Santa Fe Institute

Postdoctoral Fellows

Santa Fe Institute Postdoctoral Fellows are in residence for up to three years working on collaborative as well as individual projects.

They are funded through a variety of sources:

Omidyar Postdoctoral Fellows are supported by a gift from Pierre and Pam Omidyar.
ASU-SFI Center Postdoctoral Fellows are supported by funds from the Arizona State University-Santa Fe Institute Center for Biosocial Complex Systems.
JSMF Postdoctoral Fellows are supported by a Postdoctoral Fellowship in Studying Complex Systems from the James S. McDonnell Foundation.
Program Postdoctoral Fellows are supported by a variety of grants.

Browse the SFI Phone and Email Directory.

Andrew Berdahl

Omidyar Postdoctoral Fellow

Christa Brelsford

ASU-SFI Center Postdoctoral Fellow

Caterina De Bacco

Program Postdoctoral Fellow
Funding provided by John Templeton Foundation

Marion Dumas

Omidyar Postdoctoral Fellow

Vanessa Ferdinand

Omidyar Postdoctoral Fellow

Joshua Garland

Omidyar Postdoctoral Fellow

Justin Grana

Program Postdoctoral Fellow
Funding provided by Army Research Office

Josh Grochow

Omidyar Postdoctoral Fellow

Laurent Hebert-Dufresne

JSMF Postdoctoral Fellow

Elizabeth Hobson

ASU-SFI Center Postdoctoral Fellow

Yoav Kallus

Omidyar Postdoctoral Fellow

Chris Kempes

Omidyar Postdoctoral Fellow

Artemy Kolchinsky

Program Postdoctoral Fellow
Funding provided by Templeton World Charity Foundation 

Dan Larremore

Omidyar Postdoctoral Fellow

Eric Libby

Omidyar Postdoctoral Fellow
Funding provided by Templeton World Charity Foundation

Eleanor Power

Omidyar Postdoctoral Fellow

Caitlin Stern

Omidyar Postdoctoral Fellow

Brendan Tracey

Program Postdoctoral Fellow
Funding provided by Massachusetts Institute of Technology

Andrew Berdahl

In groups, simple creatures such as fish, birds,and insects solve complex problems. Understanding the collective intelligence that arises in schools, flocks, and swarms may yield solutions to many technological and social challenges we face.

Research goals:  Uncover general principles of collective behavior and understand how these behaviors shape patterns and processes at the level of the ecosystem.

Fields: Princeton University, PhD 2014 Ecology & Evolutionary Biology

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Christa Brelsford

As the world’s urban population doubles over the next century, the new infrastructure necessary to build in order to house and care for all of these new residents will be almost equivalent to all of the urban infrastructure that has been built in the history of our species. How can we design cities and infrastructure systems to maximize the potential for improved health and economic development of their residents? What consistent spatial patterns underlie the evolution of city systems throughout their lifespans? I use empirical methods, especially spatial analysis and remote sensing to explore the shape and topology of cities and neighborhoods. In my dissertation research, I focused on the determinants of declines in per capita water consumption in Las Vegas, Nevada. While at SFI, I have been studying the shape of cities from a more general perspective, especially considering what topological transformations are necessary for a slum neighborhood to formalize.

Research goals: To understand the local scale spatial patterns in infrastructure and social systems in cities, and use that knowledge to inform urban development worldwide.

Fields: Sustainability, Cities, Urban Infrastructure, Remote Sensing, Geography, Spatial Analysis

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Caterina De Bacco

Optimising path trajectories in communication networks or inferring the source of diffusion in a complex network are challenges that have been known for years among computer science and engineering communities. Yet in recent years statistical physics has been able to provide new insights and novel approaches to these and similar problems at the interface between these fields; the cavity method or message-passing algorithm being the most successful example of this contribution.

Research goals: understand and characterize the time evolution of complex dynamical processes on networks; pursue alternative strategies to perform inference on networks; design efficient algorithms that are capable of implementing the theory.

