AWS Education: Research Seminar Series
The Amazon Web Services (AWS) Research Seminar Series is a set of informational sessions focused on how research spurs scientific and technological evolutions. This series aims to connect worldwide researchers with the internal Amazon research communities to exchange ideas, share information, and discuss topics including artificial intelligence, blockchain for science, high performance computing, quantum computation, machine learning, and robotics.
Each session is approximately 60 minutes (50 minutes of presentation and 10 minutes of Q&A). Contact us at aws-wwps-seminar@amazon.com if you are interested in presenting or have a question about the series or about a specific presentation.
Next webinar - Accelerating Time to Research Using CloudBank
Date: October 16, 2020
Time: 12:00 PM – 1:00 PM EST
Venue: Live broadcast
Presenters
Dr. Deepankar Medhi, National Science Foundation (NSF)
Dr. Michael L Norman, San Diego Supercomputer Center and University of California, San Diego
Dr. Sanjay Padhi, Amazon Web Services (AWS)
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Upcoming Sessions
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Presenters
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Past Sessions
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Upcoming Sessions
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Title: Accelerating Time to Research Using CloudBank
Presenters:
Dr. Deepankar Medhi, National Science Foundation (NSF)
Dr. Michael L Norman, San Diego Supercomputer Center and University of California, San Diego
Dr. Sanjay Padhi, Amazon Web Services (AWS)
Date: October 16, 2020
Time: 12:00 PM – 1:00 PM EST
Abstract:
Funded by the National Science Foundation (NSF), CloudBank aims to simplify the use of the cloud across computer science research and education. Using CloudBank, researchers can access the latest processing systems, such as CPUs, GPUs, FPGAs, and quantum computing. They can also securely share data among researchers and research institutions via data lakes and open data programs. Researchers will be able to use a huge array of managed services and algorithms associated with data analytics, artificial intelligence, and machine learning to accelerate time to science. Join this talk to learn about the services CloudBank provides, how to request cloud resources for eligible NSF solicitations, and details of data management tools associated with full data lifecycle. Hear examples of supplementary documents including how to calculate anticipated annual and total costs for accessing the desired cloud computing resources via a simple monthly calculator. Learn about examples of studies using cloud services in genomics, astronomy, and physics that are causing a paradigm shift in computing.
Presenter Biographies:
Dr. Deepankar Medhi is a program director in the computer and network systems (CNS) division at the National Science Foundation (NSF). He manages wired networking research in the Networking Technologies and Systems (NeTS) program in CNS as well as several infrastructure programs like NSF Future Cloud, Mid-Scale Research Infrastructure, and Cloud Access. He is also a distinguished professor in the department of computer science and electrical engineering at the University of Missouri-Kansas City (UMKC). He received his PhD in computer sciences from the University of Wisconsin-Madison. Prior to joining UMKC in 1989, he was a member of the technical staff at AT&T Bell Laboratories where he worked on teletraffic network routing and design. While there, he co-developed facility diverse routing, a feature deployed in AT&T's nationwide dynamic routing network. He co-authored the books, Routing, Flow, and Capacity Design in Communication and Computer Networks (2004) and Network Routing: Algorithms, Protocols, and Architectures (1st edition, 2007; 2nd edition, 2017), both published by Morgan Kauffman/Elsevier. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Dr. Michael L. Norman is the director of the San Diego Supercomputer Center and distinguished professor of physics at UC San Diego where he also directs the Laboratory for Computational Astrophysics. He received his BS from Caltech in 1975 and his PhD from UC Davis in 1980. After holding appointments at the Lawrence Livermore and Los Alamos National Laboratories, the Max Planck Institute for Astrophysics, and the National Center for Supercomputing Applications, he joined the faculty at UC San Diego in 2000. His research focus is the computer simulation of astronomical phenomena using supercomputers and the development of the numerical methods to carry them out. He is the author of over 200 papers on diverse topics including star formation, cosmic jets, and cosmological evolution. His computer visualizations have appeared in numerous educational TV shows and films, including PBS Nova and the Discovery Channel. He is the recipient of the Alexander von Humboldt Research Prize and the IEEE Sidney Fernbach Award. He was elected Fellow of the American Physical Society in 2001 and the American Academy of Arts and Sciences in 2005.
Dr. Sanjay Padhi leads research initiatives for Amazon Web Services (AWS) including various federal research programs such as artificial intelligence (AI), machine learning (ML), big data, and quantum computing in collaboration with the National Science Foundation, the National Institutes of Health, and other government and international institutions. Previously, he worked as a physicist for 15+ years and led various groups with hundreds of members at CERN, Brookhaven Lab, and Deutsches Elektronen- Synchrotron (DESY) in physics, simulations, and distributed computing. He managed the core team that designed, built, and operated a global scale distributed computing platform to analyze data from the LHC, currently used by the CMS Collaboration for all its computing activities with +4,000 particle physicists, engineers, and computer scientists from +200 institutes from +40 countries. He also co-founded the Computing Grid project at DESY before joining CERN. Dr. Padhi obtained his PhD from McGill University in high-energy physics, and he is an adjunct associate professor of physics at Brown University.
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Presenters
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Ariel Rokem; PhD; Research Assistant Professor, University of Washington
Ariel Rokem is a research assistant professor at the University of Washington Department of Psychology. He received a PhD in neuroscience from University of California, Berkeley (2010) and additional postdoctoral training in computational neuroimaging at Stanford University (2011-2015). He was also a senior data scientist at the University of Washington eScience Institute (2015-2020). He leads a research program in neuroinformatics: the development of data science tools, techniques and methods and their application to the analysis of neural data. He is a member of the software and data carpentry communities, where he has been an instructor since 2013 and an instructor trainer since 2015. He also directs the annual Summer Institute for Neuroimaging and Data Science. A contributor to multiple open-source software projects in the scientific Python ecosystem, he is a member of the editorial board of the Journal for Open Source Software (JOSS).
