Calendar

Mar
19
Tue
2013
“Complex Systems Science: Solving Complex Problems in a Complex World” Seminar @ Bloomberg School of Public Health Room W2008
Mar 19 @ 12:15 pm – 1:15 pm

Dr. Yaneer Bar-Yam is the founder and president of the New England Complex Systems Institute. He received his SB and PhD in physics from MIT in 1978 and 1984 respectively. His work explores the origins and impacts of market crashes, ethnic violence, military conflict and pandemics, analyzes social networks, as well as the bases of creativity, panics, evolution and altruism. His work on the causes of the global food crisis was cited as among the top 10 scientific discoveries of 2011 by Wired magazine. Dr. Bar-Yam has advised governments, NGOs, and corporations on using principles and insights from complex systems science to solve seemingly intractable problems. He is the author of two books: his textbook Dynamics of Complex Systems, which he has taught to over 2,000 graduate students, professionals and executives, and Making Things Work, which describes the use of complex systems science for solving problems in healthcare, education, systems engineering, international development, and ethnic conflict.

Mar
3
Tue
2015
Dr. Eunshin Byon to present two seminars on Mar 3 @ Ames Hall, Room 302
Mar 3 @ 12:00 pm – 3:00 pm

Dr. Eunshin Byon

Dr. Eunshin Byon is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor, USA.  She received her Ph.D. degree in Industrial and Systems Engineering from the Texas A&M University, College Station, USA, and joined the University of Michigan in 2011.

Importance Sampling for Reliability Evaluation with Stochastic Computer Models (12-1:30pm)
ABSTRACT:

Importance sampling has been used to improve the efficiency of simulations where the simulation output is uniquely determined, given a fixed input. We extend the theory of importance sampling to estimate a system’s reliability with stochastic simulations. Thanks to the advance of computing power, stochastic computer models are employed in many applications to represent a complex system behavior. More

 Data-driven Modeling and Analysis for Wind Power Systems (1:30-3pm)
ABSTRACT:

To quantify and minimize the uncertainties in the design and operational stage, we model and analyze the dependency of wind turbine responses (e.g., power generation, loads and condition monitoring sensor measurement) on operating conditions and the interactions among turbines. Our research entails several areas… More

Center for Systems Science and Engineering