About Us

We are a research group based at the Complexity Science Hub Vienna with international collaborators at the Santa Fe Institute, Bucerius Law School, Hong Kong University, amongst others.

The Team

A team with expertise in mathematical and computational modeling working on developing conceptual and analytical tools commensurate with the complexity of social and biological dynamics.

  • Eddie Lee
  • FWF ESPRIT Postdoctoral Fellow
  • Niraj Kushwaha
  • PhD Candidate
  • Ernesto Ortega
  • Postdoc
  • Victor Odouard
  • Affiliated research intern
  • Simon Lindner
  • Affiliated PhD Candidate


Eddie Lee

Eddie leads the Complex Collective.

He is a postdoctoral fellow at Complexity Science Hub Vienna since 2021 and formerly at the Santa Fe Institute as a Program Postdoctoral Fellow. He is a recipient of the Austrian Science Fund ESPRIT postdoctoral fellowship and a former NSF Graduate Research Fellow. He holds a PhD in Theoretical Physics from Cornell University and received his AB in Physics from Princeton University. His CV is available here.

Google Scholar page

Niraj Kushwaha

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.

Personal website.

Ernesto Ortega

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 Odouard

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 Lindner

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.