Fields: Université Paris Sud and CNRS, PhD Statistical Physics; Marie Curie ESR Netadis; Università di Padova M.S. and B.S. in Theoretical Physics; Erasmus student at Imperial College London.

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Marion Dumas

By determining which activities we reward and which interests dominate, by shaping our social networks, and by affecting how we communicate and deliberate, institutions structure all our collective endeavors. Understanding how institutions affect dynamics of conflict, of investments, of beliefs and of norms could help us build more sustainable societies.

Research goals: Combine models and large-scale datasets of social structures and social behavior to understand how different institutions of capitalism and democracy interact, and how they shape a society's capacity to adapt, particularly to ecological constraints.

Fields: PhD 2015, Sustainable Development, Columbia University; MSc. 2009, Ecology and Evolution, ETH Zürich, BSc. 2006, Earth and Atmospheric Sciences, MIT.

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Vanessa Ferdinand

Cultural artifacts, like language, music, and technology, survive and replicate by passing from human mind to human mind. By mapping how this happens we can better understand evolution at large.

Research goals: Combine mathematical models and psychological experiments to explore how culturally transmitted behaviors and our capacities to learn these behaviors evolve together.

Fields: University of Edinburgh, PhD 2014 Linguistics and Cultural Evolution

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Joshua Garland

Derive a minimal system-agnostic predictive reconstruction theory for time series and explore the information mechanics (storage, processing, transmission) of specific systems, (e.g., the climate, traded financial markets and the human heart) to gain insight into universal truths like regime shifts and emergent phenomena. 

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Justin Grana

Often times, the timing of an action is as important as the nature of the action itself. Furthermore, the time and order in which decision makers acquire information is crucial. Understanding the impact of the timing and sequence of events in situations with multiple decision makers can better illuminate the incentive system driving goal-driven agents.

Research Goals: To understand how an uncertain order and timing of events can influence decision makers and incorporate such intuition into improving attack detection within computer networks.

Fields: Game theory, Computer Network Security, Industrial Organization, Behavioral Economics, Behavioral Finance

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Josh Grochow

Pursuing aspects fo a general theory of complex systems - even if a a complete theory remains elusive - could lead to insights that help us tackle the world's major challenges, from climate change to poverty.

Research goals: Understand the sources of complexity in the world — their dynamics, why they are the way they are, and how to tame them — by bringing rigorous mathematical ideas and techniques from computational complexity, algebra, and geometry to bear on problems in more general complex systems.

Fields: University of Chicago, PhD Computer Science

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Laurent Hebert-Dufresne

As our world grows more interconnected each day and network theory is becoming an essential tool in our understanding of the complexity of these interconnections. This theory sheds light on how a system depends not only on its constituents but also on the structure of their relations; and in the way different systems affect one another. Yet, despite all of its recent successes, network theory itself features many missing links and elements.

Research goals: To improve the toolbox of network theory. To find metrics that can distinguish networks of different natures and incorporate them with existing modeling tools.

Fields: Laval University in Québec, PhD Theoretical Physics.

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Elizabeth Hobson

Studying sociality in animals, using rigorous comparative methods and an evolutionary perspective, has the potential to provide insight into how the extreme sociality of some species emerged. My goal is to understand patterns and evolutionary pathways of social and cognitive complexity through investigating what individuals understand about their social worlds.

Research goals: Determine how and why animals structure social relationships, detect how feedback between interactions among individuals and emergent collective dynamics shape social behavior, and evaluate patterns in the evolution of social knowledge across species.

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Yoav Kallus

Symmetry and disorder arise in complex systems from a swirl of individual interactions. By understanding the processes at work, we may learn to design systems that maximize beneficial outcomes and frustrate unwanted ones.

Research goals: Explain what physical processes lead simple building blocks to come together in complex structures and what kinds of structures can be made to form.

