David A. Bader

David A. Bader

Distinguished Professor and Director of the Institute for Data Science

New Jersey Institute of Technology

Biography

David A. Bader is a Distinguished Professor in the Department of Computer Science and founder of the Department of Data 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 and founder of the Department of Data Science and inaugural 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.

Dr. Bader is a Fellow of the IEEE, AAAS, and SIAM, and a recipient of the IEEE Computer Society Sidney Fernbach Award. 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). Recently, Bader received an NVIDIA AI Lab (NVAIL) award, and a Facebook Research AI Hardware/Software Co-Design award.

Dr. Bader is Editor-in-Chief of the ACM Transactions on Parallel Computing, and General Co-Chair of IPDPS 2021, 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. In 2021, ROI-NJ recognized Bader on its inaugural list of technology influencers, and 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. He is a cofounder of the Graph500 List for benchmarking “Big Data” computing platforms. He is recognized as a “RockStar” of High Performance Computing by InsideHPC and as HPCwire’s People to Watch in 2012 and 2014.

Experience

 
 
 
 
 
New Jersey Institute of Technology
Distinguished Professor
Jul 2019 – Present Newark, NJ
Department of Computer Science, Ying Wu College of Computing
 
 
 
 
 
Georgia Institute of Technology
Professor
Aug 2005 – Jun 2019 Atlanta, GA
Chair, School of Computational Science and Engineering.
 
 
 
 
 
University of New Mexico
Associate Professor and Regents' Lecturer
Jan 1998 – Jul 2005 Albuquerque, NM
Department of Electrical and Computer Engineering.

Recent Boards

 
 
 
 
 
EdgeDiscovery, NJEdge Inc.
Advisory Council Member
Aug 2020 – Present Newark, NJ
 
 
 
 
 
ARLIS, University of Maryland
Advisory Board Member
Jul 2020 – Present College Park, MD
 
 
 
 
 
Northeast Big Data Innovation Hub
Steering Committee Chair, Seed Fund
May 2020 – Present New York, NY
 
 
 
 
 
OpenCilk
Advisory Board Member
Mar 2020 – Present Cambridge, MA
 
 
 
 
 
Open Source Election Technology Institute
Strategic Advisory Board Member
Sep 2019 – Present Palo Alto, CA
 
 
 
 
 
Trovares
Advisory Board Member
Jan 2019 – Apr 2020 Seattle, WA
 
 
 
 
 
Electrical and Computer Engineering Department, Lehigh University
Advisory Council Member
Jan 2018 – Present Bethlehem, PA
 
 
 
 
 
Accelogic, LLC
Advisory Board Member
Jun 2015 – Jun 2019 Weston, FL
 
 
 
 
 
Council on Competitiveness
Advisory Committee on High Performance Computing
Jan 2014 – Jun 2019 Washington, DC
 
 
 
 
 
National Science Foundation
Advisory Committee on Cyberinfrastructure
Jan 2014 – Dec 2017
 
 
 
 
 
IEEE Computer Society
Board of Governors
Jan 2014 – Dec 2016
 
 
 
 
 
Computing Research Association
Board Member
Jan 2013 – Dec 2014 Washington, DC
 
 
 
 
 
Internet2
Advisory Council Member
Jan 2007 – Dec 2011
 
 
 
 
 
DSPlogic, Inc.
Advisory Board Member
Aug 2006 – Jun 2019 Frederick, MD

Recent Posts

People

Faculty

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David A. Bader

Distinguished Professor and Director of the Institute for Data Science

Staff

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Selenny Fabre

Business Manager

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Zhihui Du

Research Scientist

Postdoctoral Alumni

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Tanya Berger-Wolf

Director, Translational Data Analytics Institute

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Tiffani L. Williams

Teaching Professor and Director of Onramp Programs

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Yuzhong Sun

Professor

PhD Students

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Fuhuan Li

PhD Student

PhD Alumni

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Adam McLaughlin

Research Scientist / Engineer

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Anita Zakrzewska

Senior Member of Technical Staff

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David Ediger

Senior Research Engineer

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Eisha Nathan

Computational Scientist

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Emily Rogers

Researcher

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Guojing Cong

Senior Staff

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James Fairbanks

Assistant Professor

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Jinyang Liu

Software Engineer

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Kamesh Madduri

Associate Professor

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Lluís-Miquel Munguía

Software Engineer

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Matthew Sottile

Affiliate Graduate Faculty

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Mi Yan

Senior Applied Research Engineer

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Oded Green

Senior Solutions Architect

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Seunghwa Kang

Senior Software Engineer

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Vipin Sachdeva

Associate Director, Head of HPC

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Virat Agarwal

Executive Directory, Head of Commodities Structuring

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Zhaoming Yin

Software Engineer

Projects

High Performance Algorithms for Interactive Data Science at Scale
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. This award will design and implement fundamental algorithms for high performance computing solutions that enable the interactive large-scale data analysis of massive data sets.
High Performance Algorithms for Interactive Data Science at Scale
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.
NVIDIA AI Lab (NVAIL) for Scalable Graph Algorithms
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.
Facebook Research
HORNET
High-Performance Streaming Graph Analytics on GPUs
HORNET
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.
STINGER
cuSTINGER
dynamic graph data structures and streaming algorithms for GPU
cuSTINGER
GTfold
Scalable Multicore Code for RNA Secondary Structure Prediction
GTfold
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”.
GraphBLAS
GraphCT: Graph Characterization Toolkit
Cray XMT software developed in collaboration with PNNL
GraphCT: Graph Characterization Toolkit
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
Multicore SWARM: Software and Algorithms for Running on Multicore Processors

Books

Massive Graph Analytics (Chapman & Hall / CRC Press), 2022
Expertise in massive scale graph analytics is key for solving real-world grand challenges from health to sustainability to detecting insider threats, cyber defense, and more. Massive Graph Analytics provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government.
Massive Graph Analytics (Chapman & Hall / CRC Press), 2022
Scientific Computing with Multicore and Accelerators (Chapman & Hall / CRC Press), 2011
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.
Scientific Computing with Multicore and Accelerators (Chapman & Hall / CRC Press), 2011
Petascale Computing: Algorithms and Applications (Chapman & Hall / CRC Press), 2008
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.
Petascale Computing: Algorithms and Applications (Chapman & Hall / CRC Press), 2008

Recent & Upcoming Talks

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