Vitaly
Nikolaev

Bio:

Prior to working at SAS, Vitaly had an experience in the position of a researcher at the Kaliningrad branch of the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences, and as an associate professor at the departments of computer science and information technology. In addition to his research and teaching activities, he also has rich experience in administrative and educational work as the dean of the faculty of automation and control of Kaliningrad State Technical University. He participated in the development of educational programs in the fields of computer science and information technology for bachelors, masters and engineers of various higher education programs.

Vitaly also has extensive experience as a software engineer. He was the main developer of automation systems for accounting of the university, as well as for control and analysis of the educational workflow of the faculty. He also automated the workflow of some local state organizations.

Vitaly received a bachelor's degree in Computer Science and Engineering and an engineer’s degree in Computer Engineering, Systems, Complexes and Networks, and later – a Master of Science in Biomedical Computing at the Department of Computer Aided Medical Procedures and Augmented Reality at Technical University of Munich. He has a PhD degree in engineering sciences («Candidate of engineering sciences» in Russian).

Vitaly Nikolaev

Research Interests:

In the past, Vitaly was engaged in simulation modeling of stochastic processes and queuing systems, as well as participated in designing heterogeneous models of diagnostic decision support environments. Currently, the main area of ​​interest is visualization, computing and analysis of medical images, and data analysis using machine learning, in particular – deep learning and convolutional neural networks.

As part of his scientific work at the Image-based Biomedical Modeling Group at Munich School of Bioengineering, he investigated the usage of convolutional neural networks for semantic segmentation of tissue cells and brain tumors. He also participated in such projects as “Simulation of PET detectors based on continuous crystals using GATE”, “Predicting Survival of Liver Tumor Patients with Machine Learning”, and “Analysis of In-vivo Breast DCE-MRI Perfusion Data”.

At present he is also developing an online course on deep learning and convolutional neural networks. Vitaly is also interested in the applications of information technologies in the field of biomedical research and development, telemedicine, e-Health and innovations’ generation.

Key Terms:

  • Information Technology
  • Computer Science
  • Neuroscience
  • Programming
  • Deep Learning
  • Neural Networks
  • Visualization
  • Biomedicine