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David A. Bader

Distinguished Professor and Director of the Institute for Data Science

New Jersey Institute of Technology

About

David A. Bader is a Distinguished Professor in the Department of Computer Science in the Ying Wu College of Computing and Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. He is a Fellow of the IEEE, AAAS, and SIAM.

Interests

  • Data Science
  • High Performance Computing
  • Real-World Analytics

Education

  • PhD in Electrical Engineering, 1996

    University of Maryland

  • MS in Electrical Engineering, 1991

    Lehigh University

  • BS in Computer Engineering, 1990

    Lehigh University

Biography

David A. Bader is a Distinguished Professor in the Department of Computer Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. He is a Fellow of the IEEE, AAAS, and SIAM, and advises the White House, most recently on the National Strategic Computing Initiative (NSCI). Dr. 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 250 scholarly papers. 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). He has also served as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor. Bader is a cofounder of the Graph500 List for benchmarking “Big Data” computing platforms. Bader is recognized as a “RockStar” of High Performance Computing by InsideHPC and as HPCwire’s People to Watch in 2012 and 2014. In April 2019, Bader was awarded an NVIDIA AI Lab (NVAIL) award, and in July 2019, Bader received a Facebook Research AI Hardware/Software Co-Design award.

Experience

 
 
 
 
 

Distinguished Professor

New Jersey Institute of Technology

Jul 2019 – Present Newark, NJ
Department of Computer Science, Ying Wu College of Computing
 
 
 
 
 

Professor

Georgia Institute of Technology

Aug 2005 – Jun 2019 Atlanta, GA
Chair, School of Computational Science and Engineering.
 
 
 
 
 

Associate Professor and Regents’ Lecturer

University of New Mexico

Jan 1998 – Jul 2005 Albuquerque, NM
Department of Electrical and Computer Engineering.

Recent Boards

 
 
 
 
 

Advisory Board Member

Trovares

2019 – Present Seattle, WA
 
 
 
 
 

Advisory Council Member

Electrical and Computer Engineering Department, Lehigh University

2018 – Present Bethlehem, PA
 
 
 
 
 

Advisory Board Member

Accelogic, LLC

2015 – Present Weston, FL
 
 
 
 
 

Advisory Committee on High Performance Computing

Council on Competitiveness

2014 – 2019 Washington, DC
 
 
 
 
 

Advisory Committee on Cyberinfrastructure

National Science Foundation

2014 – 2017
 
 
 
 
 

Board of Governors

IEEE Computer Society

2014 – 2016
 
 
 
 
 

Board Member

Computing Research Association

2013 – 2014 Washington, DC
 
 
 
 
 

Advisory Council Member

Internet2

2007 – 2011
 
 
 
 
 

Advisory Board Member

DSPlogic, Inc.

2006 – Present Frederick, MD

Recent Posts

NJIT’s Ying Wu College of Computing Launches New Location in Jersey City

NJIT@JerseyCity is located at 101 Hudson Street on the Jersey City waterfront and, in addition to an ultra-modern learning environment, also provides an expansive view of the iconic Manhattan skyline. NJIT’s Ying Wu College of Computing (YWCC) offers a master’s degree in Data Science as well as graduate certificates in Big Data and Data Mining at NJIT @JerseyCity. YWCC plans to add a graduate certificate in Data Visualization in spring 2020 and further expand next fall to include Cyber Security graduate programs.

This Week in Neo4j

Our featured community member this week is Dr. David Bader, Distinguished Professor at New Jersey Institute of Technology. Dr. David Bader – This Week’s Featured Community Member Without doing too much ego-boosting, we can just say David is a graph-addict for a long time before it was a ‘thing’. Alongside his role as a professor, he’s a fellow of the IEEE, AAAS, and SIAM, advises the White House, and the National Strategic Computing Initiative (NSCI).

Data science expert Bader looks to Fed funding for info analysis

By Evan Koblentz Data science has reached a point where techniques such as deep learning can beat humans at recognizing objects, although experts are still figuring out how to make explainable predictions from massive data, NJIT distinguished professor David Bader said. Bader leads the university’s Institute for Data Science in collaboration with the Ying Wu College of Computing, Newark College of Engineering, Martin Tuchman School of Management, and College of Science and Liberal Arts.

The Chronicle of Higher Education / NJIT

David Bader Distinguished Professor and Director of NJIT’s Institute for Data Science What is NJIT’s new Institute for Data Science? The growing abundance and variety of data we amass gives us unprecedented opportunities to improve lives in multifold arenas - manufacturing, health care, financial management, data protection, food safety and traffic navigation are just a few. The Institute for Data Science (IDS) will focus NJIT’s multidisciplinary research and workforce skills training on developing technology leaders who will solve global challenges involving data and high-performance computing (HPC).

