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DTSTART;TZID=America/Detroit:20170127T120000
DTEND;TZID=America/Detroit:20170127T130000
DTSTAMP:20260604T021400
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SUMMARY:MICDE Seminar: Vipin Kumar\, Computer Science and Engineering\, University of Minnesota
DESCRIPTION:Bio: Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering  at the University of Minnesota.  His research interests include data mining\, high-performance computing\, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data driven approaches.  He has authored over 300 research articles\, and co-edited or coauthored 10 books including the widely used text book “Introduction to Parallel Computing”\, and “Introduction to Data Mining”.  Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association).  Kumar is a Fellow of the ACM\, IEEE and AAAS.  He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013) and the Distinguished Alumnus Award from the Computer Science Department\, University of Maryland College Park (2009).  Kumar’s foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award\, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD)\, and the 2016 IEEE Computer Society Sidney Fernbach Award\, one of IEEE Computer Society’s highest awards. \nBig Data in Climate: Opportunities and Challenges for Machine Learning and Data Mining\nThis talk will present an overview of research being done in a large interdisciplinary project on the development of novel data mining and machine learning approaches for analyzing massive amount of climate and ecosystem data now available from satellite and ground-based sensors\, and physics-based climate model simulations. These information-rich data sets offer huge potential for monitoring\, understanding\, and predicting the behavior of the Earth’s ecosystem and for advancing the science of global change. This talk will discuss challenges in analyzing such data sets and some of our research results in mapping the dynamics of surface water globally as well as detecting deforestation and fires in tropical forests using data from Earth observing satellites. \nResearch funded by the NSF Expeditions in Computing Program and  NASA \nPizza lunch will be provided
URL:https://micde.umich.edu/event/micde-seminar-vipin-kumar-computer-science-and-engineering-university-of-minnesota/
LOCATION:1008 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20170111T170000
DTEND;TZID=UTC:20170111T180000
DTSTAMP:20260604T021400
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SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - North Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-north-campus/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Info Session
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BEGIN:VEVENT
DTSTART;TZID=UTC:20170109T170000
DTEND;TZID=UTC:20170109T180000
DTSTAMP:20260604T021400
CREATED:20230905T171440Z
LAST-MODIFIED:20230905T171440Z
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SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - Central Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-central-campus/
LOCATION:2001 LSA Building\, 500 State St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Info Session
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20161209T110000
DTEND;TZID=UTC:20161209T120000
DTSTAMP:20260604T021400
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SUMMARY:MICDE Seminar: Ann Almgren\, Lawrence Berkeley National Lab
DESCRIPTION:Bio:  Ann Almgren is a senior scientist in the Computational Research Division of Lawrence Berkeley National Laboratory and the Group Lead of the Center for Computational Sciences and Engineering. Her primary research interest is in computational algorithms for solving PDE’s for fluid dynamics in a variety of application areas. Her current projects include the development and implementation of new multiphysics algorithms in high-resolution adaptive mesh codes that are designed for the latest multicore architectures.  She is a SIAM Fellow and serves on the editorial boards of CAMCoS and SIREV. \nNext-Generation AMR\nBlock-structured adaptive mesh refinement (AMR) is a powerful tool for improving the computational efficiency and reducing the memory footprint of structured-grid numerical simulations. AMR techniques have been used for over 25 years to solve increasingly complex problems.  I will give an overview of recent and planned advances in AMR algorithms and implementations at BerkeleyLab to address the challenges of next-generation multicore architectures and the complexity of multiscale\, multiphysics problems.  This will include new ways of thinking about multilevel algorithms and new approaches to data layout and load balancing\, in situ and in transit visualization and analytics\, and run-time performance modeling and control. \n  \n  \n  \n 
URL:https://micde.umich.edu/event/micde-seminar-ann-almgren-lawrence-berkeley-national-lab/
LOCATION:1013 H. H. Dow\, 2300 Hayward St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20161208T170000
DTEND;TZID=America/Detroit:20161208T180000
DTSTAMP:20260604T021400
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SUMMARY:Computational Research at Lawrence Berkeley National Lab
DESCRIPTION:MICDE invites students to a presentation by Dr. Ann Almgren from the Computational Research Division (CRD) at Lawrence Berkeley National Lab (LBL). Dr. Almgren will give an overview of CRD and what type of research goes on in applied mathematics\, scientific computing / computational science\, computer science and data science & technology. This presentation will focus on students who might be interested in postdoc positions at LBL at some point in their future. \nFood and drinks will be provided!
