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 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). Bader serves on the leadership team of Northeast Big Data Innovation Hub as the inaugural chair of the Seed Fund Steering Committee. 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). Bader is Editor-in-Chief of the ACM Transactions on Parallel Computing, and will serve as General Co-Chair of IPDPS 2021. 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. Recently, Bader received an NVIDIA AI Lab (NVAIL) award (2019), and a Facebook Research AI Hardware/Software Co-Design award (2019).

Experience

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

Recent Boards

 
 
 
 
 
Steering Committee Chair, Seed Fund
May 2020 – Present New York, NY
 
 
 
 
 
Advisory Board Member
Mar 2020 – Present Cambridge, MA
 
 
 
 
 
Strategic Advisory Board Member
Sep 2019 – Present Palo Alto, CA
 
 
 
 
 
Advisory Board Member
Jan 2019 – Apr 2020 Seattle, WA
 
 
 
 
 
Advisory Council Member
Jan 2018 – Present Bethlehem, PA
 
 
 
 
 
Advisory Board Member
Jun 2015 – Jun 2019 Weston, FL
 
 
 
 
 
Advisory Committee on High Performance Computing
Jan 2014 – Jun 2019 Washington, DC
 
 
 
 
 
Advisory Committee on Cyberinfrastructure
Jan 2014 – Dec 2017
 
 
 
 
 
Board of Governors
Jan 2014 – Dec 2016
 
 
 
 
 
Board Member
Jan 2013 – Dec 2014 Washington, DC
 
 
 
 
 
Advisory Council Member
Jan 2007 – Dec 2011
 
 
 
 
 
Advisory Board Member
Aug 2006 – Jun 2019 Frederick, MD

Recent Posts

People

Faculty

Avatar

David A. Bader

Distinguished Professor and Director of the Institute for Data Science

Staff

Avatar

Selenny Fabre

Business Manager

Avatar

Zhihui Du

Research Scientist

Postdoctoral Alumni

Avatar

Tanya Berger-Wolf

Director, Translational Data Analytics Institute

Avatar

Tiffani L. Williams

Teaching Professor and Director of Onramp Programs

Avatar

Yuzhong Sun

Professor

PhD Students

PhD Alumni

Avatar

Adam McLaughlin

Research Scientist / Engineer

Avatar

Anita Zakrzewska

Senior Member of Technical Staff

Avatar

David Ediger

Senior Research Engineer

Avatar

Eisha Nathan

Computational Scientist

Avatar

Emily Rogers

Researcher

Avatar

Guojing Cong

Research Staff Member and Manager of Machine Learning and Workflow

Avatar

James Fairbanks

Assistant Professor

Avatar

Jinyang Liu

Software Engineer

Avatar

Kamesh Madduri

Associate Professor

Avatar

Lluís-Miquel Munguía

Software Engineer

Avatar

Matthew Sottile

Affiliate Graduate Faculty

Avatar

Mi Yan

Data Scientist

Avatar

Oded Green

Senior Solutions Architect

Avatar

Seunghwa Kang

Senior Software Engineer

Avatar

Vipin Sachdeva

Associate Director, Head of HPC

Avatar

Virat Agarwal

Executive Directory, Head of Commodities Structuring

Avatar

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

Recent & Upcoming Talks

Contact