David Bader

Distinguished Professor, Data Science

  • Newark NJ UNITED STATES
  • Director of the Institute for Data Science

Interests lie at the intersection of data science & high-performance computing, with applications in cybersecurity

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Spotlight

3 min

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

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.

David Bader

1 min

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

David Bader

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Biography

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.

Areas of Expertise

Graph Analytics
Massive-Scale Analytics
High-Performance Computing
Data Science
Applications in Cybersecurity
Computational Genomics

Accomplishments

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

2022

NVIDIA AI Lab (NVAIL) Award

2019

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

2019

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Education

University of Maryland

Ph.D.

Electrical and Computer Engineering

1996

Lehigh University

M.S.

Electrical Engineering

1991

Lehigh University

B.S.

Computer Engineering

1990

Affiliations

  • AAAS Fellow
  • IEEE Fellow
  • SIAM Fellow
  • ACM Fellow

Media Appearances

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

Lifewire  online

2023-05-08

The technique offers promise for stroke patients but could be invasive.

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

CBS News  online

2023-03-03

It's hard to memorize passwords as you juggle dozens of apps — whether you're logging in to stream your favorite show, view your medical records, check your savings account balance or more, you'll want to avoid unwanted prying eyes.

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The Democratization of Data Science Tools with Dr. David Bader

To the Point Cybersecurity podcast  online

2023-09-19

He deep dives into the opportunity to democratize data science tools and the awesome free tool he and Mike Merrill spent the last several years building that can be found on the Bears-R-Us GitHub page open to the public.

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Event Appearances

Massive-scale Analytics

13th International Conference on Parallel Processing and Applied Mathematics (PPAM)  BIalystok, Poland

2019-09-09

Predictive Analytics from Massive Streaming Data

44th Annual GOMACTech Conference: Artificial Intelligence & Cyber Security: Challenges and Opportunities for the Government  Albuquerque, NM

2019-03-26

Massive-Scale Analytics Applied to Real-World Problems

2018 Platform for Advanced Scientific Computing (PASC) Conference  Basel, Switzerland

2018-07-04

Research Focus

NVIDIA AI Lab (NVAIL) for Scalable Graph Algorithms

2019-08-05

Graph algorithms represent some of the most challenging known problems in computer science for modern processors. These algorithms contain far more memory access per unit of computation than traditional scientific computing. Access patterns are not known until execution time and are heavily dependent on the input data set. Graph algorithms vary widely in the volume of spatial and temporal locality that is usable my modern architectures. In today’s rapidly evolving world, graph algorithms are used to make sense of large volumes of data from news reports, distributed sensors, and lab test equipment, among other sources connected to worldwide networks. As data is created and collected, dynamic graph algorithms make it possible to compute highly specialized and complex relationship metrics over the entire web of data in near-real time, reducing the latency between data collection and the capability to take action.

With this partnership with NVIDIA, we collaborate on the design and implementation of scalable graph algorithms and graph primitives that will bring new capabilities to the broader community of data scientists. Leveraging existing open frameworks, this effort will improve the experience of graph data analysis using GPUs by improving tools for analyzing graph data, speeding up graph traversal using optimized data structures, and accelerating computations with better runtime support for dynamic work stealing and load balancing.

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

2019-05-10

Deep learning has boosted the machine learning field at large and created significant increases in the performance of tasks including speech recognition, image classification, object detection, and recommendation. It has opened the door to complex tasks, such as self-driving and super-human image recognition. However, the important techniques used in deep learning, e.g. convolutional neural networks, are designed for Euclidean data type and do not directly apply on graphs. This problem is solved by embedding graphs into a lower dimensional Euclidean space, generating a regular structure. There is also prior work on applying convolutions directly on graphs and using sampling to choose neighbor elements. Systems that use this technique are called graph convolution networks (GCNs). GCNs have proven to be successful at graph learning tasks like link prediction and graph classification. Recent work has pushed the scale of GCNs to billions of edges but significant work remains to extend learned graph systems beyond recommendation systems with specific structure and to support big data models such as streaming graphs.

This project will focus on developing scalable graph learning algorithms and implementations that open the door for learned graph models on massive graphs. We plan to approach this problem in two ways. First, developing a scalable high performance graph learning system based on existing GCNs algorithms, like GraphSage, by improving the workflow on shared-memory NUMA machines, balancing computation between threads, optimizing data movement, and improving memory locality. Second, we will investigate graph learning algorithm-specific decompositions and develop new strategies for graph learning that can inherently scale well while maintaining high accuracy. This includes traditional partitioning, however in general we consider breaking the problem into smaller pieces, which, when solved will result in a solution to the bigger problem. We will explore decomposition results from graph theory, for example, forbidden graphs and the Embedding Lemma, and determine how to apply such results into the field of graph learning. We will investigate whether these decompositions could assist in a dynamic graph setting.

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Research Grants

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

DARPA/NVIDIA

2010-06-01

Goal: Develop highly parallel, security enabled, power efficient processing systems, supporting ease of programming, with resilient execution through all failure modes and intrusion attacks

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

Department of Defense

2008-08-01

Exascale Streaming Data Analytics for social networks: understanding communities, intentions, population dynamics, pandemic spread, transportation and evacuation.

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

DARPA

2011-05-01

This paper reports on insider threat detection research, during which a prototype system (PRODIGAL)1 was
developed and operated as a testbed for exploring a range of detection and analysis methods. The data and test environment, system components, and the core method of unsupervised detection of insider threat leads are presented to document this work and benefit others working in the insider threat domain...

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Answers

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

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.

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?
David Bader

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 should non-programmers learn about AI?
David Bader

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.

Show More +

Articles

Cybersecurity Challenges in the Age of Generative AI

CTOTech Magazine

David Bader

2023-11-20

Cybersecurity professionals will not only have to discover malicious events at the time of occurrence, but also proactively implement preventative measures before an attack. For these professionals, the significant challenge will be protecting against new behaviors and methods that they are not yet familiar with.

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What CISOs need to know to mitigate quantum computing risks

Security

David Bader

2023-06-03

Quantum technologies harness the laws of quantum mechanics to solve complex problems beyond the capabilities of classical computers. Although quantum computing can one day lead to positive and transformative solutions for complex global issues, the development of these technologies also poses a significant and emerging threat to cybersecurity infrastructure for organizations.

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

Computers & Operations Research

Lluís-Miquel Munguía, Shabbir Ahmed, David A Bader, George L Nemhauser, Yufen Shao, Dimitri J Papageorgiou

2019

Parallel Alternating Criteria Search (PACS) relies on the combination of computer parallelism and Large Neighborhood Searches to attempt to deliver high quality solutions to any generic Mixed-Integer Program (MIP) quickly. While general-purpose primal heuristics are widely used due to their universal application, they are usually outperformed by domain-specific heuristics when optimizing a particular problem class.

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