ICES Event Recordings Catalog

ICES Event Recordings Catalog

Seminars are held Tuesdays and Thursdays in POB 6.304 from 3:30-5:00 pm, unless otherwise noted. Speakers include scientists, researchers, visiting scholars, potential faculty, and ICES/UT Faculty or staff. Everyone is welcome to attend. Refreshments are served at 3:15 pm.



Wednesday, Sep 27

Uncertainty Quantification in Turbulence Modeling

Wednesday, Sep 27, 2017 from 1:30PM to 2:30PM | POB 6.304

  • Additional Information

    Hosted by J. Tinsley Oden

    Sponsor: ICES Seminar

    Speaker: Robert D. Moser

    Speaker Affiliation: Professor, Department of Mechanical Engineering, Institute for Computational Engineering and Sciences, University of Texas at Austin

  • Abstract

    Many fluid flows of technological interest are turbulent, and in such flows the turbulence has an order-one effect on flow characteristics. Computational models of such flows are commonly used to make predictions in support of design and operations decisions, so the effects of turbulence must be modeled. The most common turbulence modeling approach in applications is Reynolds Averaged Navier-Stokes (RANS) modeling, but it is well known that RANS models are in error in many flow situations. How then can we make reliable predictions of turbulent flows with unreliable RANS models? As it happens, this is possible, provided the uncertainties due to the RANS model errors are accounted for.

    In this talk, we discuss the challenge of computational predictions and uncertainties in RANS models. As an examples, we discuss representations of uncertainty due to RANS model error in turbulent channel flow.

    Bio
    Robert D. Moser holds the W. A. "Tex" Moncrief Jr. Chair in Computational Engineering and Sciences and is Professor of Mechanical Engineering in thermal fluid systems. He serves as the Director of the Center for Predictive Engineering and Computational Sciences (PECOS) and Deputy director of the Institute for Computational Engineering and Sciences (ICES). Moser received his PhD in mechanical engineering from Stanford University. Before coming to the University of Texas, he was a research scientist at the NASA-Ames Research Center and then a Professor of Theoretical and Applied Mechanics at the University of Illinois. Moser conducts research on the modeling and numerical simulation of turbulence and other complex fluid flow phenomena. He also uses direct numerical simulation to investigate and model turbulent flows, particularly the development and evaluation of large eddy simulation models. Moser has also been working to develop new approaches for the validation of and quantification of uncertainty in computational models and to assess their reliability. He has pursued applications to such diverse systems as reentry vehicles, solid propellant rockets, micro-air vehicles, turbulent combustion, tokamak fusion and energy harvesting. He is a Fellow of the American Physical Society, and was awarded the NASA Medal for Exceptional Scientific Achievement.

  • Multimedia

Friday, Oct 7

Discovering Causality in Data using Entropy

Friday, Oct 7, 2016 from 10AM to 11AM | POB 6.304

  • Additional Information

    Hosted by Amir Gholaminejad

    Sponsor: ICES Seminar-Babuska Forum Series

    Speaker: Alex Dimakis

    Speaker Affiliation: Associate Professor, Electrical and Computer Engineering, UT Austin

  • Abstract

    Causality has been studied under several frameworks in statistics and artificial intelligence. We will briefly survey Pearl’s Structural Equation model and explain how interventions can be used to discover causality. We will also present a novel information theoretic framework for discovering causal directions from observational data when interventions are not possible. The starting point is conditional independence in joint probability distributions and no prior knowledge on causal inference is required for this lecture.

    Bio
    Dr. Alex Dimakis is an Associate Professor in the Electrical & Computer Engineering department at The University of Texas at Austin. Prof. Dimakis received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012.

  • Multimedia

     Event Stream Link: Click Here to Watch


Thursday, Apr 14

Stem Cells: Interplay Between Complex Data and Computational Models

Thursday, Apr 14, 2016 from 3:30PM to 5PM | POB 6.304

  • Additional Information

    Hosted by George Biros

    Sponsor: ICES Seminar - Computational Medicine Spring Series

    Speaker: Qing Nie

    Speaker Affiliation: Professor, Departments of Mathematics & Biomedical Engineering, University of California, Irvine

