Our research investigates the growth of transcriptional gene regulatory networks under various selection rules and examines large-scale properties to uncover unique patterns. The study delves into the influence of different selection processes on network evolution, including random, structure-based, sensitivity-based, and hybrid selection. By analyzing the complex dynamics governing network growth, we aim to provide a comprehensive understanding of the implications of diverse selection rules on network properties. This work offers valuable insights to HR professionals and data scientists interested in network growth and computational complexity.

Alexander B., Pushkar A., Girvan M. "Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes" J. R. Soc. Interface.182020079020200790

Our project employs machine learning and computer vision techniques in Python to uncover hidden patterns in the art of painting, offering valuable insights into color choices, artistic styles, and color mixing techniques. By analyzing a high-quality dataset of oil paintings from the National Gallery of Art, we have developed a comprehensive algorithmic pipeline that systematically processes and extracts meaningful features from each artwork. Our data-driven approach reveals fascinating trends and relationships between color palettes, artistic techniques, and historical context across different time periods, regions, and individual artists.

To be published.

As a research assistant in this project, I focused on studying large networks defined by the evolutionary relationships of complex 3D structures, with the aim of identifying emergent properties of evolution. By examining these metalloprotein networks, we gained valuable insights into the intricacies of biological systems and the underlying principles governing their development.