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Mariana Damova
Mozaika
Sofia, Bulgaria
 

Mariana Damova is the CEO of Mozaika, a company providing research and solutions for data science, natural interfaces, and human insight. Mozaika specializes in building semantic information infrastructures in different verticals, such as business information delivery, human resources management, cultural heritage, earth observation, and water management.

Her background is in natural language processing, semantic web technologies, and artificial intelligence (AI), with significant academic and industry experience in Europe and North America.

She has taught graduate courses, conducted research, and led international interdisciplinary projects and teams on virtual customer service, speech-to-speech machine translation, search based on linguistic principles, voice-enabled emotion recognition, an expert system supporting child protective services, water resources management, content mapping, and historical archives.

She has collaborated with many different universities and organizations, including The National Archives of the UK, the British Museum, the Historical Archives of the European Commission, the European Space Agency, Europeana, and the Bulgarian Academy of Sciences.

Mariana holds a PhD from the University of Stuttgart and a mini-MBA from McGill University, and currently teaches a graduate course at New Bulgarian University.

She has been a reviewer for Computing Reviews since 2008, and has authored more than 50 publications (books and papers) related to linguistics and semantic technologies.

Mariana enjoys museums, theater, and reading.


     

PyTorch recipes: a problem-solution approach to build, train and deploy neural network models (2nd ed.)
Mishra P., Apress, New York, NY, 2023. 290 pp.  Type: Book (978-1484289242)

Full of lengthy code examples, PyTorch recipes is a very good textbook for beginner and intermediary neural network developers using PyTorch....

 

Gradient expectations: structure, origins, and synthesis of predictive neural networks
Downing K., MIT Press, Cambridge, MA, 2023. 224 pp.  Type: Book (0262545616)

A very interesting and well-grounded work, Gradient expectations provides a thorough overview and explanation of the structure and origins of prediction and predictive neural networks....

 

Neuromemrisitive architecture of HTM with on-device learning and neurogenesis
Zyarah A., Kudithipudi D. ACM Journal on Emerging Technologies in Computing Systems 15(3): 1-24, 2019.  Type: Article

This very comprehensive technical account presents a thoroughly worked out “architecture for the spatial pooler (SP)” of the hierarchical temporal memory (HTM) algorithm. This algorithm is designed to produce invari...

 

 DARPA’s explainable artificial intelligence (XAI) program
Gunning D.  IUI 2019 (Proceedings of the 24th International Conference on Intelligent User Interfaces, Marina del Ray, California, Mar 17-20, 2019) ii-ii, 2019.  Type: Proceedings

This very interesting survey talk provides extensive, high-level insight into the midterm progress of this advanced research endeavor....

 

Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier
Li B., Qin Y., Yuan B., Lilja D. ACM Journal on Emerging Technologies in Computing Systems 15(1): 1-21, 2019.  Type: Article

Li et al. present a novel approach for optimizing neural network implementations, that is, “a new architecture of stochastic neural networks” with a hidden approximate activation function and a hybrid stochastic mul...

 
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