Covered Topics: Computational Complex Systems

The Jacob T. Schwartz International School for Scientific Research in 2010 opened a new set of summer schools on Computational Complex Systems

The 2013 edition addresses Ph.D. students, junior and senior researchers to the forefront of research activity on data mining and computational models for dynamic networks and social behavior. Recognized authorities in the field will give formal lectures tailoring these for an interdisciplinary audience. Participants will also have the opportunity to present the results of their research during a short communication session. As a whole, the planned summer school will allow participants to be exposed to cutting edge results in one of the most challenging research area, while at the same time enjoying the relaxing atmosphere of one of the most beautiful italian islands.

Covered Topics: Machine Learning

Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can ?see? and detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is softwares that are trained rather than programmed to solve a task.

Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high- dimensional learning. Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design new ones.

Starting from classical notions of smoothness, shrinkage and margin, the course will cover state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning, feature selection, structured prediction, multitask learning and model selection. Practical applications for high dimensional problems will be discussed.

The classes will focus on algorithmic and methodological aspects, while trying to give an idea of the underlying theoretical underpinnings. Practical laboratory sessions will give the opportunity to have hands on experience.

Covered Topics: Computer security and cryptography, System modelling, Medical devices, Sensor networks


Cyber-Physical Systems (CPS) are engineered systems that are built from and depend upon the synergy of computational and physical components. CPS are becoming an important part of our daily life. Humans build, deploy, use, and maintain these systems that can sense our activities and influence our behavior. Examples of the many CPS application areas include the smart electric grid, smart transportation, smart buildings, smart medical technologies, next-generation air traffic management, and advanced manufacturing. CPS are expected to have great technical, economic and societal impacts in the near future.

CPS will transform the way people interact with engineered systems, just as the Internet transformed the way people interact with information. We will increasingly rely on these networked embedded systems, therefore also requiring a secure and privacy-preserving treatment of sensitive human-centric data. Novel paradigms and solutions are also needed to allow humans to interact with these systems. However, these goals cannot be achieved without rigorous systems engineering. Emerging CPS will be coordinated, distributed, and connected, and must be robust and responsive. The CPS of tomorrow will need to far exceed the systems of today in capability, adaptability, resiliency, safety, security, and usability.

The Summer School on Cyber-Physical Systems aims at bringing together researchers from industry and academia. This school explores the manifold relationship between networked embedded systems and humans as their creators, users, and subjects. The format of the Summer School will be a five-day meeting, organized around different aspects of rigorous systems engineering of Cyber-Physical Systems.


The goal of the summer school is to survey fundamental and applied aspects of CPS and their relationship to humans, as well as to identify novel opportunities and research directions in these areas through a series of lectures by international experts. Participants will also experience the relevant technologies during hands-on courses and be given a chance to present their own work. The school will provide a great opportunity to know other people working in the field, to meet distinguished scholars, and to establish contacts that may lead to research collaborations in the future.

Classes will range from basics of sensors to experience from real-world deployments, security, privacy, and dependability, debugging and validation of networked embedded systems, cyber-physical internet, online data Mining, virtual reality, rigorous CPS engineering, networked control systems, management and maintenance of networked embedded systems, interacting with networked embedded systems, applications such as smart energy and smart health-care.

Students participating at this summer school will learn the current state of the art in CPS and will be able to apply new techniques coming from various communities and backgrounds to their own domain. The CPS Summer School will be held at Grenoble University. Courses will be given in English by experts from industry and academia working in various fields of CPS.

Covered Topics: chaotic dynamics, information theory, systems biology, hybrid systems, quantum computing, and automata-based models and model checking

Formal methods are emerging in computer science as a prominent approach to the rigorous design of computer, communication and software systems. The aim of the SFM series is to offer a good spectrum of current research in foundations as well as applications of formal methods, which can be of interest for graduate students and young researchers who intend to approach the field.

Covered Topics: Cancer Modeling.

Area: Mathematics Prof. Luigi Preziosi Politecnico di Torino, Italy.
Abstract: Multiscale Developments of Cellular Potts Models and Individual Cell-based Models in Cancer Modeling. All biological phenomena emerge from an intricate interconnection of multiple processes occurring at different levels of organization: namely, at the molecular, the cellular and the tissue level. These natural levels can approximately be connected to a microscopic, mesoscopic, and macroscopic scale, respectively. The microscopic scale refers to those processes that occur at the subcellular level, such as DNA synthesis and duplication, gene dynamics, activation of receptors, transduction of chemical signals and diffusion of ions. The mesoscopic scale, on the other hand, can refer to cell-level phenomena, such as adhesive interactions between cells or between cells and ECM components, cell duplication and death and cell motion. The macroscopic scale finally corresponds to those processes that are typical of multicellular behavior, such as population dynamics, tissue mechanics and organ growth and development. One of the most widespread hybrid approaches, that is particularly suitable for cancer modeling and other biological problems, is the Cellular Potts Model, a stochastic Monte Carlo method based on energy minimization principles.
The scope of the series of lectures is to present some innovative multiscale extensions of the Cellular Potts models and of Individual Cell-based models. In particular, we focus on ways to integrate and interface the standard method with detailed descriptions of microscopic dynamics located not only in the external space but also within the simulated elements. We aim therefore to introduce some nested characteristics in the basic hybrid environment, that realistically reproduce the multiscale organization typical of biological development, where the individual behavior is driven by the constant interplay between different levels of description.