Susan K. Gregurick; PhD; Associate Director, Data Science; Director, Office of Data Science Strategy (ODSS) at the National Institutes of Health (NIH)
Susan K. Gregurick, PhD, is the associate director for Data Science and director of the Office of Data Science Strategy (ODSS) at the National Institutes of Health. Under Dr. Gregurick’s leadership, the ODSS leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that comprise the NIH. Dr. Gregurick was instrumental in the creation of the ODSS in 2018 and served as a senior advisor to the office until being named to her current position. Dr. Gregurick received her undergraduate degree in chemistry and mathematics from the University of Michigan and her PhD in physical chemistry from the University of Maryland.
Ankit Malhotra; PhD; Senior Technical Business Development Manager, AWS
Ankit Malhotra, PhD, is a senior technical business development manager at AWS, where he leads a number of initiatives supporting the NIH and the life sciences departments at US Higher Education institutions. Prior to joining AWS, Dr. Malhotra spent six years as a staff scientist and team lead at The Jackson Laboratory for Genomic Medicine. At JAX, he developed several genomics algorithms and led large scale NIH initiatives such as the NHGRI's Human Genome Structural Variation project. He has also contributed to other large-scale consortia programs such as TCGA, 1000 Genomes, and ENCODE projects. Before JAX, Dr. Malhotra was a Department of Defense funded postdoctoral fellow at the University of Virginia for three years. In both of these roles, he led a team of scientists conducting computational genomics research into the areas of cancer biology and human genetics. Dr. Malhotra has a PhD in biochemistry and molecular genetics and a Masters in Computer Science from the University of Virginia and has authored more than 30 publications with more than 14,000 citations.
Joel A. Lipkin; PhD; Chief Operating Officer, Four Points Technology LLC
Joel A. Lipkin, PhD, is the chief operating officer at Four Points Technology LLC where he leads the company’s development and operational support for cloud adoption within the Federal Government. Dr. Lipkin is the executive lead for Four Points’ STRIDES Initiative. Prior to joining Four Points, he held management and analytic positions at a number of federally-focused technology studies and analysis organizations including Affigent LLC, GTSI, Zenith Data Systems, The Center for Naval Analyses, King Research, and Planning Research. Dr. Lipkin earned a PhD in medieval history from the Catholic University of America and has taught both history and information systems at George Washington University and Catholic University.
Taha Kass-Hout; MD; Director, Machine Learning, and Chief Medical Officer, AWS
Dr. Taha Kass-Hout is the director, machine learning and chief medical officer at Amazon Web Services (AWS). Prior to joining Amazon, Taha was a public servant in the U.S. federal government bringing about change through his role as the first chief health informatics officer for U.S. Food and Drug Administration, where he created openFDA and precisionFDA, part of President Obama’s 2015 Precision Medicine Initiative. Both efforts were referenced in the 2015 White House Strategy for American Innovation, and precisionFDA was awarded the top prize at the 2016 Bio-IT World Best Practices. Additionally, Taha spearheaded groundbreaking programs at the U.S. Centers for Disease Control and Prevention for electronic disease surveillance, including the quick disease surveillance scale-up during the H1N1 influenza pandemic taking it from less than 6% to >90% coverage of the U.S. population ahead of the 2nd wave of the pandemic. During his one-year transition from the U.S. federal government, Taha served as SVP and chief digital health and analytics officer at Trinity Health, one of the leaders in value-based health care systems in the U.S., and led the development of the American Heart Association Precision Medicine initiative. Taha received his medical training at Beth Israel Deaconess Medical Center, Harvard Medical School, and during his time there, was part of the BOAT coronary stent clinical trial. He holds a Doctor of Medicine and Master of Science (Bioinformatics) from the University of Texas Health Science Center at Houston.
Lucy Lu Wang; PhD; Young Investigator, Allen Institute for AI
Dr. Lucy Lu Wang is a young investigator at the Allen Institute for AI, where she is part of the Semantic Scholar Research team. She works on increasing access to and understanding of scientific text, with a focus on applications in bioNLP, biomedical ontology, and the science of science. Her work on gender trends in publishing and supplement interaction detection has been featured in Geekwire, Gizmodo, Axios, VentureBeat, and the New York Times. She completed her PhD at the University of Washington in biomedical and health informatics.
Kyle Lo, Research Scientist, Allen Institute for AI
Kyle Lo is an applied research scientist at the Allen Institute for AI on the Semantic Scholar Research team, where he works on NLP for scientific literature with emphasis on knowledge representation, discourse, and science-of-science. His work on domain adaptation of language models was nominated for best paper at ACL, and his work on sex bias in clinical trials and scientific fact checking has been featured in articles in MIT Tech Review and Quartz. He has an MS in statistics from the University of Washington.
Dr. Rob Fatland, Research Computing Director, University of Washington
Dr. Rob Fatland is the research computing director at the University of Washington (UW), jointly affiliated with the eScience Institute and UW IT since 2015. He works on research acceleration in data science with frequent consulting and emphasis on migration to the cloud. Previously, he contributed to geophysics-oriented information science projects ranging from sensor networks to executable papers at Microsoft Research and Vexcel Corporation. He received his PhD in geophysics from the University of Alaska Fairbanks and his BS in physics from California Institute of Technology. A six-year stint at NASA’s Jet Propulsion Laboratory introduced him to both the follies of Acquire-Once-View-Never data systems and the harrowing aspects of remote fieldwork. He is interested in translating questions concerning reservoir transport mechanisms in earth system science (for example, within models) to tractable observational tasks and to data analysis. Since 1994, Dr. Fatland has done freelance outreach in K20 STEM education.
