Inductee into University of Maryland's A. James Clark School of Engineering Innovator Hall of Fame

NVIDIA AI Lab (NVAIL) Award

Invited attendee to the White House’s National Strategic Computing Initiative (NSCI) Anniversary Workshop.

Facebook AI System Hardware/Software Co-Design Research Award

Named a member of "People to Watch" by HPC Wire

2012
Department of Electrical and Computer Engineering

The first recipient of the University of Maryland's Distinguished Alumni Award

Selected by Sony, Toshiba, and IBM to direct the first Center of Competence for the Cell Processor

AAAS Fellow

IEEE Fellow

SIAM Fellow

ACM Fellow

Cybersecurity Challenges in the Age of Generative AI

What CISOs need to know to mitigate quantum computing risks

Tailoring parallel alternating criteria search for domain specific MIPs: Application to maritime inventory routing

High-Performance Phylogenetic Inference

Numerically approximating centrality for graph ranking guarantees

Ranking in dynamic graphs using exponential centrality

Scalable and High Performance Betweenness Centrality on the GPU [Best Student Paper Finalist]

STINGER: High performance data structure for streaming graphs [Best Paper Award]

Large-Scale Data Analytics For Cybersecurity And Solving Real-World Grand Challenges | Redefining CyberSecurity With Professor David Bader

Massive Graph Analytics (Chapman &amp; Hall/CRC Data Science Series)

Although the highly anticipated petascale computers of the near future will perform at an order of magnitude faster than today’s quickest supercomputer, the scaling up of algorithms and applications for this class of computers remains a tough challenge. From scalable algorithm design for massive concurrency toperformance analyses and scientific visualization, Petascale Computing: Algorithms and Applications captures the state of the art in high-performance computing algorithms and applications. Featuring contributions from the world’s leading experts in computational science, this edited collection explores the use of petascale computers for solving the most difficult scientific and engineering problems of the current century. Covering a wide range of important topics, the book illustrates how petascale computing can be applied to space and Earth science missions, biological systems, weather prediction, climate science, disasters, black holes, and gamma ray bursts. It details the simulation of multiphysics, cosmological evolution, molecular dynamics, and biomolecules. The book also discusses computational aspects that include the Uintah framework, Enzo code, multithreaded algorithms, petaflops, performance analysis tools, multilevel finite element solvers, finite element code development, Charm++, and the Cactus framework. Supplying petascale tools, programming methodologies, and an eight-page color insert, this volume addresses the challenging problems of developing application codes that can take advantage of the architectural features of the new petascale systems in advance of their first deployment.

Petascale Computing: Algorithms and Applications (Chapman &amp; Hall/CRC Computational Science Series)

The hybrid/heterogeneous nature of future microprocessors and large high-performance computing systems will result in a reliance on two major types of components: multicore/manycore central processing units and special purpose hardware/massively parallel accelerators. While these technologies have numerous benefits, they also pose substantial performance challenges for developers, including scalability, software tuning, and programming issues.   Researchers at the Forefront Reveal Results from Their Own State-of-the-Art WorkEdited by some of the top researchers in the field and with contributions from a variety of international experts, Scientific Computing with Multicore and Accelerators focuses on the architectural design and implementation of multicore and manycore processors and accelerators, including graphics processing units (GPUs) and the Sony Toshiba IBM (STI) Cell Broadband Engine (BE) currently used in the Sony PlayStation 3. The book explains how numerical libraries, such as LAPACK, help solve computational science problems; explores the emerging area of hardware-oriented numerics; and presents the design of a fast Fourier transform (FFT) and a parallel list ranking algorithm for the Cell BE. It covers stencil computations, auto-tuning, optimizations of a computational kernel, sequence alignment and homology, and pairwise computations. The book also evaluates the portability of drug design applications to the Cell BE and illustrates how to successfully exploit the computational capabilities of GPUs for scientific applications. It concludes with chapters on dataflow frameworks, the Charm++ programming model, scan algorithms, and a portable intracore communication framework.  Explores the New Computational Landscape of Hybrid Processors By offering insight into the process of constructing and effectively using the technology, this volume provides a thorough and practical introduction to the area of hybrid computing. It discusses introductory concepts and simple examples of parallel computing, logical and performance debugging for parallel computing, and advanced topics and issues related to the use and building of many applications. 

