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Suggested Citation:"Appendix B: Committee Biosketches." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Research Workflows For Accelerated Discovery: Closing the Knowledge Discovery Loop. Washington, DC: The National Academies Press. doi: 10.17226/26532.
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Suggested Citation:"Appendix B: Committee Biosketches." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Research Workflows For Accelerated Discovery: Closing the Knowledge Discovery Loop. Washington, DC: The National Academies Press. doi: 10.17226/26532.
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Suggested Citation:"Appendix B: Committee Biosketches." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Research Workflows For Accelerated Discovery: Closing the Knowledge Discovery Loop. Washington, DC: The National Academies Press. doi: 10.17226/26532.
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Suggested Citation:"Appendix B: Committee Biosketches." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Research Workflows For Accelerated Discovery: Closing the Knowledge Discovery Loop. Washington, DC: The National Academies Press. doi: 10.17226/26532.
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Suggested Citation:"Appendix B: Committee Biosketches." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Research Workflows For Accelerated Discovery: Closing the Knowledge Discovery Loop. Washington, DC: The National Academies Press. doi: 10.17226/26532.
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Appendix B Committee Biosketches DANIEL E. ATKINS (Chair) (NAE) is Emeritus W.K. Kellogg Professor of Information and Professor of Electrical Engineering and Computer Science at the University of Michigan (UM), Ann Arbor. The first phase of his career focused on computer architecture including high-speed arithmetic methods now widely used in modern computers, as well as the design and construction of application-specific experimental computers. The second phase of his career focused on pioneering interdisciplinary research on cyber-enabled distributed knowledge communities including collaboratories and digital libraries applied to both scientific research and education. He has served as dean of the College of Engineering, founding dean of the School of Information, and associate vice president for research at UM, as well as the inaugural director of the Office of Cyberinfrastructure at the National Science Foundation (NSF). He chaired the Blue Ribbon Panel on Research Cyberinfrastructure for the NSF that became an international roadmap for initiatives on cyber-enabled research in the digital age. He has chaired or served on many advisory boards for government, academia, philanthropy, and industry. Professor Atkins is a member of the National Academy of Engineering and Fellow of the AAAS. He earned a Ph.D. in computer science and an M.S. in electrical engineering from the University of Illinois, Urbana- Champaign, and a B.S.E.E. from Bucknell University. ILKAY ALTINTAS is a research scientist at the University of California San Diego, the chief data science officer of the San Diego Supercomputer Center (SDSC), and a founding Fellow of the Halıcıoğlu Data Science Institute. She is the founding director of the Workflows for Data Science (WorDS) Center of Excellence and the WIFIRE Lab. The WoRDS Center specializes in the development of methods, cyberinfrastructure, and workflows for computational data science and its translation to practical applications. The WIFIRE Lab is focused on artificial intelligence methods for an all-hazards knowledge cyberinfrastructure, becoming a management layer from the data collection to modeling efforts, and has achieved significant success in helping to manage wildfires. Since joining SDSC in 2001, she has been a principal investigator and a technical 173 PREPUBLICATION COPY—Uncorrected Proofs

leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible. Her work has been applied to many scientific and societal domains including bioinformatics, geoinformatics, high-energy physics, multiscale biomedical science, smart cities, and smart manufacturing. She is also a popular MOOC instructor in the field of “big” data science and has reached out to more than a million learners across any populated continent. Among the awards she has received are the 2015 IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers and the 2017 ACM SIGHPC Emerging Woman Leader in Technical Computing Award. Dr. Altintas received her Ph.D. degree from the University of Amsterdam in the Netherlands, with an emphasis on provenance of workflow-driven collaborative science. SHREYAS CHOLIA is group leader for the Usable Software Systems Group in the Data Science and Technology department at Lawrence Berkeley National Laboratory (LBNL), focused on usability aspects of computational and data analysis systems. He is particularly interested in how web interfaces and tools can facilitate large-scale scientific computing workflows. He is currently working on various projects that integrate Jupyter Notebooks with distributed and high-performance scientific computing environments. He joined LBNL’s Computational Research Division in 2015, having worked for over a decade at the National Energy Research Scientific Computing Center, where he led the science-gateway, web, and grid efforts. Prior to his appointment at LBNL, he was a developer and consultant at IBM. He has a B.A. in computer science and cognitive sciences from Rice University. MERCÈ CROSAS is the secretary of Open Government for the government of Catalunya. Prior to her current position, Dr. Crosas was the university research data management officer, with Harvard University Information Technology, and chief data science and technology officer at Harvard’s Institute for Quantitative Social Science. In the last 10 years, Dr. Crosas has been principal investigator (PI) and co-PI of multiple research grants and collaborations related to data privacy, data provenance, research reproducibility, and data sharing in social science, biomedicine, and astronomy. She is part of numerous committees and working groups focused 174 PREPUBLICATION COPY—Uncorrected Proofs

