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Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report (2018)

Chapter: Appendix A: Biographies of the Committee

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Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
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A

Biographies of the Committee

LAURA HAAS, Co-Chair, joined the University of Massachusetts, Amherst, in August 2017 as dean of the College of Information and Computer Sciences, after a long career at IBM, where she was accorded the title IBM fellow in recognition of her impact. At the time of her retirement from IBM, she was director of IBM Research’s Accelerated Discovery Lab (2011–2017), after serving as director of computer science at IBM’s Almaden Research Center from 2005 to 2011. She had worldwide responsibility for IBM Research’s exploratory science program from 2009 through 2013. From 2001–2005, she led the Information Integration Solutions architecture and development teams in IBM’s Software Group. Previously, Dr. Haas was a research staff member and manager at Almaden. She is best known for her work on the Starburst query processor, from which DB2 LUW was developed, on Garlic, a system which allowed integration of heterogeneous data sources, and on Clio, the first semi-automatic tool for heterogeneous schema mapping. She has received several IBM awards for Outstanding Innovation and Technical Achievement, an IBM Corporate Award for information integration technology, the Anita Borg Institute Technical Leadership Award, and the Association for Computing Machinery (ACM) Special Interest Group on Management of Data Edgar F. Codd Innovation Award. Dr. Haas was vice president of the Very Large Data Bases Endowment board of trustees from 2004–2009 and served on the board of the Computing Research Association from 2007–2016 (vice chair 2009–2015). She currently serves on the Computer Science and Telecommunications Board of the National Academies of Sciences, Engineering, and Medicine (2013–2019). She is an ACM fellow, a member of the National Academy of Engineering (NAE), and a fellow of the American Academy of Arts and Sciences.

ALFRED O. HERO III, Co-Chair, is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He received the B.S. (summa cum laude) from Boston University (1980) and Ph.D. from Princeton University (1984), both in electrical engineering. His primary appointment is in the Department of Electrical Engineering and Computer Science, and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. In 2008 he was awarded the Digiteo Chaire d’Excellence, sponsored by Digiteo Research Park in Paris, located at the Ecole Superieure d’Electricite, Gif-sur-Yvette, France. He is an Institute of Electrical and Electronics Engineers (IEEE) fellow, and several of his research articles have received best paper awards. Dr. Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011). He received the IEEE Signal Processing Society Meritorious Service Award (1998) and the IEEE Third Millennium Medal (2000). He was president of the IEEE Signal Processing Society (2006–2008) and was on the board of directors of the IEEE (2009–2011) where he served as director of Division IX (Signals and Applications). Dr. Hero’s recent research interests have been in detection, classification, pattern analysis, and adaptive sampling for spatiotemporal data. Of particular interest are applications to network security, multimodal sensing and tracking, biomedical imaging, and genomic signal processing.

ANI ADHIKARI is a senior lecturer in statistics at the University of California, Berkeley, and she has received the Distinguished Teaching Award at Berkeley and the Dean’s Award for Distinguished

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

Teaching at Stanford University. While her research interests are centered on applications of statistics in the natural sciences, her primary focus has always been on teaching and mentoring students. She teaches courses at all levels and has a particular affinity for teaching statistics to students who have little mathematical preparation. She received her undergraduate degree from the Indian Statistical Institute and her Ph.D. in statistics from the University of California, Berkeley.

DAVID CULLER received his B.A. from the University of California, Berkeley in 1980, and an M.S. and Ph.D. from the Massachusetts Institute of Technology (MIT) in 1985 and 1989, respectively. He joined the electrical engineering and computer science (EECS) faculty in 1989, is the founding director of Intel Research, University of California, Berkeley, and was associate chair (2010–2012) and chair (2012–June 2014) of the EECS Department. He won the Okawa Prize in 2013. He is a member of the NAE, an ACM fellow, and an IEEE fellow. He has been named one of Scientific American’s Top 50 Researchers and the creator of one of MIT Technology Review’s “10 Technologies that Will Change the World.” He was awarded the National Science Foundation (NSF) Presidential Young Investigator and the Presidential Faculty Fellowship. His research addresses networks of small, embedded wireless devices; planetary-scale internet services; parallel computer architecture; parallel programming languages; and high-performance communication. It includes TinyOS, Berkeley Motes, PlanetLab, Networks of Workstations, Internet services, Active Messages, Split-C, and the Threaded Abstract Machine.

