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Training Students to Extract Value from Big Data: Summary of a Workshop (2015)

Chapter: Appendix A: Registered Workshop Participants

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Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
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A

Registered Workshop Participants

Agarwal, Deepak – LinkedIn Corporation

Albrecht, Jochen – Hunter College, City University of New York (CUNY)

Asabi, Faisal – Student / No affiliation known

Bailer, John – Miami University

Begg, Melissa – Columbia University

Bloom, Jane – International Catholic Migration Commission

Bloom, Joshua – University of California, Berkeley

Brachman, Ron – Yahoo Labs

Bradley, Shenae – National Research Council (NRC)

Bruce, Peter – Statistics, Inc.

Buechler, Steven – University of Notre Dame

Caffo, Brian – Johns Hopkins University

Christman, Zachary – Rowan University

Cleveland, Bill – Purdue University

Costello, Donald – University of Nebraska

Curry, James – National Science Foundation

Dell, Robert – Naval Postgraduate School

Dent, Gelonia –Medgar Evers College, CUNY

Desaraju, Kruthika – George Washington University

Dobbins, Janet – Statistics, Inc.

Donovan, Nancy – Government Accountability Office

Dozier, Jeff – University of California, Santa Barbara

Dreves, Harrison – NRC

Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×

Dutcher, Jennifer – University of California, Berkeley

Eisenberg, Jon – NRC

Eisner, Ken – Amazon Corporation

Fattah, Hind – Chipotle

Feng, Tingting – University of Virginia

Fox, Peter – Rensselaer Polytechnic Institute

Freire, Juliana – New York University

Freiser, Joel – John Jay College of Criminal Justice

Frew, James – University of California, Santa Barbara

Fricker, Ron – Naval Postgraduate School

Gatsonis, Constantine – Brown University

Ghani, Rayid – University of Chicago

Ghosh, Sujit – National Science Foundation

Glassman, Neal – NRC

Gray, Alexander – Skytree Corporation

Haque, Ubydul – Johns Hopkins University

Howard, Rodney – NRC

Howe, William – University of Washington

Hughes, Gary – Statistics, Inc.

Huo, Xiaoming – Georgia Tech, National Science Foundation

Iacono, Suzanne – National Science Foundation

Kafadar, Karen – Indiana University

Kass, Robert – Carnegie Mellon University

Khaloua, Asmaa – Macy

Kong, Jeongbae – Enanum, Inc.

Lafferty, John – University of Chicago

Lesser, Virginia – Oregon State University

Lebanon, Guy – Amazon Corporation

Levermore, David – University of Maryland

Liu, Shiyong – Southwestern University of Finance and Economics

Mandl, Kenneth – Harvard Medical School Boston Children’s Hospital

Marcus, Stephen – National Institute of General Medical Sciences, National Institutes of Health (NIH)

Martinez, Waldyn – Miami University

Mellody, Maureen – NRC

Neerchal, Nagaraj – University of Maryland, Baltimore County

Orwig, Jessica – American Physical Society

Pack, Quinn – Mayo Clinic

Parmigiani, Giovanni – Dana Farber Cancer Institute

Pearl, Jennifer – National Science Foundation

Pearsall, Hamil – Temple University

Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×

Perlich, Claudia – Dstillery

Rai, Saatvika – University of Kansas

Ralston, Bruce – University of Tennessee

Ramakrishnan, Raghu – Microsoft Corporation

Ranakrishan, Raghunath – University of Texas, Austin

Ravichandran, Veerasamy – NIH

Ré, Christopher – Stanford University

Ryland, Mark – Amazon Corporation

Schwalbe, Michelle – NRC

Schou, Sue – Idaho State University

Shams, Khawaja – Amazon Corporation

Sharman, Raj –University at Buffalo, State University of New York (SUNY)

Shekhar, Shashi – University of Minnesota

Shipp, Stephanie – VA Bioinformatics Institute at Virginia Tech University

Shneiderman, Ben – University of Maryland

Spencer Huang, ChiangChing – University of Wisconsin, Milwaukee

Spengler, Sylvia – National Science Foundation

Srinivasarao, Geetha – Information Technology Specialist, Department of Health and Human Services

Szewczyk, Bill – National Security Agency

Tannouri, Ahlam – Morgan State University

Tannouri, Charles – Department of Homeland Security

Tannouri, Sam – Morgan State University

Temple Lang, Duncan – University of California, Davis

Torrens, Paul – University of Maryland, College Park

Ullman, Jeffrey – Stanford University

Vargas, Juan – Georgia Southern University

Wachowicz, Monica – University of New Brunswick, Fredericton

Wang, Rong – Illinois Institute of Technology

Wang, Youfa – University at Buffalo, SUNY

Wee, Brian – National Ecological Observatory Network (NEON), Inc.

Weese, Maria – MIA

Weidman, Scott – NRC

Weiner, Angelica – Amazon Corporation

Wynn, Sarah – NRC Christine Mirzayan Science and Technology Policy Graduate Fellow

Xiao, Ningchuan – Ohio State University

Xue, Hong – University at Buffalo, SUNY

Yang, Ruixin – George Mason University

Zhang, Guoping – Morgan State University

Zhao, Fen – National Science Foundation

Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×
Page 49
Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×
Page 50
Suggested Citation:"Appendix A: Registered Workshop Participants." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×
Page 51
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As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human's ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.

The nation's ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.

Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council's Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula.

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