Fields: Cornell University, PhD Physics

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Chris Kempes

Evolutionary processes and ecological structure are undoubtedly defined by the complex intersection of numerous constraints. My goal is to synthesize these into mathematical frameworks where it is possible to understand the dominant mechanisms organizing various biological systems.

Research Goals: Systematically understand how different physical and abstract constraints organize evolutionary and ecological structure at different scales.

Fields: PhD 2013, Physical Biology, Massachusetts Institute of Technology; BA 2007, Physics, Colorado College.

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Artemy Kolchinsky

Information theory provides measures for quantifying the organization of various systems. Recent developments have shown that these measures also specify the minimal energy required to process information, whether this is done by a living cell, a digital computer, or any other device. Further exploration of this connection will shed light on the fundamental relationship between the seemingly-ephemeral world of information and the seemingly-concrete world of physical law.

Research Goals: To understand the constraints and organization of information processing in biological, neural, and physical systems.

Fields: PhD in Informatics (Complex Systems), minor in Cognitive Science; Indiana University, Bloomington

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Dan Larremore

The recombination processes that shuffle and mix genes and genomes provide pathogens with highly complicated ways of evolving deftly and rapidly. Decoding these abilities is essential to building a complete understanding of the evolution and pathology of diseases like malaria.

Research Goals: Develop mathematical frameworks and network methods capable of modeling the complicated and recombinant processes underlying ongoing pathogen evolution.

Fields: PhD 2012, Applied Mathematics, University of Colorado at Boulder; MS 2009 Applied Mathematics; BS 2005 Chemical Engineering, Washington University in St. Louis.

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Eric Libby

Multicellular organisms have single-celled ancestors, but the conditions responsible for their transitions are unknown. Examining these shifts can shed light on our own evolutionary history and might guide us as we design organisms to do useful tasks in industry and medicine.

Research goals: Map the routes from unicellularity to primitive multicellularity, with particular focus on life at the in-between stages where organisms might have adopted less conventional forms.

Fields: McGill University PhD Quantitative Physiology;  New Zealand Institute for Advanced Study, Postdoc Mathematical Microbial Evolution

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Eleanor Power

Establishing trusting, cooperative, mutually beneficial relationships with others requires overcoming some major hurdles, yet humans are eminently adept at doing so. Understanding how we accomplish this will help us put in place better mechanisms to restore such systems when they have broken down.

Research Goals: Broaden our understanding of signaling systems through a close study of dramatic and subtle religious signals and their reputational, social structural, and economic consequences.

Fields: PhD 2015, Anthropology, Stanford University; MSc 2008, Human Evolution & Behaviour, University College London; BA 2007, Anthropology and Old World Archaeology and Art, Brown University.

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Caitlin Stern

Social animals, from bees to people, come together in groups of varying sizes and complexities. Understanding how animal groups from and evolve might help us design better human social institutions.

Research goals: Develop a general theory for the role of competition in the evolution of sociality among humans and other social animals by incorporating both the behavioral flexibility and population complexity evident in nature.

Fields: Cornell University, PhD Behavioral Ecology and Evolutionary Biology

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Brendan Tracey

As complexity increases, there are typically a number of different ways to acquire knowledge about the problem at hand. The objective function may be broken down into systems that each have their own internal structure but are also interdependent, as in multi-disciplinary optimization. Alternatively, there may be a tradeoff between simulation time and accuracy, as in multi-fidelity optimization. The question is how to effectively use all of these sources of information to efficiently find a good design.

Research goals: To apply non-traditional ideas to engineering analysis and design. Traditional optimization techniques seek to find the minimum of a single objective function (black-box or convex), I will be researching Multi-Information Source Optimization (MISO) and using techniques from statistics, machine learning, game theory, and single-function optimization.

Fields: Stanford University, PhD in Aeronautics and Astronautics; Stanford University, M.S. in Aeronautics and Astronautics; University of Rochester, B.S. in Mechanical Engineering

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