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).

His CV is available here.

Google Scholar page

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.

Evelyn McGonigle is a rising junior at Princeton University. She is pursuing a degree in Operations Research and Financial Engineering, with a minor in Linguistics. Her interests include Natural Language Processing, Mathematical Optimization, and Probability Theory.

Evelyn will be joining the Complex Collective for the summer of 2023 to conduct research in the field of Linguistic Analysis of Collective Dynamics. She will be analyzing the text of US Supreme Court decisions, and extracting systematic measures of collective behaviour that account for patterns in the alignment of judicial opinions.

Gavin is a research intern at Institute of Science and Technology Austria (ISTA). He concentrated in Mathematics as an undergraduate at Harvard, initially working as a Software Engineer at a 3D Printing company before returning to research. He is interested in modeling collective social behavior using quantitative approaches, as well as exploring the technical basis for modeling decisions. His current research at ISTA uses evolutionary game theory to study cooperation in public goods games and social network formation. At the Complex Collective, he uses statistical physics to model US congressional voting behavior, aiming to understand latent structure of the US Congress while minimizing assumptions about political strategy and ideology.