URL:https://micde.umich.edu/event/an-overview-of-computational-research-at-lawrence-berkeley-national-lab/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20161202T140000
DTEND;TZID=America/Detroit:20161202T150000
DTSTAMP:20260604T021400
CREATED:20230905T171440Z
LAST-MODIFIED:20230905T171440Z
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SUMMARY:MICDE/RadLab/IEEE Seminar: Levent Gürel\, ABAKUS Computing Technologies
DESCRIPTION:Bio: Prof. Levent Gürel (Fellow of IEEE\, ACES\, and EMA) received the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991\, respectively\, in electrical and computer engineering. He worked at the IBM Thomas J. Watson Research Center\, Yorktown Heights\, New York\, in 1991-94. During his 20 years with Bilkent University\, he served as the Founding Director of the Computational Electromagnetics Research Center (BiLCEM) and a professor of electrical engineering. He is also an Adjunct Professor at UIUC. Prof. Gürel is the Founder and CEO of ABAKUS Computing Technologies\, a company that is geared towards advancing the use of cutting-edge computing technologies for solving difficult scientific problems with important real-life applications and societal benefits. He is conferred the UIUC ECE Distinguished Alumni Award in 2013 and the IEEE Harrington-Mittra Award in Computational Electromagnetics in 2015. He is an IEEE Distinguished Lecturer. He was invited to address the 2011 and 2017 ACES Conferences as a Plenary Speaker and a TEDx Conference in 2014. Among other recognitions of Prof. Gürel’s accomplishments\, the two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technological Research Council of Turkey (TUBITAK) in 2003 are the most notable. Since 2003\, Prof. Gürel has been serving as an associate editor for Radio Science\, IEEE Transactions on Antennas and Propagation\, IEEE Antennas and Wireless Propagation Letters\, IET Microwaves\, Antennas & Propagation\, JEMWA\, PIER\, ACES Journal\, and ACES Express. \nSolution of Extremely Large Forward and Inverse Problems in Computational Electromagnetics: BIG DATA Aspects\nAs we solve some of the largest problems in the interdisciplinary domain of computational electromagnetics\, we have to deal with various aspects of big-data issues routinely. Most recently\, we have achieved the solutions of larger than 1\,500\,000\,000×1\,500\,000\,000 (1.5 billion!) dense matrix equations! This achievement is an outcome of a multidisciplinary effort involving physical understanding of electromagnetics problems\, novel parallelization strategies (computer science)\, constructing parallel clusters (computer architecture)\, advanced mathematical methods for integral equations\, fast solvers\, iterative methods\, preconditioners\, linear algebra\, and big data. Solving such large problems on a regular basis requires the generation\, representation\, storage\, processing\, analysis\, transfer and communication\, visualization and interpretation of extremely large data sets in the order of multiple terabytes. \nAccurate formulations of real-life electromagnetics problems with integral equations necessitate the solution of extremely large dense matrix equations. Solutions of such tremendously challenging problems cannot be achieved easily\, even when using the most powerful computers with state-of-the-art petascale computing capabilities. Instead\, we have been solving some of the world’s largest integral-equation problems in computational electromagnetics by employing fast algorithms implemented on parallel computers. To achieve optimal management of multiple large data sets\, we design and implement the handling of data in various levels of cache\, memory\, and disk\, leading to meticulously designed out-of-core (OoC) schemes. That way\, we enable the solution of unprecedentedly large problems with limited amounts of DRAM. In order to avoid decelerating the solution\, we optimize communications among CPU cores\, among processors\, among nodes\, from CPU to disk (and back)\, and in the case of heterogeneous architectures\, we carefully control the data traffic to/from GPUs. Furthermore\, we employ MPI and OpenMP simultaneously in a parallelization strategy designed to reduce data duplications among processes so that vast numbers of cores can be efficiently utilized without requiring extra memory. \nI will present fast and accurate solutions of large-scale electromagnetic forward and inverse problems involving three-dimensional geometries that are larger than 1000 wavelengths using the multilevel fast multipole algorithm (MLFMA) and parallel MLFMA. Solving the world’s largest computational electromagnetics problems has important implications in terms of obtaining the solutions of future grand-challenge problems in imaging\, (subsurface)\, optics\, nanotechnology\, bio-electromagnetics\, metamaterials\, remote sensing\, as well as plethora of other disciplines of science\, e.g.\, acoustics\, elastics\, quantum mechanics\, astrophysics\, molecular dynamics\, electro-statics\, fluid dynamics\, thermodynamics. For more information: http://captains.of.computing.technology/.