  • Abstract

    Stem cells are a critical building block of life. Embryonic stem cells can differentiate into cells forming ectoderm, endoderm and mesoderm during development, and adult stem cells can maintain the normal turnover of regenerative tissues (e.g. blood, skin, intestinal crypts). Recently, there has been an explosion of data on stem cells at various biological scales (e.g. gene expression and epigenetic measurements, lineage tracing, and molecules for intercellular communications). While data collected through different cell lines and animal models provide tremendous details on individual elements under various conditions, many gaps of knowledge and understanding remain on how stem cells carry out their remarkable functions and complex tasks. Mathematical models connecting interacting elements at different scales enable integration of massive, heterogeneous datasets collected with varying methods. In this talk, I will present several computational modeling frameworks with different complexity on multistage cell lineages driven by stem cells, which account for diffusive signaling molecules, regulatory networks, individual cells, mechanics, and evolution. Questions of our interest include role of feedbacks, stem cell niche for spatial organization, crosstalk between epigenetic and gene regulations, and cellular plasticity. In particular, I will discuss our recent effort on connecting modeling and complex experimental data to elucidate principles for stem cell dynamics in development, regeneration, and diseases.

    Bio:
    Dr. Nie is a professor of Mathematics and Biomedical Engineering at University of California, Irvine (UCI). In research, he uses systems biology and data-driven approaches to study complex biological systems with focuses on embryonic development, stem cells, gene regulatory networks, and their applications to diseases. Dr. Nie has published more than 100 research articles. He has served in many NIH and NSF review panels, and has maintained a well-funded research program, currently with multiple NIH R01, project, and training grants, and NSF grants. In training, Dr. Nie has supervised near 30 postdoctoral fellows and PhD students, with many of them working in academic institutions. Dr. Nie was a UCI Chancellor Fellow. He currently serves as the director of the UCI campus-wide interdisciplinary gateway PhD program on Mathematical and Computational Biology, and the director of the Center for Mathematical and Computational Biology, as well as the associate director for UCI Center of Complex Biological Systems – a national center for systems biology funded by NIH. Dr. Nie is a fellow of the America Association for the Advancement of Science and a fellow of American Physical Society.

  • Multimedia

     Event Stream Link: Click Here to Watch


Friday, Apr 1

  • Additional Information

    Hosted by Michael Sacks

    Sponsor: ICES Seminar - Computational Medicine Spring Series

    Speaker: C. Alberto Figueroa

    Speaker Affiliation: University of Michigan

  • Abstract

    In this talk, we will provide an overview of current applications, challenges and opportunities in subject-specific blood flow modeling, a field to which computational mechanics has so much to contribute. Our laboratory is particularly interested in developing novel methods for:

    (a) Situations of dynamic changes in flow and pressure such as those induced by exercise, hemorrhage, changes in posture and anesthesia. Simulating these conditions requires implementing a ‘control systems’ approach whereby reduced order models of the circulation are dynamically adapted following certain auto-regulatory responses of the cardiovascular system.

    (b) Simulation of various types of fluid-structure interactions in both the arterial and venous systems.

    (c) Developing methods for automatic parameter estimation based on filtering techniques.

    We will discuss novel applications in the areas of surgical planning and cardiovascular disease research. Lastly, we will briefly provide an overview of CRIMSON, the simulation framework currently developed in our laboratory.

    Bio:
    Dr. Figueroa is an Associate Professor in Biomedical Engineering and Vascular Surgery at the University of Michigan. He also has an appointment as Honorary Senior Lecturer in the Department of Biomedical Engineering at King's College London. Dr. Figueroa has a well-established record of research in the field of multi-physics and multi-scale computer modeling of hemodynamics. He holds a PhD in Mechanical Engineering from Stanford University, where he developed novel algorithms to perform fluid-structure interaction simulations of anatomically accurate cardiovascular models constructed from image data. His laboratory focuses on developing tools for non-invasive parameter estimation of material properties from medical image data, and on modeling cardiovascular auto-regulatory mechanisms such as the baro-reflex and local auto-regulations. The algorithms developed in his laboratory have made it possible to simulate blood flow and arterial dynamics in full-body scale arterial models, a feat that had not been accomplished before. His research also has an important translational component such as in the area of medical device design and performance evaluation, specifically for abdominal and thoracic aortic endografts. His modeling tools have been also applied to the study of systemic hypertension, using mice data on anatomy, hemodynamics, and biaxial regionally-varying vessel tissue properties to perform full-body scale fluid-structure interaction simulations of mice hemodynamics.

