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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.


Applied reinforcement learning with Python: with OpenAI Gym, Tensorflow, and Keras
Beysolow, II T.,  Apress, New York, NY, 2019. 184 pp. Type: Book (978-1-484251-26-3)

This is a small book on the broader topic of reinforcement learning (RL), written by a practitioner for practitioners. It is very practically oriented, but with limited theoretical background. The book narrative is built around OpenAI Gym, a popul...


Towards mathematics, computers and environment: a disasters perspective
Santos L., Negri R., de Carvalho T.,  Springer International Publishing, New York, NY, 2019. 258 pp. Type: Book (978-3-030212-04-9)

This edited collection of papers, written by scientists from Latin America, focuses on various types of quantitative and stochastic models, including their potential to “improve predictability of natural disasters,” along with various ...


PDE models for atherosclerosis computer implementation in R
Schiesser W.,  Morgan&Claypool Publishers, San Rafael, CA, 2018. 141 pp. Type: Book (978-1-681734-43-9)

Atherosclerosis is a pathological condition that needs extensive research beyond clinical studies. This small and very practical book presents bloodstream modeling of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol con...


 Guide to graph algorithms: sequential, parallel and distributed
Erciyes K.,  Springer International Publishing, New York, NY, 2018. 471 pp. Type: Book (978-3-319732-34-3)

This comprehensive text focuses on graph data structures and consequent graph algorithms as fundamental to the analysis of various types of networks, from social to biological ones. The book consists of three parts, with 15 chapters including an e...


Scalable big data analytics for protein bioinformatics: efficient computational solutions for protein structures
Mrozek D.,  Springer International Publishing, New York, NY, 2018. 315 pp. Type: Book (978-3-319988-38-2)

High-performance computing (HPC) refers to the use of large computational resources for solving computationally hard and data-intensive problems. Big data refers to “the exponential growth ... of data, both structured and unstructured.”...


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