Area: Computer Science Dr. Pietro Liò University of Cambridge, UK.
Abstract: Medicine is moving from reacting to a disease to a proactive P4 medicine: personalized, predictive, preventive and participatory medicine. A difficult step is to bridge the actual distance between biomedical research and clinical practice. I would like to focus on the challenges in data analysis and modeling in cancer bioinformatics. In particular I would like to discuss the following issues:
Multi omics (1 lecture): Expensive and complex data are gathered and analysed in a rather simple way that completely misses the opportunity to uncover combinations of predictive and meaningful profiles among the omics data. Novel methodological frameworks, beyond single datasets, should integrate multilevel omics data to bring biological understanding to the next level. "Super-Meta" methods combining multilevel data across populations need to be developed. Omics may include HI-C, epigenetic, gene expression and sequence data; they are not independent each other. I will provide details of the algorithms and the software
Multi scale modeling (1 lecture): A disease manifests first as a dysfunction at the cell level and is then translated at the tissue level due to a change in the cell response. Here I am considering tgb-beta as a coupling factors for modeling breast cancer at different scales (from molecules to tissues).
Multi morbidities (1 lecture): Comorbidity addresses the occurrence of different medical conditions or diseases, usually complex and often chronic ones, in the same patient. I am addressing bone diseases as a secondary effect of several types of cancers.
Multi objective optimisation (1 lecture): If there is time I will show use multi objective optimisation to investigate how energetic factors enters the tissue dynamics.

Area: Biology Dr. Francesca Ciccarelli IEO-IFOM Milano, Italy.
Abstract: In this series of lectures I will discuss the recent advances in on our understanding of cancer genetics and evolution. I will start by reviewing the accumulating evidence of cancer heterogeneity in terms of acquired genetic mutations and genomic rearrangements. I will then describe the impact of these novel results on our modeling of cancer networks. In the last lectures, I will focus on the current attempts of using large-scale genomics data for rebuilding tumor evolution and how this is changing also anti-cancer therapeutic approaches.


This short 3-day school is meant to provide a self-contained comprehensive introduction to modern video surveillance methods and techniques, with a good balance between theory and practical applications.
This school is open to researchers, PhD and undergraduate students, scholars in the field of surveillance and security, and is conceived also for technicians from both industries and public entities.

The courses of the school will be taught by distinguished scientists of the field worldwide working in the video surveillance research. For this reason, the envisioned lessons will tackle the problems related to surveillance from different perspectives, ranging from tracking systems, behavior analysis, sensor networks to basic algorithmic components to state-of-the-art high-level techniques.

The scope is to provide also the capabilities to build from scratch a simple video surveillance system (from object extraction to high-level scene understanding).
For this purpose, a demo session willl be organized where different research algorithms may be tested on your own data.

Covered Topics: Modal Logic, Category Theory, and other related fields

The Midlands Graduate School (MGS) provides an intensive course of lectures on the Mathematical Foundations of Computing Science. It has run annually since 1999 and has been held at either the University of Birmingham, the University of Leicester, the University of Nottingham, or at the University of Sheffield. The lectures are aimed at graduate students, typically in their first or second year of study for a PhD. However, the school is open to anyone who is interested in learning more about mathematical computing foundations, and all such applicants are warmly welcomed. We very much encourage students from abroad to attend, and many have done so in the past.

Students of MGS are typically extremely pleased with our events, giving us very high satisfaction ratings, and 100% of students in recent years say they would recommend MGS. This is our tenth anniversary and we hope that you will be able to take part!

More informations here.

Covered Topics: Constraint programming

Constraint programming (CP) is a programming paradigm that provides useful tools to model and efficiently solve constrained optimisation problems, such as complex resource planning, scheduling, configuration and design.The Association for Constraint Programming (ACP) is a non-profit association that promotes constraint programming in every aspect of the scientific world. It encourages its theoretical and practical developments, its teaching in academic institutions, its adoption in the industrial world, and its use in the the application fields. Every year ACP supports the organisation of the ACP Summer School with the aim to augment and complement university teaching of CP, and to disseminate a core body of CP knowledge supporting the recognition of CP as a mature and relevant technology for use in industry.