Events - Revision history

Nikos: /* CNF Imaging Workshop */

2023-02-10T18:54:11Z

‎CNF Imaging Workshop

Nikos at 11:58, 19 August 2022

2022-08-19T11:58:13Z

Spyridon97: /* Seminars */

2020-09-01T02:54:53Z

‎Seminars

Angelos at 19:52, 25 August 2020

2020-08-25T19:52:43Z

Show changes

Jbest: /* Professor Dimitrios S. Nikolopoulos */

2019-10-08T22:59:02Z

‎Professor Dimitrios S. Nikolopoulos

Jbest: /* Professor Dimitrios S. Nikolopoulos */

2019-10-08T22:56:48Z

‎Professor Dimitrios S. Nikolopoulos

Jbest: /* Professor Dimitrios S. Nikolopoulos */

2019-10-08T22:32:16Z

‎Professor Dimitrios S. Nikolopoulos

Jbest: /* Professor Dimitrios S. Nikolopoulos */

2019-10-08T22:31:54Z

‎Professor Dimitrios S. Nikolopoulos

Jbest: /* CS Seminars */

2019-10-08T22:30:29Z

‎CS Seminars

Nikos: /* Dr. Daniele Panozzo */

2019-09-24T20:06:29Z

‎Dr. Daniele Panozzo

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 :'''Bio''': Dr. Xiangmin (Jim) Jiao is an Associated Professor in Applied Mathematics and Computer Science, and also a core faculty member of the Institute for Advanced Computational Science at Stony Brook University. He received his Ph.D. in Computer Science in 2001 from University of Illinois at Urbana-Champaign (UIUC). He was a Research Scientist at the Center for Simulation of Advanced Rockets (CSAR) at UIUC between 2001 and 2005, and then an Assistant Professor in College of Computing at Georgia Institute of Technology between 2005 and 2007. His research interests focus on high-performance geometric and numerical computing, including applied computational and differential geometry, generalized finite difference and finite element methods, multigrid and iterative methods for sparse linear systems, multiphysics coupling, and problem solving environments, with applications in computational fluid dynamics, structural mechanics, biomedical engineering, climate modeling, etc.   </li>
-== CNF Imaging Workshop ==
+== Workshops ==
-:'''More Information''': [[CNF_Imaging_Workshop | CNF Imaging Workshop ]]
 = Outreach =

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-= Seminars =
+= Seminars (before the pandemic) =
 == Serious Games and Simulations: applications, challenges and future directions ==
 '''Date:''' TBD, 2020

@@ Line 1: / Line 1: @@
 = Seminars =
 == Serious Games and Simulations: applications, challenges and future directions ==
 '''Date:''' TBD, 2020
@@ Line 37: / Line 26: @@
 '''Short Bio:''' Dr. Daniele Panozzo is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences in New York University. Prior to joining NYU he was a postdoctoral researcher at ETH Zurich (2012-2015). Daniele earned his PhD in Computer Science from the University of Genova (2012) and his doctoral thesis received the EUROGRAPHICS Award for Best PhD Thesis (2013). He received the EUROGRAPHICS Young Researcher Award in 2015 and the NSF CAREER Award in 2017. Daniele is leading the development of libigl (https://github.com/libigl/libigl), an award-winning (EUROGRAPHICS Symposium of Geometry Processing Software Award, 2015) open-source geometry processing library that supports academic and industrial research and practice. Daniele is chairing the Graphics Replicability Stamp (http://www.replicabilitystamp.org), which is an initiative to promote reproducibility of research results and to allow scientists and practitioners to immediately beneﬁt from state-of-the-art research results. His research interests are in digital fabrication, geometry processing, architectural geometry, and discrete differential geometry.
 </li>
 == New Approaches to Energy-Efficient and Resilient HPC   ==