Prof. Frank Wuerthwein, Executive Director Open Science Grid (OSG), and Professor of Physics at UC San Diego
Professor Wuerthwein is an experimental particle physicist who analyzes data from the Large Hadron Collider. He is the executive director of OSG responsible for providing an integrated data and compute platform that advances open science through distributed high throughput computing. Scientists used the platform in the detection of gravitational waves (Nobel Prize 2017), the discovery of the Higgs Boson (Nobel Prize 2013), and other scientific achievements.
Robert L. Grossman; Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science; Jim and Karen Frank Director, Center for Translational Data Science University of Chicago
Robert L. Grossman is the Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science at the University of Chicago. He has also served as the chief research informatics officer of the Biological Sciences Division since 2011. He is the principal investigator for the National Cancer Institute Genomic Data Commons (GDC), a platform for the cancer research community that manages, analyzes, integrates, and shares large-scale genomic datasets in support of precision medicine. The GDC is used by over 50,000 researchers each month. He has also built data commons to support research in other areas, including cardiology, neuroscience, infectious diseases, and the environment. He serves as chair of the Open Commons Consortium, a nonprofit that develops and operates data commons to support research in science, medicine, health care, and the environment. He is also a partner of Analytic Strategy Partners. He was the founder and Managing Partner of Open Data Group from 2002-2016. Open Data Group develops and deploys machine learning and artificial intelligence models for Fortune 500 companies. He is a fellow of the American Association for the Advancement of Science (AAAS) and the Association of Computing Machinery (ACM).
Dr. Orly Alter; Utah Science, Technology, and Research (USTAR) Associate Professor of Bioengineering and Human Genetics; the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute, University of Utah
Dr. Orly Alter is a Utah Science, Technology, and Research (USTAR) associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute at the University of Utah and the principal investigator of National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant. Inventor of the “eigengene,” she pioneered the matrix and tensor modeling of large-scale molecular biological data, which, as she demonstrated, can correctly predict previously unknown physical, cellular, and evolutionary mechanisms. Alter received her PhD in applied physics at Stanford University and her BSc magna cum laude in physics at Tel Aviv University. Wiley-Interscience published her PhD thesis “Quantum Measurement of a Single System,” as a book that is now recognized as crucial to the field of gravitational wave detection.
Dr. Dan Stanzione, Executive Director of Texas Advanced Computing Center and Associate Vice President for Research, The University of Texas at Austin
Dr. Dan Stanzione is the Associate Vice President for Research at The University of Texas at Austin and the Executive Director of the Texas Advanced Computing Center (TACC). He is a nationally recognized leader in the field of High-Performance Computing (HPC). He is also the principal investigator (PI) for a National Science Foundation (NSF) next generation Supercomputer, Frontera, the fastest supercomputer at any U.S. university. Dr. Stanzione is also responsible for TACC's Stampede2 and Wrangler systems, supercomputers for HPC and for data intensive applications. For six years he was co-PI of CyVerse, a large-scale NSF life sciences cyberinfrastructure. Dr. Stanzione also co-led TACC’s Ranger and Lonestar supercomputers deployed at UT Austin. He received his bachelor's degree in electrical engineering and his master's degree and doctorate in computer engineering from Clemson University.
Dr. Sanjay Padhi, Head of AWS Research, US Education, Amazon Web Services
Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives with the National Science Foundation. He is a physicist and Adjunct Professor at Brown University. Dr. Padhi has more than 15 years of experience in large-scale distributed computing, Data Analytics and Machine Learning. He is the co-creator of the Workload Management Systems currently used for all the data processing and simulations by CMS, one of the largest experiments in the world at CERN, consisting of more than 180 institutions across 40 countries. Sanjay obtained his Ph.D from McGill University in High Energy Physics, co-author of more than 900 publications and is also appointed by the Dean of Faculty as an Adjunct Professor of Physics at Brown University.
Alan Packer, Director, Alexa Natural Language Understanding (NLU) Interpretations, Amazon
Alan joined Amazon in March 2018 as Director of Engineering for the Alexa NLU Interpretations team. The team members build the platform and core technology for NLU models used both by external and internal developers. Alan has over 20 years of experience building ML systems at scale in security, search, speech recognition, and natural language understanding. Prior to joining Amazon, he worked at Facebook, managing horizontal ranking teams for Facebook’s search engine and language technology teams for speech recognition and machine translation. Before that, he was at Microsoft until 2014. Most recently, he led the Cortana back-end technology team within Bing. Alan has two adult children (20 and 22). In his spare time, he walks away from ML to help his wife at a winery they run in Woodinville, Washington.
Dr. Ian Foster, Director, Argonne Data Science, and Learning Division, Argonne National Lab, and Professor at the University of Chicago
Dr. Ian Foster is the Director of Argonne’s Data Science and Learning Division, Argonne Senior Scientist and Distinguished Fellow, and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was the Director of Argonne’s Computation Institute from 2006 to 2016. Foster’s research contributions span high performance computing, distributed systems, and data-driven discovery. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures.
