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Fernando Berzal
University of Granada
Granada, Spain
 

Fernando Berzal is currently an assistant professor at the Department of Computer Science and Artificial Intelligence at the University of Granada (Spain), where he is a member of Intelligent Databases and Information Systems research group (IdBIS, for short).

His current research interests include knowledge discovery in databases and data mining, OLAP and data warehousing, intelligent information systems, and almost anything related to software development, from model-driven development to design patterns and software engineering best practices.

At the University of Granada, Fernando has tried to teach courses on computer programming, databases, and computer networks, with different degrees of success. He has also been an instructor in specialized courses about advanced programming techniques.

Apart from his research and teaching work, Fernando decided that he would like to put his ideas into practice and has co-founded a start-up company called iKor Consulting. iKor Consulting offers research and development, consulting, outsourcing, and training services for those interested in pushing the limits of their technology into unchartered territories (at least, that's what he naively likes to think).

Fernando received a Bachelor's Degree in Computer Engineering from the University of Granada in 1999, and was awarded the Spanish Computer Studies First National Prize in 2000. Fernando also holds a Ph.D. in Computer Science from the University of Granada, which he obtained in 2002 with a thesis about an alternative way to build decision trees.

Fernando is a member of the Association for Computing Machinery (ACM), IEEE, and IEEE Computer Society. He was a guest editor for the ACM Crossroads issue on Windows programming and editor of the Computer Programming Briefme weekly e-zine. He has also acted as a referee for several first-class publications, such as IEEE Software and Data and Knowledge Engineering, as well as for several research-oriented conferences. Finally, he has been a reviewer for Computing Reviews, Dr. Dobb's Journal ERCB, ACM SIGMOD Record, IEEE Software, and IEEE Distributed Systems Online. Since he is an avid reader, reviewing items for publications such as Computing Reviews is one of the most gratifying activities he has found, and he sees it as an excellent way to keep himself up to date on new topics in the field.


     

 Introduction to high performance scientific computing
Chopp D.,  SIAM-Society for Industrial and Applied Mathematics, Philadelphia, PA, 2019. 453 pp. Type: Book (978-1-611975-63-5)

Parallel programming has become commonplace in many application domains, from deep learning and other machine learning approaches to computer simulations and scientific computing. Scientific computing, understood as the use of numerical algorithms...

 

Adversarial machine learning
Joseph A., Nelson B., Rubinstein B., Tygar J.,  Cambridge University Press, New York, NY, 2019. 338 pp. Type: Book (978-1-107043-46-6)

Machine learning is behind many of the systems we typically use, both online and offline, and behind even more of the systems we might use in the future. Given their economic importance, they attract attackers who might be interested in interferin...

 

Introduction to deep learning
Charniak E.,  The MIT Press, Cambridge, MA, 2019. 192 pp. Type: Book (978-0-262039-51-2)

Deep learning has taken many application domains by storm, specifically those where artificial intelligence (AI) techniques have been struggling without too much success for decades. One of those domains is natural language processing (NLP), which...

 

 Introduction to data mining (2nd ed.)
Tan P., Steinbach M., Karpatne A., Kumar V.,  Pearson, New York, NY, 2018. 864 pp. Type: Book (978-0-133128-90-1)

The first edition of this book, published in 2006 [1], was probably the best introductory textbook on data mining available. A dozen years later, the field has evolved to become mainstream under the commercial denomination of “big data,̶...

 

Parallel programming: concepts and practice
Schmidt B., González-Domínguez J., Hundt C., Schlarb M.,  Morgan Kaufmann Publishers Inc., Cambridge, MA, 2018. 416 pp. Type: Book (978-0-128498-90-3)

Parallel programming is not optional. In the past, algorithm designers focused on the design of sequential algorithms, because of Moore’s law, and hardware speed improvements did the rest. Today, computer scientists and software engineers mu...

 
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