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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). 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

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

 
 
 
 
 

Steering Committee Chair, Seed Fund

Northeat Big Data Innovation Hub

2020 – Present New York, NY
 
 
 
 
 

Advisory Board Member

OpenCilk

2020 – Present Cambridge, MA
 
 
 
 
 

Strategic Advisory Board Member

Open Source Election Technology Institute

2019 – Present Palo Alto, CA
 
 
 
 
 

Advisory Board Member

Trovares

2019 – 2020 Seattle, WA
 
 
 
 
 

Advisory Council Member

Electrical and Computer Engineering Department, Lehigh University

2018 – Present Bethlehem, PA
 
 
 
 
 

Advisory Board Member

Accelogic, LLC

2015 – 2019 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 – 2019 Frederick, MD

Recent Posts

Leadership Updates: Steering Committee and Seed Fund Steering Committee

Dear Northeast Hub Community, As new program activities launch under our second phase of NSF funding, we are delighted to welcome new community members to our leadership team who will help guide the Hub forward, and to thank those whose service has helped us reach this point. Following a search made by our project team last month, we are pleased to welcome John Goodhue of the Massachusetts Green High Performance Computing Center, Carsten Eickhoff of Brown University, and Laura Dietz of the University of New Hampshire to our Steering Committee.

Snapping Foggy Narratives Into Focus

David Bader, director of NJIT’s Institute for Data Science, works on computing initiatives that will help people make sense of large, diverse and evolving streams of data from news reports, distributed sensors and lab test equipment, among other sources connected to worldwide networks. When a patient arrives in an emergency room with high fever, coughing and shivering, the speed of diagnosis and treatment depends on the skills of the medical staff, but also on information.

Shawn Cicoria, M.S. in Data Science, NJIT@JerseyCity

Shawn Cicoria, Principle Software Engineer Manager, Microsoft, discusses the M.S. in Data Science program at NJIT@JerseyCity, highlighting Prof. David A. Bader. https://www.youtube.com/watch?v=xQt5GSgwk8k

NJIT Experts Presenting AI Answers to Real-World Problems at NYC Forum

Experts in artificial intelligence from the Ying Wu College of Computing will highlight how their work solves real-world problems at a prestigious meeting in New York next week. The professors — Chaoran Cheng, Jing Li, Zhi Wei and Pan Xu — will share their session stages with researchers from IBM, Facebook, Yahoo and other prominent organizations for audiences at the 34th annual conference of the Association for the Advancement of Artificial Intelligence.

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.

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

MIT 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 …

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 …

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