URL:https://micde.umich.edu/event/micderadlabieee-seminar-levent-gurel-abakus-computing-technologies/
LOCATION:3427 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20161110T140000
DTEND;TZID=America/Detroit:20161110T150000
DTSTAMP:20260604T021400
CREATED:20230905T171440Z
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SUMMARY:MICDE 2016 Catalyst Grants Informational Session
DESCRIPTION:The Michigan Institute for Computational Discovery & Engineering (MICDE) seeks proposals for innovative research projects in computational science that combine elements of mathematics\, computer science\, and cyberinfrastructure. Of interest is computational science research in any emerging area\, including but not limited to (a) applications such as neuroscience\, ecology\, environmental science\, evolutionary biology\, human-made complex systems\, urban infrastructure and energy; and (b) frameworks for scientific software\, and exascale computing. Priority will be given to high-impact projects with potential to attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100\,000 each. \nIn this informational session\, MICDE officials will clarify the program’s intent\, answer questions and facilitate team formation among attendees. \nPlease pre-register using this google form. You’ll need to be signed into your umich account. The session will be broadcasted via this bluejeans link. For more information go to https://live-umor-micde.pantheonsite.io/grants/catalyst-grants/
URL:https://micde.umich.edu/event/micde-2016-catalyst-grants-informational-session/
LOCATION:Michigan League\, Room D\, 911 N. University \, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Info Session
GEO:42.279296;-83.7375576
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20161026T161000
DTEND;TZID=UTC:20161026T170000
DTSTAMP:20260604T021400
CREATED:20230905T171439Z
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SUMMARY:MICDE Seminar: Andrea Lodi\, Polytechnique Montréal
DESCRIPTION:Bio:  Andrea Lodi received a PhD in System Engineering from the University of Bologna in 2000 and he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department\, NY from 2005–2006. He was a full professor of Operations Research at DEI\, University of Bologna between 2007 and 2015. Since 2015 he has been the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at the Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received recognition including the IBM and Google faculty awards. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization. He serves as Associate Editor for several prestigious journals in the area. He has been the network coordinator and principal investigator of two large EU projects/networks\, and\, since 2006\, consultant of the IBM CPLEX research and development team. Finally\, Andrea Lodi is the co-principal investigator (with Yoshua Bengio) of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution”\, recently funded by the Canadian Federal Government under the Apogée Programme. \nOn Wide Split Cuts for Mixed-Integer Programming\nCutting planes (or simply cuts) are a fundamental component of modern Mixed-Integer Linear Programming (MILP) solvers because they help in strengthening the linear programming relaxation\, a proxy to make the branchand-bound tree small. A classical way of devising cuts is to exploit disjunctions\, for example in the domain of an integer variable\, where\, of course\, no fractional value leads to any feasible solution. Cutting planes of this type\, called split cuts\, classically exploit disjunctions whose ‘width’ is always equal to one\, i.e.\, no fractional value is feasible between two consecutive integer values. We investigate cutting planes that arise when widening the associated disjunctions. This allows\, e.g.\, to model non contiguous domains of (integer) variables (or\, stated differently\, ‘holes’ in the domains). The validity of the disjunctions in a MILP can come from either primal or dual information\, and we present examples and computational results in both cases. We further explore an exact MILP approach based on these cutting planes\, that in addition tackles non-contiguity directly via branching and as a side-effect reduces the model size. (Joint work with P. Bonami\, F. Serrano\, A. Tramontani\, S. Wiese.) \nThis seminar is co-sponsored by the U-M Department of Industrial & Operations Engineering
URL:https://micde.umich.edu/event/micde-seminar-andrea-lodi-ecole-polytechnique-montreal/
LOCATION:Boeing Auditorium –  1109 Francois-Xavier Bagnoud Building\, 1320 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20161014T151000
DTEND;TZID=UTC:20161014T160000
DTSTAMP:20260604T021400
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LAST-MODIFIED:20230905T171443Z
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SUMMARY:MICDE Seminar: Anthony Wachs\, University of British Columbia
DESCRIPTION:Bio: Anthony Wachs is an assistant professor with a joint appointment in the departments of Mathematics and of Chemical and Biological Engineering at the University of British Columbia\, Vancouver\, Canada. He received his B. Sc. and M. Sc. from the University Louis Pasteur of Strasbourg and his PhD from the Institut National Polytechnique of Grenoble in 2000. Right after\, he was hired in 2001 as a Fluid Mechanics research engineer at IFP Energies nouvelles (IFPEN\, at that time Institut Français du Pétrole) in Paris. \nIn 2009\, he spent a one-year sabbatical at the nuclear research center of Cadarache in the south of France\, where he worked for IRSN (the french national safety administration for nuclear energy). In 2010\, he got his HDR (French Habilitation to Supervise Research) and was later promoted Scientific Advisor at IFPEN in Multiphase Flows and Scientific Computing. He