Dr. Susan K. Gregurick, Director, Division of Biophysics, Biomedical Technology, and Computional Biosciences at NIH's National Institute of General Medical Sciences (NIGMS)
Susan K. Gregurick is the Division Director for Biophysics, Biomedical Technology, and Computational Biosciences (BBCB) in NIH’s National Institute of General Medical Sciences (NIGMS). Her mission in BBCB is to advance research in computational biology, biophysics and data sciences, mathematical and biostatistical methods, and biomedical technologies in support of the NIGMS mission to increase understanding of life processes. Dr. Gregurick also serves as the Senior Advisor to the Office of Data Science Strategy, a newly formed office within the Office of the Director at NIH. Prior to joining the NIH, Susan was a program manager for the Department of Energy where she oversaw the development and implementation of the DOE Systems Biology Knowledgebase, which is a framework to integrate data, models, and simulations together for a better understanding of energy and environmental processes. During Susan’s academic career she was a Professor of Computational Biology at the University of Maryland, Baltimore County and her research interests include dynamics of large biological macromolecules. Susan holds a Ph.D. in Computational Chemistry and her areas of expertise are computational biology, high performance computing, neutron scattering and bioinformatics.
Prof. Judith Dexheimer, Associate Professor at Cincinnati Children's Hospital Medical Center
Dr. Dexheimer is an Associate Professor of Pediatrics at Cincinnati Children’s Hospital Medical Center and the University of Cincinnati. She is a clinical informaticist working on the development and implementation of machine learning applications into clinical care. Her research focus areas include machine learning techniques, real-time patient identification system, disparate data merging, and applications of clinical data. Dr. Dexheimer co-leads the Decision Support Analytics Workgroup (DSAW), which investigates the links between the effectiveness of clinical decision support (CDS), patient safety, and user efficiency. This research has demonstrated ties between decreasing alert burden on clinicians, increasing CDS alert salience, and improving patient outcomes.
Ms. Christine Kirkpatrick, Executive Director at National Data Service
Christine Kirkpatrick oversees the San Diego Supercomputer Center’s (SDSC) Research Data Services division, which manages infrastructure, networking, and services for research projects of regional and national scope. Kirkpatrick is a recognized expert in the implementation of research computing services, with an emphasis on data science workloads, as well as operational cyberinfrastructure (CI) at scale. Kirkpatrick founded and hosts the US GO FAIR Office at SDSC, is the Executive Director of the US National Data Service (NDS), and Co-PI and Deputy Director of the West Big Data Innovation Hub (WBDIH). She co-chairs the All (Big Data) Hub Infrastructure Working Group and is co-PI of the Open Storage Network. Kirkpatrick received her master’s degree from the Jacobs School of Engineering at University of California San Diego. She serves on the Technical Advisory Board (TAB) for the Research Data Alliance (RDA) and the external Advisory Boards for the European Open Science Cloud (EOSC) Hub and EOSC Nordic.
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Past Sessions
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Title: Cloud Computing for the Era of Brain Observatories
Presenter: Ariel Rokem; PhD; Research Assistant Professor, University of Washington
Date: September 30, 2020
Abstract:
Through large collaborative research projects, the field of neuroscience is gaining access to unprecedented amounts of information about the structure and function of the brain. These datasets are already having a profound impact on neuroscience, and they promise to help us understand the biological underpinnings of human brain health. At the same time, the deluge of data is generating new challenges, as researchers struggle to store, manage, analyze, and interpret the data. You can use scalable cloud computing methods to lower some of these barriers. But the adoption of these methods is in its infancy. Join this presentation to learn about tools to smoothly transition neuroscience into cloud computing. In particular, Cloudknot is a software library that we developed that packages existing Python code for deployment at scale in AWS Batch. In this session, see the use of this software in our research that uses large-scale magnetic resonance imaging (MRI) datasets to understand the role of brain connections in cognition and behavior.
Presenter Biographies:
Ariel Rokem is a research assistant professor at the University of Washington Department of Psychology. He received a PhD in neuroscience from University of California, Berkeley (2010) and additional postdoctoral training in computational neuroimaging at Stanford University (2011-2015). He was also a senior data scientist at the University of Washington eScience Institute (2015-2020). He leads a research program in neuroinformatics: the development of data science tools, techniques and methods and their application to the analysis of neural data. He is a member of the software and data carpentry communities, where he has been an instructor since 2013 and an instructor trainer since 2015. He also directs the annual Summer Institute for Neuroimaging and Data Science. A contributor to multiple open-source software projects in the scientific Python ecosystem, he is a member of the editorial board of the Journal for Open Source Software (JOSS).
Title: Biomedical Research in the Cloud with the NIH STRIDES Initiative
Presenters: Dr. Susan Gregurick, Dr. Ankit Malhotra, and Dr. Joel Lipkin
Date: August 26, 2020
Abstract:
The National Institutes of Health (NIH) Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative aims to modernize the biomedical research ecosystem by fostering innovation in biomedical research using technological advancements in the cloud. NIH-funded biomedical researchers are eligible to leverage the NIH STRIDES Initiative to access cutting-edge cloud services and tools, which includes access to valuable datasets and favorable pricing on cloud environments. Join this session where we will have three separate presentations from representatives from the NIH, AWS, and Four Points Technology, LLC.
Presenter Biographies:
Susan K. Gregurick, PhD, is the associate director for Data Science and director of the Office of Data Science Strategy (ODSS) at the National Institutes of Health. Under Dr. Gregurick’s leadership, the ODSS leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that comprise the NIH. Dr. Gregurick was instrumental in the creation of the ODSS in 2018 and served as a senior advisor to the office until being named to her current position. Dr. Gregurick received her undergraduate degree in chemistry and mathematics from the University of Michigan and her PhD in physical chemistry from the University of Maryland.