@@ Line 9: / Line 9: @@
 <li style="display: inline-block;">
 [[File: dimitrios.jpg|frameless|left|200px]]
-'''Bio''': Dimitrios Nikolopoulos is a Professor of Engineering and he was recently named the John W. Hancock Professor of Engineering by the Virginia Tech Board of Visitors. He received his bachelor’s degree, master’s degree, and Ph.D. from the University of Patras. He spent the past 10 years in Europe, most recently as a professor of high performance and distributed computing and director of the Institute on Electronics, Communications, and Information Technology at Queen's University Belfast. He brings to Virginia Tech a world-class record of scholarship, teaching, service, and outreach. Through fundamental scholarship on computer systems, Nikolopoulos has made contributions to the global computing systems research community. He has published 55 peer-reviewed journal articles and 122 peer-reviewed papers in highly regarded archival conference proceedings. Nikolopoulos has advised or co-advised 22 Ph.D. students through completion and continues to advise six Ph.D. students. He has also advised 16 postdoctoral research fellows. He is a Distinguished Member of the Association for Computing Machinery (ACM) and is a recipient of a Royal Society Wolfson Research Merit Award, a National Science Foundation CAREER Award, a Department of Energy CAREER Award, and an IBM Faculty Award. See '''[[ CNF_HPC_Workshop | CNF HPC Workshop]]''' for more information about the workshop.</li>
+'''Bio''': Dimitrios Nikolopoulos is a Professor of Engineering and he was recently named the John W. Hancock Professor of Engineering by the Virginia Tech Board of Visitors. He received his bachelor’s degree, master’s degree, and Ph.D. from the University of Patras. He spent the past 10 years in Europe, most recently as a professor of high performance and distributed computing and director of the Institute on Electronics, Communications, and Information Technology at Queen's University Belfast. He brings to Virginia Tech a world-class record of scholarship, teaching, service, and outreach. Through fundamental scholarship on computer systems, Nikolopoulos has made contributions to the global computing systems research community. He has published 55 peer-reviewed journal articles and 122 peer-reviewed papers in highly regarded archival conference proceedings. Nikolopoulos has advised or co-advised 22 Ph.D. students through completion and continues to advise six Ph.D. students. He has also advised 16 postdoctoral research fellows. He is a Distinguished Member of the Association for Computing Machinery (ACM) and is a recipient of a Royal Society Wolfson Research Merit Award, a National Science Foundation CAREER Award, a Department of Energy CAREER Award, and an IBM Faculty Award.</li>
 == Prof. Anastasia Angelopoulou ==

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 <li style="display: inline-block;">
 [[File: dimitrios.jpg|frameless|left|200px]]
-'''Bio''': Dimitrios Nikolopoulos is a Professor of Engineering and he was recently named the John W. Hancock Professor of Engineering by the Virginia Tech Board of Visitors. He received his bachelor’s degree, master’s degree, and Ph.D. from the University of Patras. He spent the past 10 years in Europe, most recently as a professor of high performance and distributed computing and director of the Institute on Electronics, Communications, and Information Technology at Queen's University Belfast. He brings to Virginia Tech a world-class record of scholarship, teaching, service, and outreach. Through fundamental scholarship on computer systems, Nikolopoulos has made contributions to the global computing systems research community. He has published 55 peer-reviewed journal articles and 122 peer-reviewed papers in highly regarded archival conference proceedings. Nikolopoulos has advised or co-advised 22 Ph.D. students through completion and continues to advise six Ph.D. students. He has also advised 16 postdoctoral research fellows. He is a Distinguished Member of the Association for Computing Machinery (ACM) and is a recipient of a Royal Society Wolfson Research Merit Award, a National Science Foundation CAREER Award, a Department of Energy CAREER Award, and an IBM Faculty Award. </li>
+'''Bio''': Dimitrios Nikolopoulos is a Professor of Engineering and he was recently named the John W. Hancock Professor of Engineering by the Virginia Tech Board of Visitors. He received his bachelor’s degree, master’s degree, and Ph.D. from the University of Patras. He spent the past 10 years in Europe, most recently as a professor of high performance and distributed computing and director of the Institute on Electronics, Communications, and Information Technology at Queen's University Belfast. He brings to Virginia Tech a world-class record of scholarship, teaching, service, and outreach. Through fundamental scholarship on computer systems, Nikolopoulos has made contributions to the global computing systems research community. He has published 55 peer-reviewed journal articles and 122 peer-reviewed papers in highly regarded archival conference proceedings. Nikolopoulos has advised or co-advised 22 Ph.D. students through completion and continues to advise six Ph.D. students. He has also advised 16 postdoctoral research fellows. He is a Distinguished Member of the Association for Computing Machinery (ACM) and is a recipient of a Royal Society Wolfson Research Merit Award, a National Science Foundation CAREER Award, a Department of Energy CAREER Award, and an IBM Faculty Award. See '''[[ CNF_HPC_Workshop | CNF HPC Workshop]]''' for more information about the workshop.</li>
 == Prof. Anastasia Angelopoulou ==

