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Grigore Albeanu
Spiru Haret University
Bucharest, Romania

Grigore Albeanu is a professor of computer science at Spiru Haret University in Bucharest, Romania. He received his PhD in mathematics, in 1996, from the University of Bucharest.

Grigore has also worked as a software analyst and developer for the Forest Research and Management Institute (1984-1986, and the Institute of Scientific Research and Technological Engineering for Computing and Informatics (1986-1991, He was an associate professor at Bucharest University until 2002, and served as the head of the UNESCO Chair in Information Technologies at the University of Oradea (2004-2007).

As principal investigator in a North Atlantic Treaty Organization (NATO) project, Grigore collaborated with colleagues from all over the world on many scientific projects, including PhD theses and national and international competitions, as well as consultancy for small and medium enterprises.

His current research interests are different aspects of computing systems, modeling and simulation, software reliability, virtual reality techniques, fog computing, smart technologies, and scientific computing, including computational intelligence, numerical methods, and machine learning.

Grigore has authored (or coauthored) over 120 papers and ten textbooks in applied mathematics and computer science. He is an active referee or editor for many journals and conferences, and has been a reviewer for Computing Reviews since 1988.


Simplicity is Best: Addressing the Computational Cost of Machine Learning Classifiers in Constrained Edge Devices
Gómez-Carmona O., Gómez-Carmona O., Casado-Mansilla D., Casado-Mansilla D., López-de-Ipiña D., López-de-Ipiña D., García-Zubia J., García-Zubia J.  Proceedings of the 9th International Conference on the Internet of Things (Proceedings of the 9th International Conference on the Internet of Things, Bilbao, Spain, Oct 22-25, 2019) 1-8, 2019.  Type: Proceedings

Implementing data processing toward the edge of the Internet of Things (IoT) is an important requirement in order to produce real-time feedback according to some decision-based approach....


Efficient differentiable programming in a functional array-processing language
Shaikhha A., Fitzgibbon A., Vytiniotis D., Peyton Jones S. Proceedings of the ACM on Programming Languages 3(ICFP): 1-30, 2019.  Type: Article

Differentiable programming, or automatic differentiation, is a powerful technique in many fields, including dynamic systems, machine learning, and computer vision, mainly for solving nonlinear problems. Forward (versus reverse) differe...


Access controls and healthcare records: who owns the data?
CACM Staff . Communications of the ACM 62(7): 41-46, 2019.  Type: Article

In this article, three distinguished scientists (David Evans, Richard McDonald, and Terry Coatta) debate on the most recent and challenging approaches concerning healthcare records: ownership, privacy, security, and modern medical info...


A tutorial on canonical correlation methods
Uurtio V., Monteiro J., Kandola J., Shawe-Taylor J., Fernandez-Reyes D., Rousu J. ACM Computing Surveys 50(6): 1-33, 2018.  Type: Article

Canonical correlation analysis (CCA) is used to discover relations between two or more multivariate sets of variables, called views. Data to be processed are collected for a population of individuals, and for one individual its state i...


Scalable density-based clustering with quality guarantees using random projections
Schneider J., Vlachos M. Data Mining and Knowledge Discovery 31(4): 972-1005, 2017.  Type: Article

Efficient clustering techniques are required for knowledge discovery in large databases. The efforts of scientists have contributed to the development of many clustering algorithms....


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