Scientific Computing with Multicore and Accelerators (Chapman &amp; Hall/CRC Computational Science)

Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: What are the communities within an (online) social network? How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer? How must components be organized on a computer chip such that they can communicate efficiently with each other? What are the segments of a digital image? Which functions are certain genes (most likely) responsible for? The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyze various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails. This book is published in cooperation with the Center for Discrete Mathematics and Theoretical Computer Science.

Graph Partitioning and Graph Clustering (Contemporary Mathematics)

NVIDIA AI Lab (NVAIL) for Scalable Graph Algorithms

Facebook AI Systems Hardware/Software Co-Design research award on Scalable Graph Learning Algorithms

The New Jersey Institute of Technology is a public research university in the University Heights neighborhood of Newark, New Jersey. Centrally located in the New York metropolitan area, its campus is within walking distance of Downtown Newark.

New Jersey Institute of Technology

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

Dr. Bader is a Fellow of the IEEE, ACM, AAAS, and SIAM; a recipient of the IEEE Sidney Fernbach Award; and the 2022 Innovation Hall of Fame inductee of the University of Maryland’s A. James Clark School of Engineering. He advises the White House, most recently on the National Strategic Computing Initiative (NSCI) and Future Advanced Computing Ecosystem (FACE).

Bader is a leading expert in solving global grand challenges in science, engineering, computing, and data science. His interests are at the intersection of high-performance computing and real-world applications, including cybersecurity, massive-scale analytics, and computational genomics, and he has co-authored over 300 scholarly papers and has best paper awards from ISC, IEEE HPEC, and IEEE/ACM SC. Dr. Bader has served as a lead scientist in several DARPA programs including High Productivity Computing Systems (HPCS) with IBM, Ubiquitous High Performance Computing (UHPC) with NVIDIA, Anomaly Detection at Multiple Scales (ADAMS), Power Efficiency Revolution For Embedded Computing Technologies (PERFECT), Hierarchical Identify Verify Exploit (HIVE), and Software-Defined Hardware (SDH).

Dr. Bader is Editor-in-Chief of the ACM Transactions on Parallel Computing, and previously served as Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems. He serves on the leadership team of Northeast Big Data Innovation Hub as the inaugural chair of the Seed Fund Steering Committee. ROI-NJ recognized Bader as a technology influencer on its 2021 inaugural and 2022 lists.

In 2012, Bader was the inaugural recipient of University of Maryland’s Electrical and Computer Engineering Distinguished Alumni Award. In 2014, Bader received the Outstanding Senior Faculty Research Award from Georgia Tech. Bader has also served as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor and Director of an NVIDIA GPU Center of Excellence.

In 1998, Bader built the first Linux supercomputer that led to a high-performance computing (HPC) revolution, and Hyperion Research estimates that the total economic value of Linux supercomputing pioneered by Bader has been over $100 trillion over the past 25 years.

Graph Analytics

Massive-Scale Analytics

High-Performance Computing

Data Science

Applications in Cybersecurity

Computational Genomics

Faculty Website

Google Scholar Profile

ResearchGate Profile

Wikipedia Page

This New AI Brain Decoder Could Be A Privacy Nightmare, Experts Say

Common password mistakes you're making that could get you hacked

The Democratization of Data Science Tools with Dr. David Bader

Academic Data Science Alliance Picks Up Steam

‘Weaponised app’: Is Egypt spying on COP27 delegates’ phones?

Your Hard Drive May One Day Use Diamonds for Storage

Big Data Career Notes: July 2019 Edition

David Bader to Lead New Institute for Data Science at NJIT

Massive-scale Analytics

Predictive Analytics from Massive Streaming Data

Massive-Scale Analytics Applied to Real-World Problems

Quantum computing. This technology, with its potential to solve complex problems exponentially faster than classical computers, could revolutionize fields ranging from cryptography to drug discovery, climate modeling and beyond. Quantum computing's promise to tackle challenges currently beyond our reach, due to its fundamentally different approach to processing information, represents a leap forward in our computational capabilities. Its convergence with AI could lead to unprecedented advancements, making this era an incredibly thrilling time to be at the forefront of computing and data science.

what-other-emerging-technologies-excite-you-in-their-potential-to-transform-computing

What other emerging technologies excite you in their potential to transform computing?


