We are a research team whose center of gravity is at the Complexity Science Hub Vienna, but collaborating internationally.
Ashwin Sanil Kumar
Eddie studies the role of information in the small and large living patterns around us. Examples range from the biology of neural tissue to the ecology of forests, the dynamics of armed conflict, and the processes of innovation and obsolescence in society. He is fascinated by how we paint those patterns on the shared canvas of mathematics and what the resulting similarities between the mathematical representations reveal about them. Do similarities reflect analogous function, universal dynamics, or are they (simply) artifacts of our representation? His work aims to answer these overarching questions that come together from the standpoint of information.
He is an Austrian Science Fund ESPRIT Fellow at the Complexity Science Hub and formerly a Program Postdoctoral Fellow at the Santa Fe Institute. He has a PhD in Theoretical Physics from Cornell University—where he received a National Science Foundation Graduate Research Fellowship—and a BA in Physics from Princeton University. He has been invited to panels on the science of violence (Santa Fe Council on Int’l Relations) and on the physics of the 2021 Nobel Prize (Santa Fe Institute) as well as lectures at the universities of Amsterdam, Potsdam, Northwestern, Oxford, and Bristol.
A short CV is available here.
Google Scholar page
Niraj joined the Complexity Science Hub Vienna as a Ph.D. candidate in August 2021. He has
a masters in physics from the Indian Institute of Technology Indore. His master’s thesis was in the
field of non-linear dynamics and complex networks.
For his masters’ thesis, Niraj performed network modeling using Kuramoto’s oscillators to study collective behavior found in many real-world systems and also studied critical transitions between synchronization and chimera (solitary) states found in dynamical networked systems. In order to study the phase space of such systems, he used a unique technique that used machine learning to draw boundaries between the different phases.
Niraj’s research interest lies at the intersection of statistical physics, collective behavior, network science, computational modeling, data analysis and machine learning. Through his research, he wishes to study the hidden universal laws of nature, using various tools and techniques that fall under the umbrella of complexity science.
Currently at the Hub, Niraj is building a systematic framework to study armed conflicts and also studying various emergent regularities that are found in armed conflicts.
Ernesto is currently a PhD candidate at Havana University. His current research is based on the study of epidemic processes in complex networks. In 2023 he will join the Complexity Science Hub Vienna as a postdoc researching in the field of complex systems and collective behavior with particular focus on evolutionary systems driven by innovation and obsolescence processes.
Victor is a predoctoral researcher at the Santa Fe institute. He is interested in the basic processes that give rise to interestingness in the universe. Such processes — such as diffusion, evolution, computation, aggregation — recur on the most diverse of substrates. Whether the substrate in question consists of molecules, organisms, ideas, institutions, or perhaps universes, what matters is often not what it is but how its parts relate to each other.
Victor is interested in using lenses from math and physics to understand these basic processes, including their conditions, their dynamics, and their interactions.
His research at the Santa Fe Institute has revolved around the processes of cooperation, cognition, and centralization. He graduated from Cornell summa cum laude with a degree in complex systems and math.
Simon D. Lindner is currently a PhD candidate at the Section for Science of Complex Systems at the Medical University of Vienna and the Complexity Science Hub Vienna, where he is being supervised by Peter Klimek. Simon holds a BSc and MSc in physics from the University of Vienna. His Master's program provided him with a strong foundation in statistical physics, which motivated him to pursue his PhD research at the intersection of statistical physics, machine learning, and network science.
Simon's research is primarily focused on the intersection of statistical physics, machine learning, network science and impactful applications of data science.