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Québec City, Québec, December 13-18, 2020

Hashtag: #CNWW20
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@CNWWs

The Complex Networks Winter Workshop (CNWW) is a week-long international school that offers an extraordinary opportunity for participants to engage in rigorous transdisciplinary complexity science research alongside some of the top researchers in the field of networks. The CNWW is designed for graduate students, postdoctoral fellows, and professionals. The lectures will present open problems and recent advances in the field of complex networks. Participants of this program will collaborate in small transdisciplinary research groups involving other participants as well as faculty. All course lectures will be given in English.

Program Dates: December 13-18, 2020
Arrival/Departure Dates: December 12/19, 2020

The CNWW is a collaboration between the University of Vermont Complex Systems Center and the Sentinel North Program of the Université Laval.

To sign up for notifications about CNWW click here.

Up to 40 international graduate students, postdoctoral fellows and professionals from different disciplines will be accepted. Application Deadline: June 1, 2020


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  • Faculty


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    Laurent Hébert-Dufresne
    CNWW Director
    @LHDnets

    Assistant Professor, University of Vermont Department of Computer Science; Member, The Vermont Complex Systems Center

    Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

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    Antoine Allard
    CNWW Director
    @all_are

    Assistant professor, Département de physique, de génie physique et d'optique, Université Laval, Québec, Canada

    Antoine's research combines statistical mechanics, graph theory, nonlinear dynamics and geometry to develop mathematical models of complex networks and to study the structure/function relationship specific to complex systems. His recent projects involve the mapping of real complex networks unto hyperbolic space to characterize the evolution of international trade, the use of greedy routing to unveil the spatial organization of the brain at various scales and across species, and the analytical solution of percolation on networks with a strong induced core-periphery structure to assess the potential of the Zika virus as a sexually transmitted infection.

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    Elizabeth Hobson
    Assistant Professor, Department of Biological Sciences, University of Cincinnati
    @Eliz_Hobson

    I am a biologist who specializes in animal behavior, behavioral ecology, cognitive ecology, and social cognition. My research focuses on social information: what animals know about their social worlds, how they come to know it, and what they do with that information.

    To address these questions, I integrate aspects of ecology and evolution to determine how the combination of sociality and cognition affect the emergence of group social structures from a combination of individual-level social actions, cognitive abilities, and decisions about future interactions. Combining this perspective into a feedback loop, and developing and applying new quantitative tools, allows me to back-infer what animals know about their social worlds by looking at how their decisions about social interactions are contingent on different kinds of social knowledge. Detecting the use of social knowledge provides new insight into the connections between social decisions and cognitive processing, how they can be affected by ecological dynamics, and how they can lead to the evolution of complex sociality.

    Much of my previous research focused on avian sociality, where I investigated social behavior and network structures in parrots. In my current research I apply these tools to a much wider range of species, from ants to primates, and even humans, while incorporating more quantitative and computational approaches.

    I am currently an Assistant Professor at the University of Cincinnati. My goal for research in my lab is to use rigorous comparative methods and an evolutionary perspective to discover ways in which the extreme sociality of some species emerged, through investigating what individuals understand about their social worlds and the strategies individuals use to balance the costs and benefits of social living.


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    Daniel B. Larremore
    Asst. Professor, Univ. Colorado Boulder
    BioFrontiers Institute & Dept. of Computer Science
    @DanLarremore

    My research focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in social and biological systems. I try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems.

    I obtained my PhD in Applied Mathematics from the University of Colorado Boulder in 2012, advised by Juan G. Restrepo, after which I spent three years as a postdoctoral fellow at the Harvard T.H. Chan School of Public Health studying the genetic epidemiology of malaria in the Center for Communicable Disease Dynamics. I then joined the Santa Fe Institute as an Omidyar Fellow until 2017, when I joined the faculty at the University of Colorado Boulder in the Department of Computer Science and the BioFrontiers Institute.
    Malaria's antigenic variation and evolution - The var genes of the malaria parasite P. falciparum evolve according to complicated and unknown rules, with selective pressures at multiple scales both within hosts and between hosts. I create and use mathematical tools to understand the structural and evolutionary constraints on var gene evolution, and their relationships with parasite virulence, population structure, and epidemiology.

    Networks and theory - The processes that generate complex networks leave hints about themselves in the patterns of edges, and the relationships between those patterns and vertex metadata. I work on mathematical descriptions of graph ensembles, inference of community structures, vertex ordering or ranking, and using metadata to better understand network formation.
    The scientific ecosystem - The scientific method of hypothesis, experiment, and conclusion poorly describes modern scientific discovery and productivity. Instead, science is done by people who play various social roles in the ecosystem of science. I investigate faculty hiring, productivity patterns, scientific careers, and the dynamics of discovery through large-scale data collection and modeling.


