The small conference “Modelling complex urban environments,” will be held June 21-22 at the University of Waterloo and is sponsored by the Waterloo Institute for Complexity and Innovation and the University of Waterloo International Research Partnership Development Grant program. Urban scholars have approached urban complexity using a variety of modelling tools, from descriptive models, atomistic discrete simulation models, systems dynamics models, and most recently, inductive analysis of new sources of “big” data. This conference aims to bring together these scholar groups to examine current issues in urban complexity from a multi- lens perspective. Initial identified themes and potential additional themes are identified in the Call for Papers, available at https://uwaterloo.ca/complexity- innovation/events/wici-conference-modelling-complex-urban-environments.
Proposals for additional themes, organized sessions, and hands-on activities are welcome. Keynote speakers include:
• Geoffrey West, Santa Fe Institute

• Saskia Sassen, Columbia University
• Alex Anas, University of Buffalo

Source: uwaterloo.ca

The basis of intelligence – how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines – is arguably the greatest problem in science and technology. To solve it, we will need to understand how human intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor ultimately will enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies, such as Watson and Siri, are impressive, but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations; few view this as brain-like or human intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise to build much more robust and sophisticated algorithms implemented in intelligent machines. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the development of true biologically inspired AI.

Brains, Minds and Machines
Directors: Gabriel Kreiman, Children’s Hospital, Harvard Medical School; Boris Katz, Massachusetts Institute of Technology; and Tomaso Poggio, Massachusetts Institute of Technology
Location: Marine Biological Laboratory, in Woods Hole, MA.
Course Dates: Aug. 9th – Aug. 30th, 2018
Application deadline: April 9, 2018

Source: cbmm.mit.edu

The VIII Summer School on Statistical Physics of
Complex Systems promoted by the Topical Group on Statistical and Nonlinear
Physics (GEFENOL) of the Spanish Physical Society. The School follows the
previous successful editions which took place in Palma de Mallorca (2011,
2013, 2014, 2017), Benasque (2012), Barcelona (2015) and Pamplona (2016).

The school, oriented to Master and PhD students and young postdocs worldwide
will take place at IFISC (CSIC-UIB) in Palma de Mallorca, Spain, on July 2-13, 2018
and will include six courses:

* Stochastic delay systems, by Tobias Galla, The University of Manchester.
* Bayesian inference in complex networks, by Marta Sales-Pardo, Universitat Rovira i Virgili.
* Deep learning from a statistical physics perspective, by Raúl Vicente, Univerty of Tartu
* Spreading phenomena in networks, by Claudio Castellano, Istituto dei Sistemi Complessi (ISC-CNR).
* Geometric frustration in soft matter systems, by Pietro Tierno, Universitat de Barcelona.
* Transport in nanosystems, by David Sánchez, IFISC (CSIC-UIB).

Source: school2018.gefenol.es

Network science is having a huge impact in various aspects of health-related research. The advent of more comprehensive personalized data has fostered the emergence of precision medicine: an approach that considers individual genetic and physiological characteristics, lifestyle and environment in devising personalized therapies. Such an approach benefits greatly from big data and network approaches at multiple levels. First, integrating different molecular “interactomic” datasets from a single patient, from subcellular to organ-level, is a fundamental step towards understanding the mechanistic underpinnings of personalized health states. Moreover, there has been a continued improvement in the availability of health-related data, such as electronic health records detailing patient histories and providing accurate diseases statistics.

Network Medicine ’18
Personalized Medicine in the Era of Big Data

Source: sharmalab.bwh.harvard.edu