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Alexander T Tzanov
New York University
New York, New York

Alexander Tzanov has a background in various medical and nuclear electronics (applied physics), environmental protection and sustainable development, chemistry, and computer science.

He started his professional life in 1984 when he graduated in electronics and started as a research associate in the Department of Electronics at the Technical University (TU) of Sofia (Bulgaria). In 1987, he became an assistant professor in the Department of Computer Science. For the next 12 years, he taught undergraduate and graduate courses in object-oriented programming, functional programming, and generic programming. He also taught a course on microprocessors, which covered computer system architectures, microprocessors’ organization, assembly language, and computer on a chip; he developed a new curriculum for the discipline within the MS program in automation and control at TU. His research interests evolved toward machine learning, specifically algorithms and models for inductive learning, learning by example, and pattern recognition.

In 1993, he joined the Department of Computer Science at Queen’s University (Belfast, UK) as a research scientist and did research in pattern recognition and quantitative management systems. At that time, his research interests shifted toward high-performance computing. In 1996, as a visiting professor at Buckingham University (UK) in the Department of Computer Science, he became involved in research on massive parallel numerical algorithms and got hooked on accelerators. At the same time, he completed his second MS degree in environmental protection and sustainable development. His research interests leaned toward the development of massive parallel algorithms and models for environmental issues, specifically the development of models for build up and distribution of secondary air pollutants. He developed a system for modeling the distribution of secondary air pollution over regions with complex orography in collaboration with the Institute of Meteorology and Hydrology at the Bulgarian Academy of Sciences. The software utilized a data mining algorithm for meteorological data over a 100-year period, an inductive learning algorithm for decision making, and real-time measurements for sampling.

In 1998, Tzanov joined the Department of Biochemistry and Biophysics at Columbia University (NY) as research scientist, where he researched applications of high-performance computing architectures and machine learning in computational biology and biophysics. In 2002, he joined the high-performance computing group at New York University (NYU) as a faculty technology specialist. Within this environment, he provided support for various research projects at NYU and became an expert in grid computing, shared memory servers, and clusters. He developed, tuned, ported, and benchmarked codes for molecular modeling and simulation, computational biology, and computational chemistry. Tzanov then moved to the Department of Chemistry at NYU and currently researches massive parallel computing algorithms for quantum computational chemistry.

He is the author of a book on systems programming, co-author of a book on object-oriented generic programming, and co-author of several papers in the scientific computing, modeling, data mining, and computational chemistry areas.


Handbook of graph theory, combinatorial optimization, and algorithms
Thulasiraman K., Nishizeki T., Arumugam S., Brandst├Ądt A.,  Chapman & Hall/CRC, Boca Raton, FL, 2016. 1216 pp. Type: Book (978-1-584885-95-5)

This huge volume has 44 chapters written by more than 40 experts worldwide. The text focuses on the synergy between graph theory and combinatorial optimization, plus algorithms as a vehicle for practical implementation. That format is a bit unique...


Beginning Ruby: from novice to professional (3rd ed.)
Cooper P.,  Apress, New York, NY, 2016. 585 pp. Type: Book (978-1-484212-79-0)

Although this book was written with the intent to be a comprehensive introduction to the Ruby language, it is not a textbook because it lacks exercises and questions aimed to test knowledge and concepts. Instead, the book is written by Ruby profes...


Graph and model transformation: general framework and applications
Ehrig H., Ermel C., Golas U., Hermann F.,  Springer International Publishing, New York, NY, 2015. 472 pp. Type: Book (978-3-662479-79-7)

In recent years, graph applications have attracted much attention in computer science and several other research fields. The book Graph and model transformation is written from a mathematical point of view, so there are no codes or code exa...


Parameterized algorithms
Cygan M., Fomin F., Kowalik Ł., Lokshtanov D., Marx D., Pilipczuk M., Pilipczuk M., Saurabh S.,  Springer International Publishing, New York, NY, 2015. 613 pp. Type: Book (978-3-319212-74-6), Reviews: (1 of 2)

The topic of parameterized algorithms is one of the main subjects in modern computer science. In this type of algorithm analysis, the running time is a function of the size of the input data and of a set of one or more parameters. The theory of pa...


High performance parallelism pearls: volume two
Jeffers J., Reinders J.,  Morgan Kaufmann Publishers Inc., San Francisco, CA, 2015. 592 pp. Type: Book (978-0-128038-19-2)

High performance parallelism pearls is a collection of loosely connected chapters, each one containing a discussion about parallelization or optimization of code in a particular scientific field. The book, written by experts in data centers...


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