Lab's mission is to conduct cutting-edge research in data analysis areas through both applied and theory based approaches in order to produce noteworthy results that are not only publishable, but applicable to our daily lives.
Also, it is significantly important for us to provide an environment to university students that will help them develop and polish their research skills.
Main research areas of the laboratory today:
— Protein interaction modeling
— Deep learning for peptide-protein docking
— Traffic data fusion from various types of sources
— Traffic flow modeling with adaptive control of traffic lights to improve efficiency of transportation network
— Road state forecast in situation of regular and unpredicted overloads of transportation network
— Engineering and development of algorithms for automated intelligent transport systems
Head of the laboratoty, Professor
Computational Modeling in Molecular Biology
Predicting the protein-ligand affinity with machine learning methods; сonvolutional neural networks for protein structure prediction; Monte-Carlo-based iterative generation of multiple sequence alignments for protein homolog search; development of FFT-based algorithm to compute Born radii in the generalized Born theory of biomolecule solvation
Traffic modeling by using SUMO tools with platoons and AI routing of intersection
Mathematical modelling of traffic flows (at micro and macro levels) including modeling of traffic flow consisting of unmanned vehicles and development of algorithms for their interaction