on research data management, data citation, and data standards, and is a co-author of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles as well as the Joint Declaration of Data Citation Principles. Before rejoining Harvard in 2004, Dr. Crosas worked for 6 years in the educational software and biotech industries, initially as a software developer, and subsequently as director of the software development team. She contributed to the development of lab information management systems for Single nucleotide polymorphism discovery and genotyping and mass spectrometry. Before that, she spent 6 years at the Harvard-Smithsonian Center for Astrophysics, first as a predoctoral fellow for her Ph.D. in astrophysics from Rice University, and later as a postdoctoral fellow, researcher, and software engineer with the Radioastronomy division. She earned a B.S. in physics from the Universitat de Barcelona, Spain. ALFRED HERO is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. His research is on data science and developing theory and algorithms for data collection, analysis, and visualization that use statistical machine learning and distributed optimization. These are being applied to network data analysis, personalized health, multimodality information fusion, data-driven physical simulation, materials science, dynamic social media, and database indexing and retrieval. Dr. Hero has held visiting positions at Massachusetts Institute of Technology, Boston University, Lucent Bell Laboratories (Murray Hill), and Ford Motor Company in addition to the University of Nice, the École Normale Supérieure de Lyon, and Telecom-ParisTech in France. Dr. Hero was president of the Institute of Electrical and Electronics Engineers’ (IEEE’s) Signal Processing Society (2006– 2008) and was on the Board of Directors of IEEE (2009–2011) where he served as director of Division IX (Signals and Applications). He is also a member of the Big Data Special Interest Group of the IEEE Signal Processing Society. Dr. Hero received a B.S. (summa cum laude) from Boston University (1980) and a Ph.D. from Princeton University (1984), both in electrical engineering. REBECCA LAWRENCE is managing director of F1000 Group. She was responsible for the launch of the open research publishing platform F1000Research in January 2013, and has 175 PREPUBLICATION COPY—Uncorrected Proofs

subsequently led the initiative behind the recent launch of Wellcome Open Research, Gates Open Research, and many other funder- and institution-based publishing platforms. She is a member of the High-Level Advisory Group for the European Commission’s Open Science Policy Platform (OSPP), chairing their work on next-generation indicators and their integrated advice: OSPP- REC. She has been a co-chair of a number of working groups focusing on data and peer review, for organizations including the Research Data Alliance and ORCID. She is also an Advisory Board member for the data policy and standards initiative, FAIRsharing, and for DORA (the San Francisco Declaration on Research Assessment). She has worked in Scientific, technical, and medical (STM) publishing for almost 20 years for several publishers including Elsevier where she built and ran the Drug Discovery Group. She originally trained and qualified as a pharmacist, and holds a Ph.D. in cardiovascular pharmacology from University of Nottingham. BRADLEY A. MALIN (NAM) is the vice chair for research and professor of biomedical informatics at Vanderbilt University. He is also a professor of biostatistics, a professor of computer science, and is affiliated faculty in the Center for Biomedical Ethics and Society. He co-directs the Health Data Science Center, the Center for Genetic Privacy and Identity in Community Settings—a National Institutes of Health Center of Excellence in Ethical, Legal, and Social Implications Research, and the Big Biomedical Data Science Ph.D. program. He is also the director of the Health Information Privacy Laboratory, which was established to address the growing need for data privacy research and development for the health information technology sector. Dr. Malin’s research is in big health data analytics and the infrastructure necessary to support such investigations. He has made specific contributions to a number of health-related areas, including distributed data processing methods for medical record linkage and predictive modeling, intelligent auditing technologies to protect electronic medical records from misuse in the context of primary care, and algorithms to formally anonymize patient information disseminated for secondary research purposes. He is an elected fellow of the National Academy of Medicine and American College of Medical Informatics and was honored as a recipient of the Presidential Early Career Award for Scientists and Engineers. Dr. Malin completed his education at Carnegie Mellon University, where he received a bachelor's degree in biological sciences, a 176 PREPUBLICATION COPY—Uncorrected Proofs

