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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.


     

A brave new world of genetic engineering
Greengard S.  Communications of the ACM 62(2): 11-13, 2019. Type: Article

While CRISPR, Gene Knockout Kit (GKO), and cryo-electron microscopy (Cryo-EM) may be acronyms unknown to the larger computer science (CS) community, they are well known in genetic engineering research. Software and hardware are extending and chang...

 

 From being there to watching: shared and dedicated telepresence robot usage at academic conferences
Neustaedter C., Singhal S., Pan R., Heshmat Y., Forghani A., Tang J.  ACM Transactions on Computer-Human Interaction 25(6): 1-39, 2018. Type: Article

“I was there” is a sentence that in the future might be synonymous with remotely watching, or being telepresent. When the “being there“ means attending scientific events aimed at increasing scientific cooperation with peers...

 

Optimal control of screw in-pipe inspection robot with controllable pitch rate
Tourajizadeh H., Rezaei M., Sedigh A.  Journal of Intelligent and Robotic Systems 90(3-4): 269-286, 2018. Type: Article

In-pipe inspection robots that use a screw drive mechanism move like a screw inside a pipe via three passive wheels. This paper develops mechanical and control models for an extended version of a screw robot that also controls steering....

 

Configuration analysis and design of a multidimensional tele-operator based on a 3-P(4S) parallel mechanism
Xiao X., Li Y., Wang X.  Journal of Intelligent and Robotic Systems 90(3-4): 339-348, 2018. Type: Article

Parallel robots that use closed kinematic chains have interesting mechanical properties, such as stiffness, high precision, and good load capacity, at the cost of an increasing complexity in designing and solving their kinematics. This paper propo...

 

Online estimation of discrete, continuous, and conditional joint densities using classifier chains
Geilke M., Karwath A., Frank E., Kramer S.  Data Mining and Knowledge Discovery 32(3): 561-603, 2018. Type: Article

When considering data streams, the entire data stream is not available in one shot and estimates are needed, thus it is difficult to apply traditional data analysis methods. Traditional data mining algorithms operate on the full dataset, while str...

 
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