Supercomputer analyzes web traffic across entire internet

By Rob Matheson, MIT News Office Using a supercomputing system, MIT researchers developed a model that captures what global web traffic could look like on a given day, including previously unseen isolated links (left) that rarely connect but seem to impact core web traffic (right). Image courtesy of the researchers, edited by MIT News Using a supercomputing system, MIT researchers have developed a model that captures what web traffic looks like around the world on a given day, which can be used as a measurement tool for internet research and many other applications.

People

Faculty

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David A. Bader

Distinguished Professor and Director of the Institute for Data Science

Staff

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Zhihui Du

Research Scientist

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Selenny Fabre

Business Manager

Postdoctoral Alumni

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Tanya Berger-Wolf

Director, Translational Data Analytics Institute

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Yuzhong Sun

Professor

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Tiffani L. Williams

Teaching Professor and Director of Onramp Programs

PhD Alumni

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Virat Agarwal

Executive Directory, Head of Commodities Structuring

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Guojing Cong

Research Staff Member and Manager of Machine Learning and Workflow

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David Ediger

Senior Research Engineer

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James Fairbanks

Research Engineer

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Oded Green

Senior Solutions Architect

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Seunghwa Kang

Senior Software Engineer

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Jinyang Liu

Software Engineer

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Kamesh Madduri

Associate Professor

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Adam McLaughlin

Research Scientist / Engineer

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Lluís-Miquel Munguía

Software Engineer

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Eisha Nathan

Computational Scientist

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Emily Rogers

Researcher

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Vipin Sachdeva

Associate Director, Head of HPC

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Matthew Sottile

Affiliate Graduate Faculty

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Mi Yan

Data Scientist

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Zhaoming Yin

Software Engineer

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Anita Zakrzewska

Senior Member of Technical Staff

Projects

NVIDIA AI Lab (NVAIL) for Scalable Graph Algorithms

Research Directions 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.

Facebook Research

Facebook AI Systems Hardware/Software Co-Design research award on Scalable Graph Learning Algorithms https://research.fb.com/blog/2019/05/announcing-the-winners-of-the-ai-system-hardware-software-co-design-research-awards/ 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.

HORNET

High-Performance Streaming Graph Analytics on GPUs

STINGER

Dynamic graphs are all around us. Social networks containing interpersonal relationships and communication patterns. Information on the Internet, Wikipedia, and other datasources. Disease spread networks and bioinformatics problems. Business intelligence and consumer behavior. The right software can help to understand the structure and membership of these networks and many others as they change at speeds of thousands to millions of updates per second. Motivation The application of graph analysis has proven to be a useful abstraction for solving many important problems accross a variety of disciplines.

cuSTINGER

dynamic graph data structures and streaming algorithms for GPU

GTfold

Scalable Multicore Code for RNA Secondary Structure Prediction

GraphBLAS

The GraphBLAS Forum is an open effort to define standard building blocks for graph algorithms in the language of linear algebra. We believe that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. We believe that it is critical to move quickly and define such a standard, thereby freeing up researchers to innovate and diversify at the level of higher level algorithms and graph analytics applications. This effort was inspired by the Basic Linear Algebra Subprograms (BLAS) of dense Linear Algebra, and hence our working name for this standard is “the GraphBLAS”.

GraphCT: Graph Characterization Toolkit

Cray XMT software developed in collaboration with PNNL

Multicore SWARM: Software and Algorithms for Running on Multicore Processors

an open source library for developing efficient and portable implementations that make use of multi-core processors

Recent Publications

Quickly discover relevant content by filtering publications.

Traversing Large Graphs on GPUs with Unified Memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of …

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that …

High-Performance Phylogenetic Inference

Software tools based on the maximum likelihood method and Bayesian methods are widely used for phylogenetic tree inference. This …

Skip the Intersection: Quickly Counting Common Neighbors on Shared-Memory Systems

Counting common neighbors between all vertex pairs in a graph is a fundamental operation, with uses in similarity measures, link …

Recent & Upcoming Talks

HPC User Forum 2020 Invited Speaker: 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 …

Distinguished Speaker Series, College of Science, Rochester Institute of Technology, 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 …

Fall 2019 Applied Math Colloquium, NJIT: 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 …

NYU CS Invited Talk: 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 …

EAS 2019 Keynote Talk: 'Carnac the Magnificent' in the Age of High Performance Data Analytics

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

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