While it’s natural to harbor concerns about the rapid progression of AI, allowing fear to dominate the discourse would be a disservice to the potential benefits these technologies can offer. Instead, this moment calls for proactive engagement with AI and an investment in understanding its inner workings, limitations and the ethical dilemmas it presents. By advocating for responsible AI development, emphasizing education and promoting transparency, we can foster an environment where AI serves as a tool for societal advancement. This approach ensures that we remain at the helm of AI's trajectory, steering it toward outcomes that uplift humanity rather than scenarios that fuel dystopian fears.

theres-a-scifi-plot-where-computers-get-so-smart-that-people-lose-control-the-new-class-of-userfriendly-ai-is-making-people-excited-but-also-nervous-should-we-be-afraid

There’s a sci-fi plot where computers get so smart that people lose control. The new class of user-friendly AI is making people excited but also nervous. Should we be afraid?


It’s important to be aware of how AI decisions are made, the potential biases in AI systems and the ethical considerations of AI use. Additionally, developing data literacy is crucial, as it enables individuals to evaluate AI outputs and understand the importance of data quality and biases. A basic grasp of AI and machine learning concepts — even without programming skills — can demystify AI technologies and reveal their potential applications. Staying informed about AI advancements across various sectors can also inspire innovative ideas and foster interdisciplinary collaborations.

what-should-nonprogrammers-learn-about-ai

What should non-programmers learn about AI?


Echelon: Extreme-scale Compute Hierarchies with Efficient Locality-Optimized Nodes

Center for Adaptive Supercomputing Software for Multithreaded Architectures (CASS-MT): Analyzing Massive Social Networks

Proactive Detection of Insider Threats with Graph Analysis at Multiple Scales (PRODIGAL), under Anomoly Detection at Multiple Scales (ADAMS)

Challenge Applications and Scalable Metrics (CHASM) for Ubiquitous High Performance Computing