Ankit Malhotra, PhD, is a senior technical business development manager at Amazon Web Services (AWS), where he leads a number of initiatives supporting the NIH and the life sciences departments at US Higher Education institutions. Prior to joining AWS, Dr. Malhotra spent six years as a staff scientist and team lead at The Jackson Laboratory for Genomic Medicine. At JAX, he developed several genomics algorithms and led large scale NIH initiatives such as the NHGRI's Human Genome Structural Variation project. He has also contributed to other large-scale consortia programs such as TCGA, 1000 Genomes, and ENCODE projects. Before JAX, Dr. Malhotra was a Department of Defense funded postdoctoral fellow at the University of Virginia for three years. In both of these roles, he led a team of scientists conducting computational genomics research into the areas of cancer biology and human genetics. Dr. Malhotra has a PhD in biochemistry and molecular genetics and a Masters in Computer Science from the University of Virginia and has authored more than 30 publications with more than 14,000 citations.
Joel A. Lipkin, PhD, is the chief operating officer at Four Points Technology LLC where he leads the company’s development and operational support for cloud adoption within the Federal Government. Dr. Lipkin is the executive lead for Four Points’ STRIDES Initiative. Prior to joining Four Points, he held management and analytic positions at a number of federally-focused technology studies and analysis organizations including Affigent LLC, GTSI, Zenith Data Systems, The Center for Naval Analyses, King Research, and Planning Research. Dr. Lipkin earned a PhD in medieval history from the Catholic University of America and has taught both history and information systems at George Washington University and Catholic University.
Title: CORD-19 Search: Using Machine Learning to Explore COVID-19 Scientific Literature
Presenters: Taha Kass-Hout, MD; Lucy Lu Wang, PhD; and Kyle Lo
Date: July 29, 2020
Abstract:
Healthcare providers and researchers face an exponentially increasing volume of information about COVID-19, which makes it difficult to derive trends that can inform treatment. In response, Amazon Web Services (AWS) launched CORD-19 Search, a search website powered by machine learning, to help researchers quickly search for research papers and documents to answer questions like “When is the salivary viral load highest for COVID-19?” Built on the Allen Institute for AI’s CORD-19 open research dataset of more than 130,000 research papers and other materials, this machine learning solution uses Amazon Comprehend Medical to extract relevant medical information from unstructured text. It also uses Amazon Kendra to deliver robust natural-language query capabilities to accelerate the pace of discovery.
Join this session to explore the challenges faced by COVID-19 researchers and the role machine learning is playing in helping derive insights from the tens of thousands of scientific and medical papers on COVID-19.
Presenter Biographies:
Dr. Taha Kass-Hout is the director, machine learning and chief medical officer at Amazon Web Services (AWS). Prior to joining Amazon, Taha was a public servant in the U.S. federal government bringing about change through his role as the first chief health informatics officer for U.S. Food and Drug Administration, where he created openFDA and precisionFDA, part of President Obama’s 2015 Precision Medicine Initiative. Both efforts were referenced in the 2015 White House Strategy for American Innovation, and precisionFDA was awarded the top prize at the 2016 Bio-IT World Best Practices. Additionally, Taha spearheaded groundbreaking programs at the U.S. Centers for Disease Control and Prevention for electronic disease surveillance, including the quick disease surveillance scale-up during the H1N1 influenza pandemic taking it from less than 6% to >90% coverage of the U.S. population ahead of the 2nd wave of the pandemic. During his one-year transition from the U.S. federal government, Taha served as SVP and chief digital health and analytics officer at Trinity Health, one of the leaders in value-based health care systems in the U.S., and led the development of the American Heart Association Precision Medicine initiative. Taha received his medical training at Beth Israel Deaconess Medical Center, Harvard Medical School, and during his time there, was part of the BOAT coronary stent clinical trial. He holds a Doctor of Medicine and Master of Science (Bioinformatics) from the University of Texas Health Science Center at Houston.
Dr. Lucy Lu Wang is a young investigator at the Allen Institute for AI, where she is part of the Semantic Scholar Research team. She works on increasing access to and understanding of scientific text, with a focus on applications in bioNLP, biomedical ontology, and the science of science. Her work on gender trends in publishing and supplement interaction detection has been featured in Geekwire, Gizmodo, Axios, VentureBeat, and the New York Times. She completed her PhD at the University of Washington in biomedical and health informatics.
Kyle Lo is an applied research scientist at the Allen Institute for AI on the Semantic Scholar Research team, where he works on NLP for scientific literature with emphasis on knowledge representation, discourse, and science-of-science. His work on domain adaptation of language models was nominated for best paper at ACL, and his work on sex bias in clinical trials and scientific fact checking has been featured in articles in MIT Tech Review and Quartz. He has an MS in statistics from the University of Washington.
Title: Research Computing on the Cloud at the University of Washington and CloudBank
Presenter: Dr. Rob Fatland, Research Computing Director, University of Washington
Date: May 28, 2020
Abstract:
Research computing at the University of Washington (UW) is a fascinating landscape of topics, challenges, catastrophes, and successes. Join this session to hear about the transition of research computing to the AWS Cloud. With growing public awareness, large research funding organizations like the National Science Foundation (NSF) have joined in supporting the cloud transition process. An example of NSF-supported efforts includes a collaboration of three universities called CloudBank, which includes UW.
You will learn about CloudBank, its vision and goals, and the underlying problem of organizing information to impedance match. Discover cloud-hosted computing environments that flip decades of tradition by bringing computing to data. This process, exemplified in a big data geoscience project called Pangeo, raises important questions about infrastructure-building, data hosting, and how non-commercial entities address paying for cloud-based research.
