Month: October 2016

Four Tenure-Track Positions in Computer Science & Complex Systems

The College of Engineering and Mathematical Sciences (CEMS) at the University of Vermont (UVM) is seeking applications for four tenure-track faculty positions in Computer Science and Complex Systems, with a Fall 2017 start date. These positions will be at the rank of Assistant Professor, or Associate Professor with tenure for outstanding candidates already at that rank. We seek candidates with active research in one or more of the following areas:
• Cybersecurity, especially in languages and verification, or applications of machine learning or complex systems approaches to cybersecurity.
• Computational Intelligence, broadly defined to include data mining, machine learning, data science, bio-inspired approaches, and Deep Learning, with broad potential for applications to Big Data in areas such as biology, medicine, cybersecurity, social science, sociotechnical systems, and/or environmental science.
• Complex Systems, modeling and/or analysis of emergent phenomena allied with data-driven empirical work, ideally with applications in biology, medicine, cybersecurity, the social sciences, sociotechnical systems, and/or environmental science.
• Computational Biology, computational approaches to the study of biological systems such as in genomics, proteomics, phylogenetics, biological pathways or networks, etc.

Source: www.uvm.edu

Control principles of complex systems

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

 

Control principles of complex systems
Yang-Yu Liu and Albert-László Barabási
Rev. Mod. Phys. 88, 035006

Source: journals.aps.org

Foundations of Data Science

While traditional areas of computer science remain highly important, increasingly re- searchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make com- puters useful on specific well-defined problems. With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as an understanding of automata theory, algorithms and related topics gave students an advantage in the last 40 years. One of the major changes is the switch from discrete mathematics to more of an emphasis on probability, statistics, and numerical methods.

 

Foundations of Data Science
by Avrim Blum (CMU), John Hopcroft (Cornell), and Ravindran Kannan (MSR) 
June 2016
Available at https://www.cs.cornell.edu/jeh/book2016June9.pdf

Source: www.cs.cornell.edu

Stochastic dynamics and the predictability of big hits in online videos

The competition for the attention of users is a central element of the Internet. Crucial issues are the origin and predictability of big hits, the few items that capture a big portion of the total attention. We address these issues analyzing 10 million time series of videos’ views from YouTube. We find that the average gain of views is linearly proportional to the number of views a video already has, in agreement with usual rich-get-richer mechanisms and Gibrat’s law, but this fails to explain the prevalence of big hits. The reason is that the fluctuations around the average views are themselves heavy tailed. Based on these empirical observations, we propose a stochastic differential equation with Le\’evy noise as a model of the dynamics of videos. We show how this model is substantially better in estimating the probability of an ordinary item becoming a big hit, which is considerably underestimated in the traditional proportional-growth models.

 

Stochastic dynamics and the predictability of big hits in online videos
Jose M. Miotto, Hogler Kantz, Eduardo G. Altmann

Source: arxiv.org