Recent & Upcoming Talks

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Welcome: Senior Provost for Research Atam P. Dhawan Host: Distinguished Professor Guiling “Grace” Wang, Associate Dean for Research Learn insights and strategies from faculty and industry partners who have successfully secured funding from leading agencies.

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

About The HPC Connection Workshop is an international High Performance Computing event organized by the Asia Supercomputer Community. This event takes place three times a year: during ASC in China, ISC in Germany, and SC in the USA.

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of Arkouda, an open-source NumPy-like replacement for interactive data science on tens of terabytes of data.

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

In the last 5-10 years, Deep Neural Networks (DNNs) not only emerged as a new target class of applications for HPC researchers, but …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power grid.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Bader will present a keynote talk on massive-scale analytics for real-world applications.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Advances in computers, mobile devices, nanotechnologies and cyberinfrastructure are at the vanguard of profound transformations across …

With the advancements of high performance computing (HPC) and edge computing (EC) use cases are now arising for the need of combining …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Moderator: David A. Bader Panelists: Joe Corsi, Deputy CISO, Excellus BCBS Steve Hendrie, VP and CISO, The Hershey Company Richard Rushing, CISO, Motorola Mobilty Russ Wolfe, Business Information Security Officer, Navy Federal

Quantum computing is the processing of information that’s represented by special quantum states. By tapping into quantum phenomena like …

Data analytics are an integral part of data science. The introduction of new scalable and high-performance data analytics frameworks, including the use of accelerators such as GPUs, has enabled data scientists to inspect their data and extract insights previously unattainable.

Abstract Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of the electric power grid.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

David A. Bader is a Distinguished Professor in the Department of Computer Science and founder of the Department of Data Science and inaugural Director of the Institute for Data Science at New Jersey Institute of Technology.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Supercomputers enable us to tackle some of the most challenging computational and data-intensive real-world problems facing our societies and the planet. In this talk, David A. Bader, the 2021 IEEE Computer Society Sidney Fernbach Award recipient, will share his passion for solving these global grand challenges.

Supercomputing has become an essential tool for computational science and engineering and such real-world problems as weather …

Meet the speaker for the event, 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗿𝗮𝗻𝗱 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘄𝗶𝘁𝗵 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, A distinguished Professor and Director, 𝗗𝗿. 𝗗𝗮𝘃𝗶𝗱 𝗔. 𝗕𝗮𝗱𝗲𝗿. David A. Bader is a Professor in the Department of Computer Science and founder of the Department of Data Science and inaugural Director of the 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 at 𝗡𝗲𝘄 𝗝𝗲𝗿𝘀𝗲𝘆 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆.

This talk presents graph analytics designed and implemented for Arkouda, an open-source framework for providing NumPy-like Python functionality on large datasets.

Supercomputing has become an essential tool for computational science and engineering and such real-world problems as weather …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are …

https://aiweek.com/live/streaming-analytics

Oliver Alvarado Rodriguez (presenter), Zhihui Du and David Bader; New Jersey Institute of Technology Exploratory graph analytics is a much sought out approach to help extract useful information from graphs. One of its main challenges arises when the size of the graph expands outside of the memory capacity that a typical computer can handle.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Panel Topic: Best practices for Data-Intensive applications Panelists: Nirav Merchant, University of Arizona Data Science Institute David A. Bader, Institute for Data Science at the New Jersey Institute of Technology Sandra Gesing, US-RSE & Center for Research Computing of University of Notre Dame Daniel S.

David A. Bader Distinguished Professor in the Department of Computer Science Director of the Institute for Data Science at New Jersey Institute of Technology Title: Solving Global Grand Challenges with High Performance Data Analytics

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

AGENDA Tuesday October 27 1:00 PM - 1:05 PM Welcome and Overview (All Times are ET) 1:05 PM - 1:10 PM NJIT Welcome from Dean Craig Gotsman, Dean of the Ying Wu College of Computing

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

As Data Science emerges as a new field, academic institutions around the world are following different routes toward establishing …

We’ll introduce a new algorithm called anti-section transitive closure (ATC) for finding the transitive closure of a graph, …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Government agencies across the globe are challenged with complex, ever-evolving problems. The answers exist somewhere in a vast amount of data, but they are only identifiable with the right technology to make sense of the interconnectedness within your data.

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

Deep learning has boosted the machine learning field at large and created significant increases in the performance of tasks including …

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community …

In this emerging era of analytics, nearly every government and industry sector envisions superiority by real-time decision making from …

The need to sift through massive datasets from applications in cybersecurity, social media, financial transactions, and sensor feeds, …

Government agencies and a variety of institutions from disparate areas (finance, healthcare, insurance, tele-communication, computing, …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Prof. David Bader, one of the nation’s leading experts in massive-scale graph analytics, presents a Neo4j case study on predictive …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Prof. David Bader, one of the nation’s leading experts in massive-scale graph analytics, presents a case study on predictive analytics …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Government agencies and a variety of institutions from disparate areas (finance, healthcare, insurance, tele-communication, computing, …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

This forward-looking panel will explore the future of CSE in a broad academic context. What new “grand challenges” may drive progress in CSE? How can CSE shape the future of new application fields such as computational medicine and biology, computational geoscience, and materials science?

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Supercomputing and Big Data: From Collision to Convergence As data intensive science emerges, the need for high performance computing (HPC) to converge capacity and capabilities with Big Data becomes more apparent and urgent.