  • Multimedia

Thursday, Mar 24

The Emerging Discipline of Computational Medicine

Thursday, Mar 24, 2016 from 3:30PM to 5PM | POB 6.304

  • Additional Information

    Hosted by Michael Sacks

    Sponsor: ICES Seminar - Computational Medicine Spring Series

    Speaker: Raimond Winslow

    Speaker Affiliation: Johns Hopkins University School of Medicine

  • Abstract

    Because of the inherent complexity of biological systems, the development of computational models is necessary to achieve a quantitative understanding of their structure and function in health and disease. Computational Medicine is a discipline in which mechanistic models of disease are developed, personalized using data from individual patients, and then applied to deliver improved health care. In computational molecular medicine, statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks in health and disease. In computational physiological medicine, multiscale modeling links networks to cells, organs, and organ systems. In computational anatomy, mathematical approaches are used to analyze medical imagery to characterize anatomic shape and its variations in health and disease. In computational healthcare, statistical models of electronic health record data are developed. In each case, models are personalized using patient data, and then applied to improve disease diagnosis and to guide therapy. This talk will present success stories in each of these areas of computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Challenges that must be confronted to translate these computational methods to the clinic will be discussed.

    Bio:
    Dr. Winslow is the Raj and Neera Singh Professor of Biomedical Engineering, and Director of the Institute for Computational Medicine at Johns Hopkins University. His research is focused on two areas. The first is use of computational modeling to understand the molecular mechanisms of cardiac arrhythmias and sudden death. The second is development of informatics technologies supporting cardiovascular clinical research and that provide researchers secure, seamless access to study data and analysis tools. He is Principal Investigator of the CardioVascular Research Grid Project, an NHLBI-funded resource, with research teams at four universities, with the goal of creating a national infrastructure for sharing and analysis of cardiovascular data. He holds joints appointments in the departments of Electrical and Computer Engineering, Computer Science, and the Division of Health Care Information Sciences.

  • Multimedia

Tuesday, Mar 8

Fast Direct Solvers for Elliptic PDEs

Tuesday, Mar 8, 2016 from 3:30PM to 5PM | POB 6.304

  • Additional Information

    Hosted by George Biros and Leszek Demkowicz

    Sponsor: ICES Seminar - Computational Mathematics Series

    Speaker: Per-Gunnar Martinsson

    Speaker Affiliation: University of Colorado- Boulder

  • Abstract

    That the linear systems arising upon the discretization of elliptic PDEs can be solved very efficiently is well-known, and many successful iterative solvers with linear complexity have been constructed (multigrid, Krylov methods, etc). Interestingly, it has recently been demonstrated that it is often possible to directly compute an approximate inverse to the coefficient matrix in linear (or close to linear) time. The talk will survey some recent work in the field and will argue that direct solvers have several advantages, including improved stability and robustness, and dramatic improvements in speed in certain environments. Moreover, the direct solvers being proposed have low communication costs, and are very well suited to parallel implementations.

    The talk will also briefly describe randomized techniques for factorizing matrices. These are used to accelerate the direct solvers for elliptic PDEs, but have also proven highly competitive in machine learning, data analysis, etc.

    Per-Gunnar Martinsson's Bio

  • Multimedia

     Event Stream Link: Click Here to Watch


Monday, Mar 7

Bridging scales through nonlocal modeling

Monday, Mar 7, 2016 from 4PM to 5PM | POB 6.304

  • Additional Information

    Hosted by Kui Ren

    Sponsor: ICES Seminar - Computational Mathematics Series

    Speaker: Quang Du

    Speaker Affiliation: Professor

  • Abstract

    Nonlocality is ubiquitous in nature. Although partial differential equations (PDEs) remain favored as effective continuum models for many applications, nonlocal equations and nonlocal balanced laws are also becoming acceptable alternatives to model various processes exhibiting anomalies and singularities. They may also serve as effective bridges for multiscale modeling. In this talk, after a brief introduction to the framework of nonlocal vector calculus, we elucidate how it helps us to resolve a few computational issues of particular concerns to nonlocal modeling, including the development of asymptotically compatible schemes for validation and verification, the effective nonlocal gradient recover, and the seamless coupling of local and nonlocal models for efficient and adaptive computation.

  • Multimedia

Thursday, Mar 3

  • Additional Information

    Hosted by Michael Sacks

    Sponsor: ICES Seminar - Computational Medicine Spring Series

    Speaker: Ronald M. Peschock, M.D.

