February 1, 2:30pm, Room 103 John T. Rettaliata Engineering Center Experience suggests that uncertainties often play an important role in quantifying the performance of complex systems. Therefore, uncertainty needs to be treated as a core element in the modeling, simulation, and optimization of complex systems. The field of uncertainty quantification…
N. Sukumar, Virtual Element Method in Computational Mechanics
Generalized barycentric coordinates (e.g., Wachspress, mean value coordinates, maximum-entropy coordinates, and harmonic coordinates) have been widely adopted for applications in computer graphics and as basis functions in polytopal finite element methods. Over the past decade many new formulations on polygonal and polyhedral meshes have been developed. A few examples of…
Boris Glavic, IIT DBGroup Research Profile
In this talk I give an overview of my research interests in database systems spanning from data provenance, over building the next-generation high performance and distributed query engines, to supporting data science through transparency, data cleaning, and management of uncertainty in data. Boris Glavic is an Associate Professor of Computer…
Jay Schieber, Modeling Dynamics In Soft Matter
Prof. Jay Schieber is the Director of the Center for molecular study of condensed soft matter (μCoSM), Professor of Chemical and Biological Engineering, Professor of Physics, and Professor of Applied Mathematics, all at the Illinois Institute of Technology. He received his bachelors degree in Chemical Engineering at the University of…
Jeffrey Larson, Numerical Optimization of Computationally Expensive Functions at Argonne National Laboratory
The increase in computational resources in the past decades has resulted in computationally expensive numerical simulations appearing in nearly all scientific domains. Often times, domain scientists wish to identify inputs to the simulation that produce some form of optimal behavior. In this talk, we focus on algorithmic approaches for optimizing…
Anita Nikolich, A Look Inside the Federal Funding Machine
Scientific discovery has become more interdisciplinary and collaborative, necessitating cooperation within and between universities and partner organizations. The priorities of Federal funding agencies and the rationale behind grant decision-making is sometimes confusing or mysterious. I’ll offer my observations from four years as an NSF Program Director and some suggestions on…
Lulu Kang, Statistical Design and Modeling for Physical and Computer Experiments
New challenges are rising in the traditional statistical design and modeling for experimental data areas. When the experiments have complex structure, including quantitive and qualitative data type, or functional data output, new design and modeling methods for such complicated experiments are introduced. Computer experiment is another challenging area. Space-filling design…
Aron Culotta, Analyzing Online Social Networks for Interdisciplinary Science
Our lab uses machine learning, natural language processing, and social network analysis to study text and graph data in online social networks like Twitter, Instagram, and Facebook. Applications we have investigated include: estimating the spread of disease, estimating the health of a community, predicting which products should be recalled, and…
Andrey Rogachev, Applied Quantum Chemistry
Quantum chemistry nowadays is one of the most rapidly growing and highly efficient tools of modern chemistry. It allows to provide an accurate description and what is more important – make a reliable prediction of electronic structure, chemical reactivity and many other properties. In this talk, I will outline the…