AWS Education: Research Seminar Series
The 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). Interested in presenting? Contact us at aws-wwps-seminar@amazon.com.
Next webinar - Multi-Tensor Decompositions for Personalized Cancer Diagnostics, Prognostics, and Therapeutics
Date: January 30, 2020
Time: 9:00 - 10:00AM PST
Venue: Live broadcast
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
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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: 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
Time: 9:00 - 10:00AM PST
Abstract:
Join this webinar to learn more 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 brain, lung, ovarian, and uterine cancer patients, for example. They uncover patterns of DNA copy-number alterations that 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 in these cancers’ genomes for decades; however, researchers did not identify copy-number subtypes predictive of patients’ outcomes before. 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 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 a 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. Her PhD thesis on “Quantum Measurement of a Single System,” which was published by Wiley-Interscience as a book, is recognized today as crucial to the field of gravitational wave detection.
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Presenters
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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.
Prof. Frank Wuerthwein, Executive Director Open Science Grid (OSG), and Professor of Physics at UC San Diego
Prof. Wuerthwein, an experimental particle physicist analyzing data from the Large Hadron Collider, is responsible for providing an integrated data and compute platform that advances open science through distributed high-throughput computing as executive director of OSG. The platform has been used in the detection of gravitational waves (Nobel Prize 2017) and the discovery of the Higgs Boson (Nobel Prize 2013), as well as many other scientific achievements.
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Past Sessions
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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 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
Presenter Abstract/Biography:
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.
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
Presenter Abstract/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.
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.
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
Presenter Abstract/Biography:
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.
Hear from Prof. 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.