DAVID DONOHO is an Anne T. and Robert M. Bass Professor of Humanities and Sciences and professor of statistics at Stanford University. Dr. Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to the understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description. His theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. His applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems. He is a member of the National Academy of Sciences. Dr. Donoho received an A.B. from Princeton University and a Ph.D. from Harvard University.

E. THOMAS EWING is an associate dean for graduate studies, research, and diversity in the College of Liberal Arts and Human Sciences and a professor in the Department of History. His education included a B.A. from Williams College and a Ph.D. in history from the University of Michigan. He teaches courses in Russian, European, Middle Eastern, and world history; gender/women’s history; and historical methods. His publications include, as author, Separate Schools: Gender, Policy, and Practice in the Postwar Soviet Union (2010) and The Teachers of Stalinism. Policy, Practice, and Power in Soviet Schools in the 1930s (2002); as editor, Revolution and Pedagogy: Transnational Perspectives on the Social Foundations of Education (2005); and as co-editor, with David Hicks, Education and the Great Depression: Lessons from a Global History (2006). His articles on Stalinist education have been published in Gender & History, American Educational Research Journal, Women’s History Review, History of Education Quarterly, Russian Review, and The Journal of Women’s History. He has received funding from the National Endowment for the Humanities, the Spencer Foundation, and the National Council for Eurasian and East European Research.

LOUIS J. GROSS is a James R. Cox and Alvin and Sally Beaman Distinguished Professor of Ecology and Evolutionary Biology and Mathematics and director of the Institute for Environmental Modeling at the University of Tennessee, Knoxville (UTK). He is also director of the National Institute for Mathematical and Biological Synthesis, an NSF-funded center to foster research and education at the interface between math and biology. He completed a B.S. in mathematics at Drexel University and a Ph.D. in applied mathematics at Cornell University, and has been a faculty member at UTK since 1979. His research focuses on applications of mathematics and computational methods in many areas of

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

ecology, including disease ecology, landscape ecology, spatial control for natural resource management, photosynthetic dynamics, and the development of quantitative curricula for life science undergraduates. He led the effort at UT to develop an across-trophic level modeling framework to assess the biotic impacts of alternative water planning for the Everglades of Florida. He has co-directed several courses and workshops in mathematical ecology at the International Centre for Theoretical Physics in Trieste, Italy, and he has served as program chair of the Ecological Society of America, president of the Society for Mathematical Biology, president of the UTK Faculty Senate, and treasurer for the American Institute of Biological Sciences. He is the 2006 Distinguished Scientist awardee of the American Institute of Biological Sciences and is a fellow of the American Association for the Advancement of Science and of the Society for Mathematical Biology. He has served as chair of the National Academies Committee on Education in Biocomplexity Research, as a member of the Board on Life Sciences, and as a liaison to the Standing Committee on Emerging Science for Environmental Health Decisions.

NICHOLAS HORTON is a professor of statistics at Amherst College. As an applied biostatistician, Dr. Horton’s work is based squarely within the mathematical sciences but spans other fields in order to ensure that biomedical research is conducted on a sound footing. He has published more than 160 papers in the statistics and biomedical literature and 4 books on statistical computing and data science. He has taught a variety of courses in statistics and related fields, including introductory statistics, data science, probability, theoretical statistics, regression, and design of experiments. He is passionate about improving quantitative and computational literacy for students with a variety of backgrounds as well as engagement and mastery of higher-level concepts and capacities to think with data. Dr. Horton received the American Statistical Association (ASA) Waller Award for Distinguished Teaching, the Mathematical Association of America Hogg Award for Excellence in Teaching, the Mu Sigma Rho Statistics Education Award, and the ASA Founders Award. He was a co-principal investigator of the NSF-funded Project MOSAIC, serves as the chair of the Committee of Presidents of Statistical Societies, is a fellow of the ASA, and was a research fellow at the Bureau of Labor Statistics. Dr. Horton earned his A.B. from Harvard College and his Sc.D. in biostatistics from the Harvard T.H. Chan School of Public Health.

