Invited speaker: Ioannis Kevrekidis (Pomeroy and Betty Perry Smith Professor of Engineering, CBE, PACM and Mathematics, Princeton University) Yannis Kevrekidis studied Chemical Engineering at the National Technical University in Athens. He then followed the steps of many alumni of that department to the University of Minnesota, where he studied under Rutherford Aris and Lanny Schmidt (also Dick McGehee and Don Aronson in Mathematics) on computational studies of dynamical systems, which still remains the main theme of his research. He was a Director's Fellow at Los Alamos in 1985-86. He has been at Princeton since 1986, where he teaches Chemical Engineering and also Applied and Computational Mathematics. His research interests are centered around the dynamics of physical and chemical processes, types of instabilities, pattern formation, and their computational study. In more recent years he has developed an interest in multiscale computations. He has been a Packard Fellow, a Guggenheim Fellow and the Ulam Scholar at LANL. He holds the Colburn and Wilhelm Awards of the AIChE, and a Humboldt Prize. Last year he was the Gutzwiller Fellow at the Max Planck Institute for the Physics of Complex Systems in Dresden.
Abstract: In current modeling practice for complex systems, including agent-based and network-based simulations, the best available descriptions of a system often come at a fine level (atomistic, stochastic, individual-based) while the questions asked and the tasks required by the modeler (parametric analysis, optimization, control) are at a much coarser, averaged, macroscopic level. Traditional modeling approaches start by deriving macroscopic evolution equations from the microscopic models. I will review a mathematically inspired, systems-based computational enabling technology that allows the modeler to perform macroscopic tasks acting on the microscopic models directly in an input-output mode. This "equation-free" approach circumvents the step of obtaining accurate macroscopic descriptions. I will discuss applications of this approach and its linking with recent developments in data mining algorithms, exploring large complex data sets to find good "reduction coordinates".
Invited speaker: Jeffrey Wilcox (Vice President for Engineering, Lockheed Martin Corporation) Jeffrey J. Wilcox is the Vice President for Engineering at the Lockheed Martin Corporation. In this capacity, he is responsible for leading the development and execution of engineering strategy for the Lockheed Martin Engineering Enterprise and its 60,000 engineers, scientists, and technologists. Previously, Mr. Wilcox was the Vice President for Systems and Software Engineering at Lockheed Martin. In that role, he was responsible for directing the development and implementation of enterprise-wide systems and software engineering processes, tools, technology, and training with special emphasis on complex, software-intensive systems development. Prior to joining Lockheed Martin, Mr. Wilcox served in a variety of increasingly responsible positions at the Science Applications International Corporation (SAIC), including Senior Vice President, where he led business planning and analysis for the Technology and Advanced Systems business unit. Mr. Wilcox graduated from Drexel University with a master's degree in Electrical Engineering and Case Western Reserve University with a degree in Biomedical Engineering. Mr. Wilcox holds an honorary doctorate of Engineering from Stevens Institute of Technology. Mr. Wilcox is an American Institute of Aeronautics and Astronautics (AIAA) Associate Fellow and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
Abstract: Organizations like Lockheed Martin have evolved over the past half-century to solve highly-complex problems for a highly-complex set of stakeholders. During this time, the systems engineering discipline has been at the core of how our industry approached and met these challenges. It has served us well for decades. In recent years, however, the increasing complexity of these challenges has stressed our ability to sense and adapt to a terrain that is rapidly changing in multiple dimensions. This talk will explore those changes, identify where traditional approaches are being stressed, and provide suggestions for how the research community can advance and support the creation of effective complex systems and enterprises.
Invited speaker: Rene Doursat (Complex Systems Institute, CNRS and Ecole Polytechnique, Paris, France) Rene Doursat is a Research Scientist and former Director of the Complex Systems Institute, Paris, under the French research council CNRS. He also co-founded the European Complex Systems Master's at Ecole Polytechnique, Paris, where he is an Adjunct Lecturer. Previously, he was a Visiting Assistant Professor in computer science at the University of Nevada, Reno, after an engineering period in the San Francisco Bay Area's software industry. An alumnus of Ecole Normale Superieure, Paris, he completed his PhD in 1991 and a postdoc in computational neuroscience at the Ruhr-Universitat Bochum, Germany. The main theme of Rene Doursat's research is bio-inspired models and simulations of "morphogenetic engineering" systems (book with Springer-Verlag), i.e. how complex architectures (e.g. software, robotic, network, neural) can self-organize from a swarm of heterogeneous agents via dynamical, developmental, and evolutionary processes. He was the General Chair of ECAL 2011, the European Conference on Artificial Life, and organized or created a dozen other conferences and workshops. He wrote over 100 publications, among which 40 full papers and chapters, and 10 edited books, proceedings and journal issues. In 2013, he moved to Washington DC, and received formal affiliations with Drexel University and George Mason University. He also holds a teaching appointment at the School of Engineering of The Catholic University of America in DC
Abstract: Engineering is torn between an attitude of strong design and dreams of autonomous devices. It wants full mastery of its artifacts, but also wishes these artifacts were much more adaptive or "intelligent". Meanwhile, the escalation in system size and complexity has rendered the tradition of rigid top-down planning and implementation in every detail unsustainable. In this context, natural complex systems, large sets of elements interacting locally and behaving collectively, can constitute a powerful source of inspiration and help create a new generation of artificial systems with the desired "self-x" properties absent from classical engineering. Historically, along these lines, the observation of neurons and genes has given rise to machine learning and evolutionary algorithms. Yet, these domains have also shifted their focus toward classical optimization and search problems, away from emergent computation.