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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

This Week in Neo4j

Our featured community member this week is Dr. David Bader, Distinguished Professor at New Jersey Institute of Technology. Dr. David …

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, …

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 …

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 …

Researchers Set to Receive Two Innovation Awards at HPEC’19

Defined by the practice of aggregating power in an effort to achieve greater performance, high-performance computing (HPC) is …

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. …

Facebook Research

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

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 …

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 …

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.

A Linear Time Algorithm for Finding Minimum Spanning Tree Replacement Edges

Given an undirected, weighted graph, the minimum spanning tree (MST) is a tree that connects all of the vertices of the graph with …

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

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 …

Fall 2019 Distinguished Speaker Series, Department of Computer and Information Science, University of Delaware, Massive-scale Analytics

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

Fall 2019 Colloquium, Department of Computer and Information Science, Temple University, Massive-scale Analytics

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

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