JULIA LANE is a professor at the Center for Urban Science and Progress (CUSP), and at New York University’s (NYU’s) Wagner Graduate School of Public Service. She also serves as a provostial fellow for innovation analytics and senior fellow at NYU’s GovLab. As part of the CUSP team, Dr. Lane works with the research team to build the CUSP Data User Facility. Dr. Lane is an economist who is the co-founder of the Longitudinal Employer-Household Dynamic (LEHD) partnership with the Census Bureau. LEHD data has been used to analyze commuting patterns for transportation planning, and the study of workforce turnover, pensions, and low-wage work. Dr. Lane has authored over 65 refereed articles and edited or authored 7 books. She has been working with a number of national governments to document the results of their science investments. Her work has been featured in several publications, including Science and Nature. Work Dr. Lane started at the NSF (as senior program director of the Science of Science and Innovation Policy Program) to quantify the results of federal stimulus spending is the basis of the new Institute for Research on Innovation and Science at the University of Michigan. The data will be used to describe the structure of the research workforce, the nature and evolution of research collaborations, and the diffusion of sponsored research results. Dr. Lane has had leadership positions in a number of policy and data science initiatives at her other previous appointments, which include senior managing economist at the American Institutes for Research; senior vice president and director, Economics Department at NORC/University of Chicago; various consultancy roles at the World Bank; and assistant, associate, and full professor at American University. Dr. Lane received her Ph.D. in economics and master’s in statistics from the University of Missouri.

ANDREW MCCALLUM is a professor and director of the Center for Data Science, as well as the Information Extraction and Synthesis Laboratory, in the College of Information and Computer Science at the University of Massachusetts, Amherst. He has published over 250 papers in many areas of artificial

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

intelligence, including natural language processing, machine learning, and reinforcement learning; his work has received over 45,000 citations. He obtained his Ph.D. from the University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from Carnegie Mellon University (CMU) with Tom Mitchell and Sebastian Thrun. In the early 2000s he was vice president of research and development at WhizBang Labs, a 170-person start-up company that used machine learning for information extraction from the Web. He is an Association for the Advancement of Artificial Intelligence fellow, the recipient of the UMass Chancellor’s Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM, Microsoft, and Yahoo. He was the general chair for the International Conference on Machine Learning 2012 and is the current president of the International Machine Learning Society, as well as member of the editorial board of the Journal of Machine Learning Research. For the past 10 years, Dr. McCallum has been active in research on statistical machine learning applied to text, especially information extraction, entity resolution, social network analysis, structured prediction, semi-supervised learning, and deep neural networks for knowledge representation.

RICHARD MCCULLOUGH has a B.S. in chemistry from the University of Texas, Dallas, and earned his M.A. and Ph.D. degrees in chemistry at Johns Hopkins University. He did his postdoctoral fellowship at Columbia University. Since 2012, Dr. McCullough has been the vice provost for research, working with the president and provost to encourage, cultivate, and coordinate high-impact academic research across all of Harvard University’s schools and affiliated institutions. The Office of the Vice Provost for Research (VPR) has broad responsibility and oversight for the development, review, and implementation of strategies, planning, and policies related to the organization and execution of academic research across the entire university. Dr. McCullough leads a new office of Foundation and Corporate Development. He also assists in oversight of many of the interdisciplinary institutes, centers, and initiatives across Harvard. Under Vice Provost McCullough’s leadership, the Office of the VPR is particularly focused on removing barriers to collaboration, whether in university policies or financial or administrative systems. Additionally, the vice provost for research works with the president and provost to foster and encourage entrepreneurship among undergraduates, graduate students, and faculty members. He also helps to lead the development of the new innovation campus. Dr. McCullough is also a professor of materials science and engineering at Harvard and is a member of numerous professional societies and boards. Prior to being named vice provost for research at Harvard, he was the vice president for research at CMU, where he previously served as the dean of the Mellon College of Science and professor and head of the Department of Chemistry. Dr. McCullough has founded two companies: Plextronics, Inc., and Liquid X Printed Metals.