then moved to IFPEN-Lyon where he supervised a group of researchers (including PhD and post-doc students) on the numerical simulation of reactive particulate flows (www.peligriff.com). \nHis main research interests are non-Newtonian Flows\, Multiphase Flows and High Performance Computing. He collaborates extensively with academic groups in Canada\, Brazil\, France and Germany. \nMicro/meso numerical modeling of flows laden with particles of arbitrary shape\nParticulate flows are ubiquitous in environmental\, geophysical and engineering processes. The intricate dynamics of these two-phase flows is governed by momentum transfer between the continuous fluid phase and the dispersed particulate phase. When significant temperature differences exist between the fluid and particles and/or chemical reactions take place at the fluid/particle interfaces\, the phases also exchange heat and/or mass\, respectively. While some multi-phase processes may be successfully modelled at the continuum scale through closure approximations\, an increasing number of applications require resolution across scales\, e.g. dense suspensions\, fluidized beds. Within a multi-scale micro/meso/macro-framework\, we develop robust numerical models at the micro and meso scales\, based on a Distributed Lagrange Multiplier/Fictitious Domain method and a two-way Euler/Lagrange method\, respectively. Collisions between finite size particles are modeled with a Discrete Element Method. Many real-life processes and/or flows involve non-spherical particles. Although there is still a lot to learn about flows laden with spherical particles\, there is also a strong incentive to develop new modeling tools to account for non-spherical\, angular\, convex or even non-convex particles. We discuss assorted issues related to the numerical modelling of flows laden with particles of arbitrary shape. Along the way\, we also address high performance computing issues related to our massively parallel numerical tools and challenges to efficiently transfer knowledge from small scales to large scales. We illustrate the modelling capabilities of our tools on the two following problems relevant of applications from the chemical engineering and process industry: (i) a rotating drum filled with non-convex particles and (ii) fixed and fluidized beds of multilobic (and hence non-convex) particles.\n\n  \nThis seminar is co-organized with the Applied Interdisciplinary Mathematics program
URL:https://micde.umich.edu/event/micde-seminar-anthony-wachs-university-of-british-columbia/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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DTSTART;TZID=UTC:20161006T154500
DTEND;TZID=UTC:20161006T170000
DTSTAMP:20260604T021400
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SUMMARY:MICDE Seminar: Jonathan Freund\, University of Illinois at Urbana-Champaign
DESCRIPTION:Bio: Jonathan Freund is the Donald Biggar Willett Professor of Mechanical Science & Engineering and Aerospace at the University of Illinois at Urbana-Champaign.   He is a Fellow of the American Physical Society\, and a winner of the 2008 Frenkiel Prize from its Division of Fluid Dynamics where he currently serves as the division secretary/treasurer.  He is an associate editor of Physical Review Fluids and on the editorial board of Annual Review of Fluid Mechanics.  Computational science has been central to his research\, which has included simulations of turbulent jet noise and its control\, the dynamics of molecularly thin liquid films\, nanostructure formation by ion-bombardment of semiconductor materials\, and most recently the dynamics of red blood cells flowing in the narrow confines of the microcirculation.  He co-directs the DOE-funded Center for Exascale Simulation of Plasma-Coupled Combustion at the University of Illinois. \nAdjoint-based optimization for understanding and reducing flow noise\nAdvanced simulation tools\, particularly large-eddy simulation techniques\, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions.  Increasing computer resources will be a key factor in improving these predictions still further.  Quality prediction\, however\, is only a necessary condition for the use of such simulations in design optimization.  Predictions do not of themselves lead to quieter designs.  They must be interpreted or harnessed in some way that leads to design improvements.  As yet\, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity.  In this light\, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs.  This is done with only a single flow solutions and a solution of an adjoint system\, which is solved at computational cost comparable to that for the flow. Optimization requires iterations\, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.  The talk will review the formulation of the adjoint of the compressible flow equations for optimizing noise-reducing controls and present examples of its use.  We will particularly focus on some mechanisms of flow noise that have been revealed via this approach. \nThis seminar is co-sponsored by U-M Aerospace Engineering
URL:https://micde.umich.edu/event/micde-seminar-jonathan-freund-university-of-illinois-at-urbana-champaign/
LOCATION:Boeing Auditorium –  1109 Francois-Xavier Bagnoud Building\, 1320 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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DTSTART;TZID=UTC:20160929T160000
DTEND;TZID=UTC:20160929T170000
DTSTAMP:20260604T021400
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SUMMARY:MICDE Seminar: Jeremy Lichstein\, University of Florida