Presenter Biography:
Dr. Rob Fatland is the research computing director at the University of Washington (UW), jointly affiliated with the eScience Institute and UW IT since 2015. He works on research acceleration in data science with frequent consulting and emphasis on migration to the cloud. Previously, he contributed to geophysics-oriented information science projects ranging from sensor networks to executable papers at Microsoft Research and Vexcel Corporation. He received his PhD in geophysics from the University of Alaska Fairbanks and his BS in physics from California Institute of Technology. A six-year stint at NASA’s Jet Propulsion Laboratory introduced him to both the follies of Acquire-Once-View-Never data systems and the harrowing aspects of remote fieldwork. He is interested in translating questions concerning reservoir transport mechanisms in earth system science (for example, within models) to tractable observational tasks and to data analysis. Since 1994, Dr. Fatland has done freelance outreach in K20 STEM education.
Title: GPU Cloud Bursting for Multi-Messenger Astrophysics with IceCube
Presenter: Frank Wuerthwein, Executive Director, Open Science Grid (OSG), and Professor of Physics, University of California (UC) San Diego
Date: April 30, 2020
Abstract:
On November 16, the Saturday before SC19 - the International Conference for High Performance Computing, Networking, Storage, and Analysis in Denver, UC San Diego performed a graphics processing unit (GPU) cloud burst during which they purchased the entire for-sale capacity of all GPUs available across Amazon Web Services (AWS) and other cloud providers worldwide.
Join this webinar to learn about the scientific motivation behind this project, hear about the preparation and execution, and learn about follow up bursts. In addition, you will discover the extent to which this is applicable to other sciences as well as commercial applications. At peak, UC San Diego operated a sustained workflow across 51,500 GPUs, eight generations of NVIDIA GPUs, and 28 cloud regions across three cloud providers. In a follow up burst, they integrated cloud and on-premises, simplifying the workflow.
Presenter Biography:
Professor Wuerthwein is an experimental particle physicist who analyzes data from the Large Hadron Collider. He is the executive director of OSG responsible for providing an integrated data and compute platform that advances open science through distributed high throughput computing. Scientists used the platform in the detection of gravitational waves (Nobel Prize 2017), the discovery of the Higgs Boson (Nobel Prize 2013), and other scientific achievements.
Title: The Road from Data Commons to Data Ecosystems: Challenges, Opportunities, and Emerging Best Practices
Presenter: Robert L. Grossman, Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science, Jim and Karen Frank Director, Center for Translational Data Science University of Chicago
Date: March 12, 2020
Abstract:
There are now several large-scale data commons supporting the biomedical research community and the beginnings of data ecosystems. In this talk, we discuss some of the emerging best practices around data ecosystems, as well as some of the challenges and opportunities. We also discuss some case studies of cloud-based data commons and data ecosystems that were developed using the open source Gen3 data platform.
Presenter Biography:
Robert L. Grossman is the Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science at the University of Chicago. He has also served as the chief research informatics officer of the Biological Sciences Division since 2011. He is the principal investigator for the National Cancer Institute Genomic Data Commons (GDC), a platform for the cancer research community that manages, analyzes, integrates, and shares large-scale genomic datasets in support of precision medicine. The GDC is used by over 50,000 researchers each month. He has also built data commons to support research in other areas, including cardiology, neuroscience, infectious diseases, and the environment. He serves as chair of the Open Commons Consortium, a nonprofit that develops and operates data commons to support research in science, medicine, health care, and the environment. He is also a partner of Analytic Strategy Partners. He was the founder and Managing Partner of Open Data Group from 2002-2016. Open Data Group develops and deploys machine learning and artificial intelligence models for Fortune 500 companies. He is a fellow of the American Association for the Advancement of Science (AAAS) and the Association of Computing Machinery (ACM).
Title: Multi-Tensor Decompositions for Personalized Cancer Diagnostics, Prognostics, and Therapeutics
Presenter: Dr. Orly Alter, Utah Science, Technology, and Research (USTAR) Associate Professor of Bioengineering and Human Genetics, the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute, University of Utah
Date: January 30, 2020
Abstract:
Join this webinar to learn about the development of novel, multi-tensor generalizations of the singular value decomposition and their use in the comparisons of tumor and normal genomes of cancer patients. The talk will describe examples from brain, lung, ovarian, and uterine cancers. By using the complex structure of the datasets, rather than simplifying them as is commonly done, these mathematical frameworks enable the separation of genome-scale patterns of DNA copy-number alterations—which occur only in the tumor genomes—from those that occur in the genomes of normal cells in the body and variations caused by experimental inconsistencies. These patterns predict survival and response to treatment statistically better than, and independent of, the best indicators in clinical use and existing laboratory tests. The talk will also describe a recent, retrospective clinical trial that validates the brain cancer pattern. Researchers have recognized recurring alterations as a hallmark of cancer for over a century and observed them in these cancers’ genomes for decades. However, nobody succeeded in identifying copy-number subtypes predictive of patients’ outcomes before this work. The data had been publicly available, but the patterns remained unknown until the data were modeled by using the multi-tensor decompositions. This demonstrates that the decompositions underlie a mathematically universal description of the genotype-phenotype relationships in cancer that other machine learning methods miss.
Presenter Biography:
Dr. Orly Alter is a Utah Science, Technology, and Research (USTAR) associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute at the University of Utah and the principal investigator of National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant. Inventor of the “eigengene,” she pioneered the matrix and tensor modeling of large-scale molecular biological data, which, as she demonstrated, can correctly predict previously unknown physical, cellular, and evolutionary mechanisms. Alter received her PhD in applied physics at Stanford University and her BSc magna cum laude in physics at Tel Aviv University. Wiley-Interscience published her PhD thesis “Quantum Measurement of a Single System,” as a book that is now recognized as crucial to the field of gravitational wave detection.
