Prof. Amir Bashan

General background on the researcher:

Dr. Bashan joined the Department of Physics as a senior-lecturer in 2016 after a postdoctoral position at Harvard University. He has completed his Ph.D. physics studies in Bar-Ilan in the field of network science under the guidance of Prof. Shlomo Havlin in 2013.

Research Description:

Dr. Bashan's research group is developing and implementing advanced computational methods from the fields of statistical physics, network science, non-linear dynamics, and machine learning to investigate systems characterized by large datasets. The main focus is the analysis of large and complex biological systems that began to be measured in previous years. For example, our body is populated with trillions of bacteria of hundreds of different species. The composition of the bacterial populations is of critical importance to our health. On the other hand, a disordered composition is associated with diverse diseases. To understand and control such complex populations, Dr. Bashan's research group investigates, using theoretical, computational methods, and real data analysis, the governing laws which determine their dynamics, stability, and composition. In a recent study published in the journal Nature, Dr. Bashan found the universality of these laws in various places in the human body. Another example explored in the group is the study of stochasticity in the aging process using detailed data on the biological activity of single cells.

Why research matters:

Many fields of research and industry are now dominated by vast amounts of data. The ability to analyze information using advanced methods of networks, data science, and machine learning is a key to understanding their complex systems, laws and dynamics. For example, with the understanding of the laws governing the bacterial ecosystem of the human body, medical treatments based on the composition of these populations can be personalized. In another example, understanding the importance of stochasticity in the aging process is essential for developing treatments to improve the quality of aging.