Talk by Prof. Makse at the Pujiang Forum on Innovation organized by Nature Physics in Shanghai, October 30-31, 2018. Talk of Prof Makse starts at 6:00 minutes.

Our research is related to “emergent properties”, i.e., “properties not contained in the simple laws of physics, although they are a consequence of them” (Phil Anderson, 1971, “More is different“).

As Schopenhauer¹, we  conclude that “it is as though our lives were the features of the one great dream of a single dreamer in which all the dream characters dream, too; so that everything links to everything else, moved by the one will to life which is the universal will in nature.”

By Joseph Campbell on Schopenhauer:

¹Schopenhauer, A. Transcendent Speculation on the Apparent Deliberateness in the Fate of the Individual, in Parerga and Paralipomena:  Short Philosophical Essays, Vol. I, pp. 199-225 (Oxford University Press, Oxford, 1974).

“It’s the research we do that counts, not the journal”: “The reviewers have turn it into a porridge. You don’t want to publish porridge!”


Our group has openings for graduate students to work on granular matter and complex networks, broadly considered. For an account of the daily routine for students in our lab, see the report of Louis Portal, research intern from France. Our lab will be working under a fully remote mode for the foreseeable future.

Graduate students and postdocs working in our group are expected to be (or become) familiar with the following computing systems. There are also a number of pertinent videos here, by presenters at the NIPS conference.

For background on the research areas of the various group members, please click here.


Hernán Makse, a member of the Brazilian Academy of Science, currently serves as Distinguished Professor of Physics at the Physics Department of City College of New York, wherein he is responsible for the Complex Networks and Soft Matter lab at the Levich Institute. He is also an Affiliate Member at Memorial Sloan Kettering Cancer Center and the CEO of Kcore Analytics, where he develops machine learning algorithms to predict human behavior. He holds a Ph.D. degree in Physics from Boston University. He has been the author of numerous publications on the theory of complex systems and the physics of soft materials, and he is an APS Fellow. His research focuses on the theoretical understanding of Complex Systems from a Statistical Physics viewpoint. He is working towards developing new emergent laws for complex systems, ranging from brain networks to biological networks and social systems.  Treating these complex systems from a unified theoretical approach, he uses concepts from statistical mechanics, network and optimization theory, artificial intelligence, and big-data science to advance new views on complex systems and networks. CV


Ph.D. Students

Bryant Avila

Fiber symmetries in the C. elegans connectome and other networks <>

Luis Alvarez

Symmetries and computation in biological networks <>

Alireza Hashemi

Graph neural networks and graph symmetries <>

Nastassia Samadzelkava

Brain networks and graph symmetries <>

Pedro Augusto,  Manuel Zimmer Group at the Research Institute of Molecular Pathology (IMP) at the University of Vienna, Austria

Raquel Garcia-Hernandez,

Santiago Canals Group at the Instituto de Neurociencia, Alicante, Spain

Sofia del Pozo,

Pablo Balenzuela Group at the Universidad de Buenos Aires, Argentina.

Visiting Students



Jiannan Wang, Beihang University


Zhenhua Wang, East China Normal University

Undergraduate Students

Louis Portal, ’13

Louis Portal <>

Ph.D. Graduates


Kate Burleson-Lesser, 2018

Eye-tracking, critical phenomena, stock market inference <>

Shaojun Luo, 2018

Social networks, Big Data analytics <>

Qiongge Li, ’19

Cancer-affected brain networks <>

Ian Leifer, ’22

Symmetries and machine learning in biological networks <>


Projects of Current Students and Postdocs:

Alireza Hashemi: Theory and application of deep learning on graph-based data structures in complex systems.
Bryant Avila: Using integer linear programming to repair brain networks to a fiber symmetric state which encapsulates the patterns of recorder brain signals.
Luis Alvarez: Understanding the impact of manipulating the mouse connectome into its memory formation process.
Osvaldo Velarde: Optimizing operations in deep neural networks (e.g. convolutions, gradient updating) using graph symmetries (automorphisms, coverings, and fibrations).
Nastassia Samadzelkava: Analysis of fibration symmetries in Alzheimer’s patients’ gene expression networks to accurately identify and correct deviations from normal dynamics.