Biosketch

Albert-László Barabási is a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research. He holds appointments in the Departments of Physics and Computer Science at Northeastern University and the Department of Medicine at Harvard Medical School and Brigham and Women Hospital, and he is a visiting professor at the Department of Network and Data Science at Central European University in Budapest. A Hungarian-born native of Transylvania, Romania, he received his Master’s in Theoretical Physics at the Eotvos University in Budapest, Hungary and was awarded a Ph.D. three years later at Boston University. He is a fellow of the American Physical Society and Massachusetts Academy of Sciences, and he is an elected member of the Hungarian Academy of Sciences, Academia Europaea, AAAS, European Academy of Arts and Sciences, Romanian Academy of Arts and Sciences, and Austrian Academy of Science. Awards include the Lise Meitner Award, Julius Edgar Lilienfeld Prize, Lagrange Prize in Complexity, Cozzarelli Prize, John Von Neumann Medal, and FEBS Anniversary Prize for Systems Biology. Barabási is the author of the textbooks Network Science (2020), the monograph Science of Science (with Dashun Wang), and the general audience books The Formula: The Science of Success (2018), Bursts: The Hidden Pattern Behind Everything We Do (2010), and Linked: The New Science of Networks (2002).

Research Interests

Dr. Barabási's research is focused on uncovering the mechanisms that govern the emergence and evolution of real networks. His work led to the discovery of scale-free networks in 1999 and the Barabási-Albert model that explain the widespread emergence of scale-free networks in natural, technological, and social systems. His lab has pioneered discoveries pertaining to network robustness, control, and resilience. His current work continues to focus on the fundamental aspects of the statistical mechanics of networks, along with applications to network medicine, science of science, physical networks, and network materials.

Membership Type

Member

Election Year

2024

Primary Section

Section 33: Applied Physical Sciences