Lev Muchnik is an Associate Professor in the Data Science Department at the Hebrew University Business School.
His expertise lies in the collection and analysis of massive datasets representing large-scale social systems, and their modeling using tools borrowed from social sciences and statistical physics. Muchnik’s recent research has focused on theoretical and empirical problems related to the structure and evolution of social networks, as well as peer effects, the spread of behavioral norms, information diffusion, and other processes specific to networked environments. Jointly with collaborators, Prof. Muchnik developed a seminal method for the identification of peer influence on networks, and conducted large-scale randomized controlled experiments in online communities. His expertise includes the design of scalable microscopic simulations of complex multi-agent systems and time-series analyses, in particular of long-term memory and scaling characteristics of financial data.
His research has been published in Science, Network Science, Scientific Reports, Advances on Practical Applications of Agents and Multi-Agent Systems, Physica A: Statistical Mechanics and its Applications, Bulletin of the American Physical Society, Complexity Hints for Economic Policy, Nature Physics, Physical Review E, Practical Fruits of Econophysics, Bioinformatics, and Artificial Economics.
Prof. Muchnik earned his Ph.D. in Physics from Bar-Ilan University. He holds a B.A. in Physics from Hebrew University.
In addition to his academic experience, Prof. Muchnik has served as a Visiting Researcher at Microsoft, and a Senior Research Scientist at NYU’s Stern School of Business.
He teaches courses in Data Science, Big Data Analytics, and Information Science.