie Y., Nakano T., Hara T., Nishio S.,  Springer International Publishing, New York, NY, 2016. 68 pp. Type: Book

Bionanosensor networks are spatially distributed populations of sensors that use the methods of network engineering in a new way. Bionanosensors can be expressly engineered for a task, or be natural machines such as bacteria. The important point i...

May 31 2017  
   Bayesian multi-tensor factorization
Khan S., Leppäaho E., Kaski S.  Machine Learning 105(2): 233-253, 2016. Type: Article

Data mining is increasingly facing the problem of extracting new knowledge from experimental data collected from complex phenomena. To extract hidden information, such datasets can be decomposed into the components that underlie them. Because data...

Feb 23 2017  
  Open issues in evolutionary robotics
Silva F., Duarte M., Correia L., Oliveira S., Christensen A.  Evolutionary Computation 24(2): 205-236, 2016. Type: Article

Evolutionary methods can be adopted for developing controllers for task-oriented robots without the need to fully write models and programs. Initially, the considered task was the navigation of wheeled mobile robots; today, methods are available f...

Nov 28 2016  
  Collaboration in multi-robot exploration: To meet or not to meet?
Andre T., Bettstetter C.  Journal of Intelligent and Robotic Systems 82(2): 325-337, 2016. Type: Article

We expect that using more communicating robots to explore and map an environment will reduce the time to completion of the task. But this basic question is sometimes neglected: Is the cooperation between more robots really useful?...

Jul 29 2016  
  Exponential moving average based multiagent reinforcement learning algorithms
Awheda M., Schwartz H.  Artificial Intelligence Review 45(3): 299-332, 2016. Type: Article

Reinforcement learning for multiagent systems aims to find optimal policies that can be learned by agents during their interaction in cooperative or competitive games. In game theory, the target is reaching the Nash equilibrium, where each agent i...

Jun 15 2016  
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