    Speaker Affiliation: Vice Chairman of Informatics for Radiology, Professor of Radiology and Internal Medicine, University of Texas Southwestern Medical Center

  • Abstract

    Precision medicine tailors medical treatment to the individual characteristics of each patient. Computational medicine has the potential to play a crucial role in the precise characterization of individuals and identifying the best individualized therapy and outcome. However, bringing computational medicine approaches to the bedside will pose important challenges including their integration into existing clinical practice and application of this precise characterization in clinical decision making. Growth of the use of computational medicine in this new era of precision medicine will require close collaboration between centers of excellence in compuational medicine and clinicians who can interpret this knowledge to directly impact clinical care.

    Bio
    Ronald M. Peshock, M.D., is Vice Chairman of Informatics for Radiology and Professor of Radiology and Internal Medicine at the University of Texas Southwestern Medical Center. His clinical interests include cardiovascular disease, cardiovascular imaging, and imaging informatics. Dr. Peshock’s research has focused on the development and use of magnetic resonance imaging in investigation of cardiovascular disease. These studies began with the creation and validation of basic MRI techniques to assess cardiac structure and function which have subsequently been applied to a wide range of physiological and clinical questions. These include the evaluation of ischemic heart disease, cardiomyopathy, cerebral blood flow, alterations in skeletal muscle blood flow in the setting of exercise, and investigating links between changes in vascular stiffness and structural changes in the brain. These approaches have also been applied in the Dallas Heart Study, a population based study of cardiovascular health in Dallas County. In the area of clinical informatics, he has worked to implement electronic medical records, PACS and other systems to translate technology into clinical processes.

  • Multimedia

     Event Stream Link: Click Here to Watch


Thursday, Feb 18

Multigrid at Scale?

Thursday, Feb 18, 2016 from 3:30PM to 5PM | POB 6.304

  • Additional Information

    Hosted by Leszek Demkowicz

    Sponsor: ICES Seminar - Computational Mathematics Series

    Speaker: Mark Ainsworth

    Speaker Affiliation: Professor, Division of Applied Mathematics,Brown University

  • Abstract

    Multigrid and multilevel iterative algorithms are often the method of choice for the solution of large-scale systems of linear equations arising from discretisation of partial differential equations using finite element or finite difference methods. The method involves a number of components including smoothing relaxation, coarse grid solve, prolongation and restriction operators between grids in the multilevel hierarchy, and the convergence behavior of the method has been extensively analysed in the context of standard computer architectures. However, comparatively little is known about the resilience or fault-tolerance of the algorithm on next generation hardware architectures which are expected to suffer from frequent data corruption and hardware failures. We will address this issue and present some of the results of our recent work showing that the issue is anything but clear.

    This is joint work with Christian Glusa (Brown University).

    Bio
    Mark Ainsworth obtained his PhD in Mathematics at Durham University in the United Kingdom. Prior to moving to Brown, he held the 1825 Chair in Mathematics at Strathclyde University and was Director of
    NAIS , a joint centre between the Universities of Edinburgh, Heriot-Watt and Strathclyde, and Edinburgh Parallel Computing Centre to develop UK capacity in high performance computing and numerical analysis.
    Learn More About Mark Ainsworth

  • Multimedia

     Event Stream Link: Click Here to Watch


Thursday, Feb 11

A generalized multiscale model reduction technique for heterogeneous problems

Thursday, Feb 11, 2016 from 3:30PM to 5PM | POB 6.304

  • Additional Information

    Hosted by Todd Arbogast

    Sponsor: ICES Seminar - Computational Mathematics Series

    Speaker: Yalchin Efendiev

    Speaker Affiliation: Professor of Mathematics, Texas A&M University

  • Abstract

    In this talk, I will discuss multiscale model reduction techniques for problems in heterogeneous media. I will describe a framework for constructing local (space-time) reduced order models for problems with multiple scales and high contrast. I will focus on a recently proposed method, Generalized Multiscale Finite Element Method, that systematically constructs local multiscale finite element basis functions on a coarse grid, which is much larger than the underlying resolved fine grid. The multiscale basis functions take into account the fine-scale information of the resolved solution space via careful choices of local snapshot spaces and local spectral decompositions. I will discuss the issues related to the construction of multiscale basis functions, main ingredients of the method, and a number of applications. These methods are intended for multiscale problems without scale separation and high contrast.

  • Multimedia