Title: Advancing the Frontiers of Science Using Extreme Scale Computing
Presenters: Dr. Dan Stanzione, Executive Director of Texas Advanced Computing Center and Associate Vice President for Research, The University of Texas at Austin (Discussion Topic: Leadership Computing in the Age of Cloud)
Dr. Sanjay Padhi, Head of AWS Research, US Education, Amazon Web Services (Discussion Topic: Transforming Research in collaboration with Scientific Communities)
Date: November 13, 2019
Session 1: Leadership Computing in the Age of Cloud
Presenter Abstract:
Attend this session to learn an overview of Frontera, the latest leadership class computing system hosted at the Texas Advanced Computing Center, and its integration with the public cloud, including AWS. Frontera is currently the top #5 leadership class high performance computing (HPC) machine in the world and the nation’s most powerful academic high performance computing system. Join us to learn more about the scientific use cases that will (and will not) require such extreme scale machines and the shared vision of making hybrid HPC-Cloud based scientific workflows a reality.
Presenter Biography:
Dr. Dan Stanzione is the Associate Vice President for Research at The University of Texas at Austin and the Executive Director of the Texas Advanced Computing Center (TACC). He is a nationally recognized leader in the field of High-Performance Computing (HPC). He is also the principal investigator (PI) for a National Science Foundation (NSF) next generation Supercomputer, Frontera, the fastest supercomputer at any U.S. university. Dr. Stanzione is also responsible for TACC's Stampede2 and Wrangler systems, supercomputers for HPC and for data intensive applications. For six years he was co-PI of CyVerse, a large-scale NSF life sciences cyberinfrastructure. Dr. Stanzione also co-led TACC’s Ranger and Lonestar supercomputers deployed at UT Austin. He received his bachelor's degree in electrical engineering and his master's degree and doctorate in computer engineering from Clemson University.
Session 2: Transforming Research in Collaboration with Scientific Communities
Presenter Abstract:
With quantum computing on the horizon, artificial intelligence and machine learning on the rise, and HPC systems becoming more heterogeneous, researchers are more distributed than ever. Collaborations and research platforms to share data, algorithms and computing resources are vital to foster scientific innovations. Learn about the role AWS plays in several domains including National Institutes of Health’s STRIDES initiative to advance biomedical discoveries, NSF’s program on exascale studies using AWS to advance multi-messenger astrophysics, accelerating time to science with Internet2 (E-CAS), nationwide Open Storage Network (OSN), NASA's Desert research, Astronomy in the Cloud with Chilean Data Observatory, as well as Scientific Computing at the Large Hadron Collider. AWS provides elastic and scalable compute resources, a secure and shareable storage platform, and tools for advanced analytics and machine learning with global network connectivity for research collaborations. Tune in to also learn about how we plan to integrate with the nation’s capacity and capability class supercomputers including Frontera and expanse for science at the endless frontier.
Presenter Biography:
Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives with the National Science Foundation. He is a physicist and Adjunct Professor at Brown University. Dr. Padhi has more than 15 years of experience in large-scale distributed computing, Data Analytics and Machine Learning. He is the co-creator of the Workload Management Systems currently used for all the data processing and simulations by CMS, one of the largest experiments in the world at CERN, consisting of more than 180 institutions across 40 countries. Sanjay obtained his Ph.D from McGill University in High Energy Physics, co-author of more than 900 publications and is also appointed by the Dean of Faculty as an Adjunct Professor of Physics at Brown University.
Title: Natural Language Understanding in Alexa
Presenter: Alan Packer, Director, Alexa Natural Language Understanding (NLU) Interpretations, Amazon
Date: October 29, 2019
Presenter Abstract:
It has taken decades for scientists to understand natural human speech. Today, voice-activated, artificial intelligence interfaces such as Alexa, the natural language processing system by Amazon, can interact with humans at various levels. Join this webinar to learn more about the internals of the data flow within Alexa in response to a spoken command, how our natural language understanding stack works, and our investments in deep learning, automated model updates, and privacy-preserving machine learning (ML).
Presenter Biography:
Alan Packer joined Amazon in March 2018 as Director of Engineering for the Alexa NLU Interpretations team. The team members build the platform and core technology for NLU models used both by external and internal developers. Alan has over 20 years of experience building ML systems at scale in security, search, speech recognition, and natural language understanding. Prior to joining Amazon, he worked at Facebook, managing horizontal ranking teams for Facebook’s search engine and language technology teams for speech recognition and machine translation. Before that, he was at Microsoft until 2014. Most recently, he led the Cortana back-end technology team within Bing. Alan has two adult children (20 and 22). In his spare time, he walks away from ML to help his wife at a winery they run in Woodinville, Washington.
Title: A Simplified Approach to Data-Driven Research Collaboration
Presenter: Dr. Ian Foster, Director, Argonne Data Science, and Learning Division, Argonne National Lab, and Professor at the University of Chicago
Date: September 30, 2019
Abstract:
With artificial intelligence and machine learning on the rise, high performance computing (HPC) systems proliferating, and research teams more distributed than ever, reliable research data management is now more important than ever. We tend to take these functions for granted, and yet modern collaborative research would not be possible without them. When scientists can move and share data quickly, easily, and securely, they accelerate the pace of research and innovation. Globus has spent the past nine years developing a research data management service with over 100,000 users at thousands of organizations worldwide. Tune in to learn about what makes Globus unique, support for various compliance requirements, and common use cases, such as data migration from scientific instruments and cloud storage like Amazon S3.