master's in machine learning, a master's in public policy and management, and a doctorate in computer science (with a focus on databases and software systems). LARA MANGRAVITE is president of Sage Bionetworks. This organization is focused on the development and implementation of practices for large-scale collaborative biomedical research. Sage Bionetworks’ work is centered on new approaches to scientific process that use open systems to enable community-based research regarding complex biomedical problems. Previously, Dr. Mangravite served as director of the Systems Biology research group at Sage Bionetworks where she focused on the application of collaborative approaches to advance understanding of disease biology and treatment outcomes at a systems level with the overriding goal of improving clinical care. Dr. Mangravite obtained a B.S. in physics from Pennsylvania State University and a Ph.D. in pharmaceutical chemistry from the University of California, San Francisco. She completed a postdoctoral fellowship in cardiovascular pharmacogenomics at the Children’s Hospital Oakland Research Institute. TAPIO SCHNEIDER is Theodore Y. Wu Professor of Environmental Science and Engineering at Caltech and Senior Research Scientist at the Jet Propulsion Laboratory. His research is focused on understanding atmosphere dynamics on Earth and other planets; turbulence in atmosphere and oceans; and climate change and climate modeling. Previously, Dr. Schneider served as professor of climate dynamics at Swiss Federal Institute of Technology Zurich from 2013–2016, and associate research scientist at New York University’s Courant Institute of Mathematical Sciences from 2000 to 2002. Dr. Schneider received his M.Sc. (1997) and Ph.D. (2001) in atmospheric and oceanic sciences from Princeton University. He was a visiting graduate student (Physics) at the University of Washington, Seattle from 1994 to 1995, and studied mathematics and physics (Vordiplom 1993) at Albert-Ludwigs-Universität Freiburg, Germany. 177 PREPUBLICATION COPY—Uncorrected Proofs

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The needs and demands placed on science to address a range of urgent problems are growing. The world is faced with complex, interrelated challenges in which the way forward lies hidden or dispersed across disciplines and organizations. For centuries, scientific research has progressed through iteration of a workflow built on experimentation or observation and analysis of the resulting data. While computers and automation technologies have played a central role in research workflows for decades to acquire, process, and analyze data, these same computing and automation technologies can now also control the acquisition of data, for example, through the design of new experiments or decision making about new observations.

The term automated research workflow (ARW) describes scientific research processes that are emerging across a variety of disciplines and fields. ARWs integrate computation, laboratory automation, and tools from artificial intelligence in the performance of tasks that make up the research process, such as designing experiments, observations, and simulations; collecting and analyzing data; and learning from the results to inform further experiments, observations, and simulations. The common goal of researchers implementing ARWs is to accelerate scientific knowledge generation, potentially by orders of magnitude, while achieving greater control and reproducibility in the scientific process.

Automated Research Workflows for Accelerated Discovery: Closing the Knowledge Discovery Loop examines current efforts to develop advanced and automated workflows to accelerate research progress, including wider use of artificial intelligence. This report identifies research needs and priorities in the use of advanced and automated workflows for scientific research. Automated Research Workflows for Accelerated Discovery is intended to create awareness, momentum, and synergies to realize the potential of ARWs in scholarly discovery.

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