On July 29, 2015, President Obama issued an Executive Order establishing the National Strategic Computing Initiative (NSCI) to ensure …

DESCRIPTION David Bader, School of Computational Science and Engineering Georgia Institute of Technology

http://www.usc.edu/dept/engineering/parlearning

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

David A. Bader, Aydin Buluc, John Gilbert, Joseph Gonzalez, Jeremy Kepner and Tim Mattson

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

The HPC Connection workshop aims to provide an international communication platform for High Performance Computing (HPC) users and …

Big Data and research computing go hand-in-hand, especially for research disciplines that operate at extreme scales. Current Big Data …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Large-scale data analytics applications represent increasingly important workloads but most of today’s supercomputers are ill …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Panel Discussion Abstract: There is a lot of excitement about “Big Data” which is at the intersection of the ongoing explosion in data …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Data intensive computing, popularly known as Big Data, has grown enormously in importance over the past 5 years. However, most data …

Cyber security increases in complexity and network connectivity every day. Todays problems are no longer limited to malware using hash …

Data intensive applications represent increasingly important workloads but are ill suited for most of todays machines. The Graph500 has …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in computational science and engineering, solving these problems at scale often raises new challenges because of sparsity and the lack of locality in the data, the need for additional research on scalable algorithms and development of frameworks for solving these problems on high performance computers, and the need for improved models that also capture the noise and bias inherent in the torrential data streams.

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Brief Biography David A. Bader is a Full Professor in the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology, and Executive Director for High Performance Computing.

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

https://cass-mt.pnnl.gov/pargraph.aspx

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

http://csitoday.com/2011/07/csi-to-host-two-day-workshop-on-data-intensive-computing-graphs-and-combinatorics-in-bio-informatics-finance-linguistics-and-national-security/

Organized by the Deutsches Klimarechenzentrum GmbH (DKRZ), Max-Planck-Institut für Meteorologie (MPI-M) and Cray Inc.

Data intensive supercomputer applications are increasingly important HPC workloads, but are ill suited for platforms designed for 3D …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

The STI Cell Broadband Engine (Cell BE) has shown the potential to provide outstanding performance, scalability, and flexibility in …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Tarek El-Ghazawi and leading lights in the software field ask (and maybe answer) the question, “Can Developing Applications for Massively Parallel Systems with Heterogeneous Processors Be Made Easy(er)?”

Talk slides

While we are still witnessing Moore’s Law by the steady production of chips that mass billions of transistors, clearly we have …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex …

Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex …

Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Whether petascale computing can be sustained into the future; or whether power, space, and cost will restrict the number of sites around the world is the central question for SOS11. The SOS series of workshops aims to assemble interdisciplinary teams and collaboratories to develop the required, state-of-the-art mathematical algorithms and software, supported by appropriate hardware and middleware infrastructure to use petascale (and beyond) computing resources effectively to advance fundamental research in compelling scientific missions.

Talk Slides

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Joint work with Kamesh Madduri.

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Talk slides

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Combinatorial problems such as those from graph theory pose serious challenges for parallel machines due to non-contiguous, concurrent …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Workshop on Building Scalable Simulations of Complex Socio-Technical Systems Complex socio-technical systems consist of a large number of interacting physical, technological, and human/societal components. Examples of such systems are urban regional transportation systems, national electrical powermarkets and grids, the Internet, ad-hoc communication and computing systems, public health, etc.

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

In this talk, we present a new framework for measuring the productivity of high-end systems, propose new performance metrics, and …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

http://vulcan.ee.iastate.edu/~aluru/bcb2002/

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Moderator: Craig Stewart, Indiana University Panelists: Chris Johnson (Utah); John Reynders (Celera); David Bader (New Mexico); Debra Goldfarb (IDC); Rick Stevens (Argonne National Laboratories and the University of Chicago) The biological/biomedical/bioinformatic/genomic/proteomic sciences now require the use of massive high performance computing (HPC) resources.

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and …

Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very …

Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and …

Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and …

IEEE Outstanding Young Engineering Award Talk, Cosponsored by the Electrical & Computer Engineering Department David Bader is an Assistant Professor of EECE at the University of New Mexico (UNM). He will review recent new engineering developments in parallel computing algorithms and parallel computers which may break the bottleneck of sequential computing for practical applications that require complex combinatorial optimization.

Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and …

Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and …

A fundamental problem in high-performance computing is to design high-level, architecture independent, algorithms that execute …

A fundamental problem in high-performance computing is to design high-level, architecture independent, algorithms that execute …

With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of …

A common statistical problem is that of finding the median element in a set of data. This paper presents an efficient randomized …

https://web.archive.org/web/19991126010709/http://chautauqua.bu.edu/ https://web.archive.org/web/20000206063025/http://www.ncsa.uiuc.edu/alliance/chautauqua/1999Presentations.html

Clusters - the Most Rapidly Growing Architecture of High-end Computing https://web.archive.org/web/20000914051025/http://www.ccs.uky.edu/~chautauqua/ Agenda: https://web.archive.org/web/20001101123729/http://www.ccs.uky.edu/~chautauqua/agenda.html

https://web.archive.org/web/19991126000736/http://chautauqua.ahpcc.unm.edu/ CLUSTERS - The Most Rapidly Growing Architecture of High-End Computing Dr. David A. Bader, University of New Mexico, and lead for the UNM Roadrunner Linux-based Supercluster, will talk about the “next wave” in high performance computing.

With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of …

With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of …

With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of …

A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute …

A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute …

A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute …

A fundamental challenge for parallel computing is to obtain high-level, architecture independent, algorithms which execute efficiently …

This talk presents efficient and portable implementations for several useful primitives in image processing algorithms, such as …