Presenter Biography:
Dr. Ian Foster is the Director of Argonne’s Data Science and Learning Division, Argonne Senior Scientist and Distinguished Fellow, and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was the Director of Argonne’s Computation Institute from 2006 to 2016. Foster’s research contributions span high performance computing, distributed systems, and data-driven discovery. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures.
Title: NIH’s Strategic Vision for Data Science: Enabling a FAIR-Data Ecosystem
Presenter: Dr. Susan Gregurick, Director, Division of Biophysics, Biomedical Technology, and Computational Biosciences at NIH’s National Institute of General Medical Sciences (NIGMS)
Date: August 29, 2019
Presenter Abstract/Biography:
Susan K. Gregurick is the Division Director for Biophysics, Biomedical Technology, and Computational Biosciences (BBCB) in NIH’s National Institute of General Medical Sciences (NIGMS). Her mission in BBCB is to advance research in computational biology, biophysics and data sciences, mathematical and biostatistical methods, and biomedical technologies in support of the NIGMS mission to increase understanding of life processes. Dr. Gregurick also serves as the Senior Advisor to the Office of Data Science Strategy, a newly formed office within the Office of the Director at NIH. Prior to joining the NIH, Susan was a program manager for the Department of Energy where she oversaw the development and implementation of the DOE Systems Biology Knowledgebase, which is a framework to integrate data, models, and simulations together for a better understanding of energy and environmental processes. During Susan’s academic career she was a Professor of Computational Biology at the University of Maryland, Baltimore County and her research interests include dynamics of large biological macromolecules. Susan holds a Ph.D. in Computational Chemistry and her areas of expertise are computational biology, high performance computing, neutron scattering and bioinformatics.
Title: Artificial Intelligence and Machine Learning in Pediatric Biomedical Research
Presenter: Prof. Judith Dexheimer, Associate Professor at Cincinnati Children's Hospital Medical Center
Date: July 31, 2019
Presenter Abstract/Biography:
Dr. Dexheimer is an Associate Professor of Pediatrics at Cincinnati Children’s Hospital Medical Center and the University of Cincinnati. She is a clinical informaticist working on the development and implementation of machine learning applications into clinical care. Her research focus areas include machine learning techniques, real-time patient identification system, disparate data merging, and applications of clinical data. Dr. Dexheimer co-leads the Decision Support Analytics Workgroup (DSAW), which investigates the links between the effectiveness of clinical decision support (CDS), patient safety, and user efficiency. This research has demonstrated ties between decreasing alert burden on clinicians, increasing CDS alert salience, and improving patient outcomes.
She will discuss the role and impact of artificial intelligence and machine learning in research with the electronic health record, explore unique aspects of conducting work in pediatrics with biomedical informatics, and examine what is being done now with machine learning and what the future might hold.
Title: Leveraging Distributed Cyberinfrastructure to Support Data-Driven Collaborative Research
Presenter: Ms. Christine Kirkpatrick, Executive Director at National Data Service
Date: June 26, 2019
Abstract:
Investments in computation and highspeed networking have increased and improved the national cyberinfrastructure (CI) capacity that researchers rely on. With research becoming increasingly data-driven, collaborative, and multi-institutional, there is a need for distributed storage with data sharing, data discovery, computation at the edge, and cloud caching. This session will introduce the national initiatives working to enhance data-driven and collaborative research. Hear highlights from the Open Storage Network, the US National Data Service and its collaborations with the Cybersecurity education project, CHEESE, the IMLS-funded PresQT, and NIH-funded Metabolomics Workbench. Learn how to get involved with the Big Data Hubs via the All Hub Infrastructure Working Group.
Presenter Biography:
Christine Kirkpatrick oversees the San Diego Supercomputer Center’s (SDSC) Research Data Services division, which manages infrastructure, networking, and services for research projects of regional and national scope. Kirkpatrick is a recognized expert in the implementation of research computing services, with an emphasis on data science workloads, as well as operational cyberinfrastructure (CI) at scale. Kirkpatrick founded and hosts the US GO FAIR Office at SDSC, is the Executive Director of the US National Data Service (NDS), and Co-PI and Deputy Director of the West Big Data Innovation Hub (WBDIH). She co-chairs the All (Big Data) Hub Infrastructure Working Group and is co-PI of the Open Storage Network. Kirkpatrick received her master’s degree from the Jacobs School of Engineering at University of California San Diego. She serves on the Technical Advisory Board (TAB) for the Research Data Alliance (RDA), and the external Advisory Boards for the European Open Science Cloud (EOSC) Hub and EOSC Nordic.
Title: Science at the Petascale: Advancing Global Scientific Projects Through Distributed Computing
Presenter: Prof. Frank Wuerthwein, Executive Director, Open Science Grid (OSG), and Professor of Physics, UC San Diego
Date: Wednesday, May 15, 2019
Abstract:
Attend this session to receive an overview of three global science drivers – the Large Hadron Collider, gravitational wave detection, and the IceCube neutrino observatory, elucidating common patterns in the architecture of their global computing infrastructure. Learn about the role AWS can play in these large international projects using advancements in computing technologies as data volumes explode over the next decade.
Presenter Biography:
Hear from Professor Wuerthwein, an experimental particle physicist analyzing data from the Large Hadron Collider, who is the executive director of OSG responsible for providing an integrated data and compute platform that advances open science through distributed high-throughput computing. The platform has been used in the detection of gravitational waves (Nobel Prize 2017), the discovery of the Higgs Boson (Nobel Prize 2013), and many other scientific achievements.