Natalia Vesselinova

Research

As a Postdoctoral Researcher at the Mathematical Statistics and Data Science research group, I conduct research at the intersection of telecommunications, applied statistics and machine learning. I am passionate about addressing problems relevant to the engineering practice—and ultimately, to the society—by devising sustainable solutions that are concurrently effective and simple. My most recent focus has been on predicting the load placed by users on the mobile cellular network that serves them. This is a critical area of research as it ensures the effective use of the network’s resources and provision of services that support smart cities and roads. An innovative aspect of my approach to solving this prediction task is employing population dynamics, which can effectively scale-up the prediction accuracy of the forecasting algorithms. This approach is a paradigm shift in mobile cellular traffic forecasting. It allows for implementing learning structures that employ such heterogeneous data—mobile cellular traffic time-series and population dynamics—for improved prediction performance without increasing the algorithms’ computational complexity, memory requirements nor energy expenditure. Overall, my interest is in delivering efficacious and sustainable solutions to societal problems with science-based tools and creativity.

Teaching

In my teaching practice, I aim at nurturing thought-engaging discussions with the next generation of game-changers. Lately, I have been sparkling their interest in probability and statistics by showing the relevance of this field to virtually all spheres of science and everyday life.

Collaboration and thesis supervision

Are you interested in inter-disciplinary research—explore how far crafting solutions with mathematical-based tools to real-world problems, especially those from the mobile communications area, can take you? Let us meet and discuss!