SHARP: Software Toolkit for Accelerating Graph Algorithms on Hive Processors

GRATEFUL: GRaph Analysis Tackling power EFficiency, Uncertainty, and Locality

expert-qa-njits-david-bader-on-ai-data-science-quantum-computing

Distinguished Professor, Data Science

Artificial intelligence, data science and the emerging field of quantum computing areamong the hottest research topics in computing today. David Bader, a distinguishedprofessor at New Jersey Institute of Technology’s Ying Wu College of Computing andthe director of the university’s Institute for Data Science, offers his take on each.The Computer History Museum has recognized you for developing a Linuxsupercomputer using commodity hardware. Was that a life-shaping lesson?It was a venture into the unknown, leveraging the emerging potential of Linux and commodity hardware to build something that was both accessible and powerful. This experience taught me the importance of embracing risk and the value of resilience. There were technical hurdles, skepticism from peers and the daunting task of venturing beyond established norms. Also, the Linux supercomputer project was not just about the technology. It was about building a community around an idea.How do user-friendly AI systems like ChatGPT impact your work?It enriches the palette of methodologies and technologies at our disposal, enabling us to tackle more ambitious projects with greater efficiency and creativity. By integrating these AI systems into our research and educational programs, we're not just enhancing our ability to process and analyze data.  We're also empowering students and researchers with the means to innovate and explore new horizons in data science without being hindered by the technical complexities that once acted as barriers.Some information workers fear that AI will make their careers obsolete. But tech progress can’t be stopped, so how should people adapt?By embracing these technologies, learning to work alongside them and leveraging their capabilities to enhance our own skill sets and productivity. Also, it's important to focus on the uniquely human skills that AI cannot replicate, such as creativity, emotional intelligence and critical thinking. By honing these abilities, workers can ensure they remain irreplaceable components of the workforce, capable of tasks that require a human touch — from complex decision-making to empathetic interactions with customers or clients.What should non-programmers learn about AI?It’s important to be aware of how AI decisions are made, the potential biases in AI systems and the ethical considerations of AI use. Additionally, developing data literacy is crucial, as it enables individuals to evaluate AI outputs and understand the importance of data quality and biases. A basic grasp of AI and machine learning concepts — even without programming skills — can demystify AI technologies and reveal their potential applications. Staying informed about AI advancements across various sectors can also inspire innovative ideas and foster interdisciplinary collaborations.There’s a sci-fi plot where computers get so smart that people lose control. The new class of user-friendly AI is making people excited but also nervous. Should we be afraid?While it’s natural to harbor concerns about the rapid progression of AI, allowing fear to dominate the discourse would be a disservice to the potential benefits these technologies can offer. Instead, this moment calls for proactive engagement with AI and an investment in understanding its inner workings, limitations and the ethical dilemmas it presents. By advocating for responsible AI development, emphasizing education and promoting transparency, we can foster an environment where AI serves as a tool for societal advancement. This approach ensures that we remain at the helm of AI's trajectory, steering it toward outcomes that uplift humanity rather than scenarios that fuel dystopian fears.What other emerging technologies excite you in their potential to transformcomputing?Quantum computing. This technology, with its potential to solve complex problems exponentially faster than classical computers, could revolutionize fields ranging from cryptography to drug discovery, climate modeling and beyond. Quantum computing's promise to tackle challenges currently beyond our reach, due to its fundamentally different approach to processing information, represents a leap forward in our computational capabilities. Its convergence with AI could lead to unprecedented advancements, making this era an incredibly thrilling time to be at the forefront of computing and data science.Looking to know more? We can help.David Bader is available to discuss AI, quantum computing and data science withmedia. Simply click on his icon to arrange an interview.

#Expert Q&A: NJIT’s David Bader on AI, Data Science, Quantum Computing

what-big-data-tells-us-in-health-care-cybersecurity-sustainability-and-elections

Every day sees more data created around the world than every day before, with a staggering 64 billion terabytes amassed in 2020 alone.Big data leaves large organizations wondering how to manage their share and individuals concerned about how to be heard. And both groups fret about information overload, privacy and security.NJIT’s David Bader is adept at discussing these issues as a nationally recognized data scientist who seeks new ways for companies to analyze massive data streams that arrive in real-time. He’s also a pioneer of Linux supercomputing who’s developing software to bring the power of supercomputers to regular people.Additionally, as director of NJIT's Institute for Data Science, Bader seeks powerful solutions to myriad global challenges. As he explains:“We personalized health and medicine. We look at cyber security applications. We look at urban sustainability. We even look at things like, how do we ensure trustworthy elections?” To interview him, simply click on the button below.David's Profile  

What big data tells us – in health care, cybersecurity, sustainability and elections

Prof. David Bader, one of the nation’s leading experts in massive-scale graph analytics, presents a Neo4j case study on predictive analytics on a homeland security knowledge graph that connects disparate data from multiple sources such as spreadsheets and relational databases. Graphs are a natural representation for connecting information in real-world challenges such as understanding financial transactions in digital currencies, finding new communities in social networks, increasing power grid resiliency, and protecting us from cyberattack. Bader discusses his Spatio-Temporal Interaction Networks and Graphs (STING) initiative that supports new methods for finding interesting patterns and features in these critical knowledge graphs.

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

#KnowledgeGraphs #Neo4 #GraphConnect

Predictive Analysis from Massive Knowledge Graphs on Neo4j – David Bader

In this video from PASC18, David Bader from Georgia Tech summarizes his keynote talk on Big Data Analytics.

Learn more: https://pasc18.pasc-conference.org/

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Interview: David Bader on Real World Challenges for Big Data Analytics,

5-Minute Interview: Dave Bader, Professor at Georgia Tech College of Computing

Dr. David A. Bader, Distinguished Professor and Director of NJIT's Institute for Data Science discusses the future of artificial intelligence and how this technology will impact the workforce, democracy, and our everyday lives.

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