REBECCA NUGENT is a teaching professor in the Department of Statistics at CMU and has been teaching at CMU since she completed her Ph.D. in statistics from University of Washington in 2006. Prior to that, she received her B.A with majors in mathematics, statistics, and Spanish at Rice University and her M.S. in statistics at Stanford University. She recently was awarded top teaching honors with the ASA Waller Education Award; The William H. and Frances S. Ryan Award for Meritorious Teaching; and Statistician of the Year by the ASA Pittsburgh Chapter. Nugent’s research interests lie in clustering, record linkage, educational data mining/psychometrics, public health, tech/innovation/entrepreneurship, and semantic organization.

LEE RAINIE is the director of internet, science, and technology research at Pew Research Center. Under his leadership, the center has issued more than 500 reports based on its surveys that examine people’s online activities and the Internet’s role in their lives. He also directs the center’s new initiative on the intersection of science and society. The American Sociological Association gave Dr. Rainie its award for “excellence in the reporting on social issues” in 2014 and described his work as the “most authoritative source of reliable data on the use and impact of the internet and mobile connectivity.” He is a co-author of Networked: The New Social Operating System and five books about the future of the Internet that are

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

drawn from the center’s research. He gives several dozen speeches a year to government officials, media leaders, scholars and students, technology executives, librarians, and nonprofit groups about the changing media ecosystem. He is also regularly interviewed by major news organizations about technology trends. Prior to launching Pew Research Center’s technology research, Dr. Rainie was managing editor of U.S. News & World Report. He is a graduate of Harvard University and has a master’s in political science from Long Island University.

ROB RUTENBAR received his Ph.D. from the University of Michigan in 1984 and then joined the faculty at CMU. He spent 25 years in electrical and computer engineering at CMU, ultimately holding the Stephen J. Jatras (E’47) Chair. He was the founding director of the Center for Circuit and System Solutions (called “C2S2”), a large consortium of U.S. schools supported by the Defense Advanced Research Projects Agency and the U.S. semiconductor industry, focused on design problems at the end of Moore’s Law scaling. In 2010, he moved to the University of Illinois, Urbana-Champaign, where he is Abel Bliss Professor and head of Computer Science. At Illinois, he pioneered the novel “CS + X” program, which combines a core computer science curriculum with a disciplinary “X” curriculum, leading to a Bachelor’s degree in “X”; student pipelines for CS + anthropology, astronomy, chemistry, linguistics, are now under way, with several more CS + X degrees under design. His research has focused in three broad areas: tools for integrated circuit design, statistics of nanoscale chip designs, and custom architectures for machine learning and perception. In 1998, he founded Neolinear, Inc., to commercialize the first practical synthesis tools for non-digital ICs, and served as Neolinear’s chief scientist until its acquisition by Cadence in 2004. In 2006, he founded Voci Technologies, Inc., to commercialize enterprise-scale voice analytics. He has won numerous awards, including the IEEE CASS Industrial Pioneer Award and the Semiconductor Research Corporation Aristotle Award. His work has been featured in venues ranging from Slashdot to the Economist magazine.