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    C. Brandon Ogbunu
    Assistant Professor of Ecology and Evolutionary Biology
    Brown University
    @big_data_kane

    C. Brandon Ogbunu is an Assistant Professor in the Department of Ecology and Evolutionary Biology at Brown University. He is an evolutionary systems biologist, and uses experimental evolution, mathematical modeling, and computational biology to better understand the underlying causes and consequences of disease, across scales: from the biophysics of proteins involved in drug resistance to the social determinants underlying disease. In doing so, he aims to develop theory that enriches our understanding of the evolutionary and ecological underpinnings of disease, while contributing to practical solutions for clinical medicine and public health. Read more about his research and activities here:

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    Patrick Desrosiers - Affiliate Professor, Laval University , Centre de recherche CERVO; Département de physique, Université Laval; Dynamica research group


    Theoretical and mathematical physicist Fields of interest: classical and quantum integrable systems, symmetric functions, random matrix theory, orthogonal polynomials in many variables, representation theory, conformal field theory, supersymmetry, complex systems.



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    Jean-Gabriel Young
    James S. McDonnell Foundation Postdoctoral Fellow
    Center for the Study of Complex Systems, University of Michigan
    @jgyou

    I am a Postdoctoral Fellow at the Center for the Study of Complex Systems of the University of Michigan, supported by the James S. McDonnell Foundation Fellowship. I focus on statistical inference for complex systems and complex networks. I obtained my PhD in Physics from Université Laval, where I was advised by Prof. Louis J. Dubé and Prof. Patrick Desrosiers.

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    Samuel Scarpino
    Assistant Professor in the Network Science Institute at Northeastern University.
    @svscarpino

    I am an Assistant Professor of Marine & Environmental Sciences and Physics and a core faculty member in the Network Science Institute at Northeastern University. I am also the Chief Data Scientist at Dharma.ai. My research spans a broad range of topics in complex systems and network science, including: infectious diseases, forecasting and predictive modeling, disease genomics and transcriptomics, outbreak surveillance, network science, and decision making under uncertainty. Our group, the Emergent Epidemics Lab, approaches these topics by investigating questions at the intersection of biology, behavior, and disease.

  • Partners
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  • Program Overview
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    The CNWW will focus on the themes detailed below and promote a lively, versatile and dynamic approach that includes lectures and a strong hands-on component framed by experts from a variety of disciplines.

    -Introduction to Network Theory
    -Random Networks
    -Social, Ecological, Technological Networks
    -Epidemiological Networks
    -Big Data and Complex Networks
    -Complex Networks applications in Northern Research

    The training will also allow the development of interpersonal skills such as networking and international scientific collaboration, creativity and communication in a transdisciplinary research context.
  • Application Requirements
    • Completed application form
    • Up-to-date curriculum vitae

    Applications will be open until June 1, 2020.

    Applications will be accepted from graduate students, postdoctoral fellows, faculty, and professionals. We envision a diverse cohort of participants for the CNWW, applicants from all disciplines with an interest in networks are encouraged to apply. Proficiency in English and some background in science or mathematics are required. Participants are expected to attend the entire session. Applicants are welcome from all geographic regions. Underrepresented minorities and women are encouraged to apply.

    To sign up for notifications about CNWW click here.

    A limited number of tuition waivers will be given on the basis of merit and need. Please contact juniper.lovato@uvm.edu for more information

  • Testimonials
    The CNWW allowed me to establish bonds with my peers in network science from around the globe. Since the workshop, I have been connecting with them and I think these could become lasting friendships that will foster collaboration and a broader, more diverse understanding of network science through casual discussion.

    Sam Rosenblatt
    University of Vermont


    The most engaging workshop I have ever attended! The organizers put great effort in encouraging a collaborative, multi-disciplinary environment where ideas about complex networks could be explored.

    Niall Keleher
    U.C. Berkeley


    The multiple disciplines represented by all the participants, the knowledgeable and highly approachable lecturers and mentors, the multicultural atmosphere and the historical location, the scientific content of the lectures, the imaginative research ideas, and the intense collaborative work that emerges during this event make CNWW one of most satisfying workshops that I have ever attended.

    José R. Nicolás-Carlock
    National Autonomous University of Mexico


    CNWW was the perfect scholarly vacation. I learned so much, made new friends, experienced a beautiful city, and had a safe and supportive environment to take intellectual risks beyond what my typical responsibilities allow. I truly learned something from every single person I met at CNWW, and each welcomed me as someone from whom they could learn as well. I returned exhausted and exhilarated, brimming new knowledge and perspectives and excited for the cross-disciplinary connections I made with scholars from around the world. I would highly recommend participation in CNWW; the experience made me a better scholar and brought renewed vigor to my work. I am so thankful I had the opportunity to participate.

    Sarah Shugars
    Network Science Institute, Northeastern University


    CNWW was a think tank, a refreshing intellectual experience that got me thinking about interesting topics that I otherwise have no time to think of in my daily work-life.

    Anonymous
  • Contact

    For any additional information, please contact:

    Juniper Lovato
    Director of Education and Outreach for Complex Systems
    The Vermont Complex Systems Center
    juniper.lovato@uvm.edu


More Information


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