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 '''Title:''' Optimistic Cloud & Edge Computing outside Hardware Boundaries
-'''ABSTRACT'''
+'''Abstract:'''
-To address scaling limitations of future hardware, computing systems turned to parallelism and distribution. Most of the software and applications in science and engineering, but also applications that we use in our daily lives are actually distributed programs with some components running on edge or IoT devices to serve clients, data collectors or actuators, and other components running on datacenters to provide data analytics, simulation, or visualization. The disaggregation of computing services raises new challenges for system challenges. We explores two of these challenges in this talk and discuss some solutions. The first challenge is that many applications necessitate low latency and more analytical power at or near the data sources. We demonstrate a system called TAPAS, which is neural network architecture search exploration engine. TAPAS uses aggressive compression, approximation and learning techniques to avoid entirely the simulation process in exploring neural network architectures. It further uses learning methods to adapt immediately to unseen data sets. TAPAS  runs on a single low-power GPU and can train over 1,000 networks per second. This makes TAPAS suitable for training machine learning models on edge devices with limited resources. The second challenge is the one of scaling the performance and energy-efficiency of the hardware used in the Cloud and the Edge beyond current boundaries. We explore a co-designed compiler/OS/firmware system for characterizing hardware operating boundaries and safely operating hardware outside those boundaries to gain performance at the expense of additional, yet infrequent errors and mitigating actions. We demonstrate that many applications are inherently resilient to extended hardware boundaries and indeed benefit substantially from boundary relaxation.
+To address scaling limitations of future hardware, computing systems turned to parallelism and distribution. Most of the software and applications in science and engineering, but also applications that we use in our daily lives are actually distributed programs with some components running on edge or IoT devices to serve clients, data collectors or actuators, and other components running on data centers to provide data analytics, simulation, or visualization. The disaggregation of computing services raises new challenges for system challenges. We explores two of these challenges in this talk and discuss some solutions. The first challenge is that many applications necessitate low latency and more analytical power at or near the data sources. We demonstrate a system called TAPAS, which is neural network architecture search exploration engine. TAPAS uses aggressive compression, approximation and learning techniques to avoid entirely the simulation process in exploring neural network architectures. It further uses learning methods to adapt immediately to unseen data sets. TAPAS  runs on a single low-power GPU and can train over 1,000 networks per second. This makes TAPAS suitable for training machine learning models on edge devices with limited resources. The second challenge is the one of scaling the performance and energy-efficiency of the hardware used in the Cloud and the Edge beyond current boundaries. We explore a co-designed compiler/OS/firmware system for characterizing hardware operating boundaries and safely operating hardware outside those boundaries to gain performance at the expense of additional, yet infrequent errors and mitigating actions. We demonstrate that many applications are inherently resilient to extended hardware boundaries and indeed benefit substantially from boundary relaxation.
 <li style="display: inline-block;">
 [[File: dimitrios.jpg|frameless|left|100px]]

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 == Dr. Daniele Panozzo ==
-'''Date:''' Nov. 1,2019
+'''Date:''' TBD, 2020
 '''Title:''' Black-Box Analysis