KRISTIN M. TOLLE is the director of the Data Science Initiative in Microsoft Research Outreach, Redmond, Washington. Since joining Microsoft in 2000, Dr. Tolle has acquired numerous patents and worked for several product teams including the Natural Language Group, Visual Studio, and the Microsoft Office Excel Team. Since joining Microsoft Research’s outreach program in 2006, she has run several major initiatives from biomedical computing and environmental science to more traditional computer and information science programs around natural user interactions and data curation. She also directed the development of the Microsoft Translator Hub and the Environmental Science Services Toolkit. Dr. Tolle is an editor, along with Tony Hey and Stewart Tansley, of one of the earliest books on data science, The Fourth Paradigm: Data Intensive Scientific Discovery. Her current focus is developing an outreach program to engage with academics on data science in general and more specifically around using data to create meaningful and useful user experiences across devices platforms. Prior to joining Microsoft, Dr. Tolle was an Oak Ridge Science and Engineering Research Fellow for the National Library of Medicine and a research associate at the University of Arizona Artificial Intelligence Lab managing the group on medical information retrieval and natural language processing. She earned her Ph.D. in management of information systems with a minor in computational linguistics. Dr. Tolle’s present research interests include global public health as related to climate change, mobile computing to enable field scientists and inform the public, sensors used to gather ecological and environmental data, and integration and interoperability of large heterogeneous environmental data sources. She collaborates with several major research groups in Microsoft Research including eScience, computational science laboratory, computational ecology and environmental science, and the sensing and energy research group.

TALITHIA WILLIAMS takes sophisticated numerical concepts and makes them understandable and relatable to everyone. As illustrated in her popular TedTalk “Own Your Body’s Data,” she demystifies the mathematical process in amusing and insightful ways, using statistics as a way of seeing the world in a new light and transforming our future through the bold new possibilities inherent in the science, technology, engineering, and mathematics (STEM) fields. As an associate professor of mathematics at

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

Harvey Mudd College, she has made it her life’s work to get people—students, parents, educators, and community members—more excited about the possibilities inherent in a STEM education. In her present capacity as a faculty member, she exemplifies the role of teacher and scholar through outstanding research, with a passion for integrating and motivating the educational process with real-world statistical applications. Her educational background includes a bachelor’s degree in mathematics from Spelman College, masters’ degrees in both mathematics from Howard University and statistics from Rice University, and a Ph.D. in statistics from Rice University. Her professional experiences include research appointments at the Jet Propulsion Laboratory, the National Security Agency, and NASA. Dr. Williams develops statistical models which emphasize the spatial and temporal structure of data and has partnered with the World Health Organization in developing a cataract model used to predict the cataract surgical rate for countries in Africa. Through her research and work in the community at large, she is helping change the collective mindset regarding STEM in general and math in particular—rebranding the field of mathematics as anything but dry, technical, or male-dominated but instead a logical, productive career path that is crucial to the future of the country.

ANDREW ZIEFFLER is a senior lecturer and researcher in the Quantitative Methods in Education program within the Department of Educational Psychology at the University of Minnesota. He teaches undergraduate- and graduate-level courses in statistics and trains and supervises graduate students in statistics education. Prior to receiving his Ph.D., Dr. Zieffler taught mathematics and A.P. Statistics at ROCORI High School in Cold Spring, Minnesota. His scholarship primarily focuses on statistics education. Dr. Zieffler has authored/co-authored several papers and book chapters related to statistics education and has been a co-principal investigator on many NSF-funded statistics education research projects. Additionally, he has co-authored two textbooks that serve as an introduction to modern statistical and computational methods for students in the educational and behavioral sciences. Dr. Zieffler currently serves as co-editor of the journal Technology Innovations in Statistics Education and as a member of the Research Advisory Board for the Consortium for the Advancement of Undergraduate Statistics Education.

Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 43
Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 44
Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 45
Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 46
Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 47
Suggested Citation:"Appendix A: Biographies of the Committee." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 48
Next: Appendix B: Meetings and Presentations »
Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report Get This Book
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The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation’s ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses.

The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction.

This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

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