Thursday March 16, 2017 │Samberg Conference Center (E52-6th Floor)
4:15 PM Keynote│5:15 PM Poster Session │6:00 PM Reception

Navigating Technology at the Limits of Comprehension
Dr. Samuel Arbesman

From the power grid to the stock market to the latest iOS, complex systems are plagued by unintended glitches, unpredictable behavior, and unexplainable system failures. Why can’t we make things simpler? Is technological complexity inevitable? And how are we supposed to deal with technology that nobody can understand anymore? This talk explores the forces that lead us to continue to make systems more complicated and more incomprehensible, despite our desire for them to be more coherent. A framework for thinking about and handling these complex systems is provided, which involves numerous approaches, including importing ideas from biology and philosophy into the realm of technology, as well as how to rekindle generalists in the face of ever-increasing specialization.

Dr. Samuel Arbesman is a complexity scientist, whose work focuses on the nature of scientific and technological change. His recent book, "Overcomplicated: Technology at the Limits of Comprehension," analyzes the consequences of the incredibly complex and interdependent algorithms and systems we have engineered, and discusses how to ensure reliability in an increasingly unpredictable environment.

Dr. Arbesman is currently Scientist in Residence at Lux Capital, Senior Fellow at the Silicon Flatirons Center at the University of Colorado, and Research Fellow at the Long Now Foundation. Previously, he was a Senior Scholar at the Ewing Marion Kauffman Foundation and a Research Fellow at Harvard Medical School. He holds a PhD in computational biology from Cornell University, and a BA in computer science and biology from Brandeis University.

Thursday, March 23rd 2017 | 12:00 PM* | 37-212

Optimal interpolatory model reduction: Moving from linear to nonlinear dynamics
Serkan Gugercin
Department of Mathematics
Virginia Tech, Blacksburg

Numerical simulation of large-scale dynamical systems plays a crucial role and may be the only possibility in studying a great variety of complex physical phenomena with applications ranging from heat transfer to fluid dynamics, to signal propagation and interference in electronic circuits, and many more. However these large-scale dynamical systems present significant computational difficulties when used in numerical simulation. Model reduction aims to reduce this computational burden by constructing simpler (reduced order) models, which are much easier and faster to simulate yet accurately represent the original system. These simpler reduced order models can then serve as efficient surrogates for the original, replacing them as components in larger systems; facilitating rapid development of controllers for real time applications; enabling optimal system design and uncertainty analysis.

For linear dynamical systems, model reduction has achieved great success. In the case of linear dynamics, we know how to construct, at a modest cost, (locally) optimal, input-independent reduced models; that is, reduced models that are uniformly good over all inputs having bounded energy. In addition, in some cases we can achieve this goal using only input/output data without a priori knowledge of internal dynamics. Even though model reduction has been successfully and effectively applied to nonlinear dynamical systems as well, both the reduction process and the reduced models are usually input dependent and the high fidelity of the resulting approximation is generically restricted to the training input/data. In this talk, we will offer remedies to this situation. First, we will review model reduction for linear systems by using rational interpolation as the underlying framework. The concept of transfer function will prove fundamental in this setting. Then, we will show how rational interpolation and transfer function concepts can be extended to nonlinear dynamics, specifically to bilinear systems and quadratic-in-state systems, allowing us to construct input-independent reduced models in this setting as well. Several numerical examples will be illustrated to support the discussion.

* Lunch available at 11:45

Thursday, April 27th 2017 | 12:00 PM* | 37-212

Misha Kilmer
Professor of Mathematics, Adjunct Professor of Computer Science
Tufts University

* Lunch provided at 11:45