DESCRIPTION:Bio: Jeremy Lichstein is an assistant professor of Biology at the University of Florida. Professor Lichstein got his Ph. D. from Princeton University and was a postdoctoral research fellow at Princeton’s department of Ecology and Evolutionary Biology. He was the recipient of the University of Florida Excellence Award for Assistant Professor\, and was named a Florida Climate Institute Fellow for 2016-2017. His research interests are forest dynamics\, biodiversity\, carbon cycle and climate change. \nBiodiversity and the changing Earth System: computational challenges and new answers to old questions\nTerrestrial ecosystems currently offset roughly 25% of global annual anthropogenic fossil fuel emissions. However\, the fate of this carbon sink is highly uncertain\, in large part because global models diverge in their predictions of ecosystem responses to climate change\, drought\, and other perturbations. Although there is little agreement on how terrestrial ecosystems will respond to global change on decadal and longer time-scales\, there is wide consensus that current global models are overly simplistic in their representation of important ecological processes. I will discuss our current understanding of how tree functional diversity is maintained in forests\, the consequences of including more realistic levels of functional diversity in global models\, and the computational challenges that need to be overcome in order to introduce ecological realism into the Earth System Models that the scientific and policy communities rely on for climate projections. A key result that is emerging from empirical and theoretical studies is that shifts in species composition across time or space (beta diversity) have different (and sometimes opposite) effects on ecosystem stability as local (alpha) diversity. \nThis seminar is co-sponsored by the U-M department of Ecology and Evolutionary Biology
URL:https://micde.umich.edu/event/micde-2016-fall-seminar-series-jeremy-lichstein-university-of-florida/
LOCATION:1210 Chemistry & Willard H Dow Laboratory\, 930 University Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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DTSTART;TZID=UTC:20160922T160000
DTEND;TZID=UTC:20160922T170000
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SUMMARY:MICDE Seminar: Rob Gardner\, University of Chicago
DESCRIPTION:Bio: Robert Gardner is a Senior Scientist at the Computation Institute from the University of Chicago\,  and aSenior Scientist in the Enrico Fermi Institute. He spent his early academic career doing experimental high-energy physics research at different universities in the Midwest. He has been a member of the ATLAS experiment using the Large Hadron Collider at the CERN Laboratory\, Geneva\, Switzerland since 1998. His experimental work led him to specialize in developing and improving distributed computing technologies necessary for discoveries at the frontier of particle physics. He was instrumental in developing early research computing grids in the U.S.: the International Virtual Data Grid Laboratory (iVDGL)\, and the first deployment of the Open Science Grid (OSG) (NSF\, Department of Energy). He have also generated systems for metrics collection for distributed systems (Grid Telemetry\, PI\, NSF-ITR). Currently\, he directs the ATLAS Midwest Tier2 Center\, which is comprised of integrated computing facilities from the University of Chicago\, Indiana University\, and the University of Illinois. \nLeadership cyberinfrastructure for science and the humanities\nIn the past two decades high energy physics transformed its computing model from one relying on a single high performance computing center at the host laboratory to one incorporating resources distributed across institutional boundaries and geographic regions. Given the complexity of detectors and scale of data\, the international collaborations of the Large Hadron Collider at CERN demanded it. By removing barriers to resource sharing\, the resulting data and computation platform democratized the physics process across collaborations. Accelerated modes of scientific discovery by thousands of physicists were forged using hundreds of data centers linked by very high bandwidth networks. Meanwhile the explosion of commercial\, social and enterprise data has driven innovation in resource abstraction and the creation of new service platforms\, offering fresh opportunities to accelerate science and intellectual inquiry at all scales and across all domains. In this talk I’ll discuss the strategic significance that cyberinfrastructure technology plays in this regard and describe models for creating ubiquitous “substrates” that remove obstacles to connecting campuses\, facilities\, instruments and researchers. \nThis seminar is co-sponsored by the U-M department of Physics
URL:https://micde.umich.edu/event/micde-2016-fall-seminar-series-rob-gardner-university-of-chicago/
LOCATION:340 West Hall\, 1085 South University Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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DTSTART;TZID=UTC:20160921T170000
DTEND;TZID=UTC:20160921T180000
DTSTAMP:20260604T021400
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SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - Central Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-central-campus-2/
LOCATION:2001 LSA Building\, 500 State St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Info Session
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DTSTART;TZID=UTC:20160919T170000
DTEND;TZID=UTC:20160919T180000
DTSTAMP:20260604T021400
CREATED:20230905T171442Z
LAST-MODIFIED:20230905T171442Z
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SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - North Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-north-campus-2/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Info Session
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