Copy
COVID Information Commons Newsletter | May 28, 2021
Events
  • COVID-19 Research Webinar: Lightning Talks and Q&A - June 9
  • IEEE / Northeast Hub Data & Device Identity, Validation & Interoperability in Connected Healthcare Workshop - June 16
Opportunities
  • Request for Information (RFI): Use of Common Data Elements (CDEs) in NIH-funded research - Due May 28
  • Duke Machine Learning Virtual Summer School 2021 - June 14-17
News
  • Winners of the Inaugural CIC Student Paper Challenge
  • May COVID-19 Research Webinar Recap and Recordings
  • Build Your PI Page
June 9, 2021 COVID Information Commons Community Webinar: Lightning Talks and Q&A

Date: June 9, 2021, 4:00-5:00pm ET
REGISTER HERE

Meet the scientists seeking new insights on COVID-19. Every month, we bring together a group of researchers studying wide-ranging aspects of the current pandemic, to share their research and answer questions from our community. Learn more about their ongoing efforts in the fight against COVID-19, including opportunities for collaboration.

Featured Speakers:

Katie Corvey, Auburn University: A participatory study of how policy-makers and marginalized communities in the American South consume and act on scientific information in the context of Covid-19. Funded by NSF Social, Behavioral and Economic Sciences / Office of Multidisciplinary Activities.

Luis Ortiz, The New School: Interdependent social vulnerability of COVID-19 and weather-related hazards in New York City. Funded by NSF Engineering / Division of Chemical, Bioengineering, Environmental and Transport Systems.

Jeffrey Townsend, Yale University: Analyses of polymorphism and divergence to illuminate molecular evolution permissive of zoonoses in SARS and COVID-19. The durability of immunity against reinfection by SARS-CoV-2. Funded by NSF Biological Sciences / Division of Environmental Biology.

Gangli Wang, Georgia State University: Microelectrode Array Sensors for SARS-CoV-2 and Other RNA Viruses. Funded by NSF Mathematical and Physical Sciences / Division of Chemistry.

Haichong (Kai) Zhang, Worcester Polytechnic Institute: Robotic Lung Ultrasound for Triage of COVID-19 Patients in a Resource-Limited Environment. Funded by NIH Office of the Director.

Caroline Zeiss, Yale University. Viral Shedding and Transmission following Natural Infection and Vaccination in an Animal Model of Betacoronaviral Infection. Funded by NSF Biological Sciences / Division of Integrative Organismal Systems.

If you would like to present your NSF or NIH-funded COVID-related research at a future CIC community event, please contact the project team. We look forward to hearing from you!
IEEE / Northeast Hub Data & Device Identity, Validation & Interoperability in Connected Healthcare Workshop
 
Date: June 16, 2021, 11:00am–2:00pm ET
REGISTER HERE

The Global Connected Healthcare Cybersecurity Virtual 2021 Workshop Series is presented by the IEEE SA, IEEE/UL P2933™ Standards Working Group, and the Northeast Big Data Innovation Hub headquartered at Columbia University. 

As clinical IoT devices become increasingly connected to each other and to other technologies, the ability of connected systems to safely, securely, and effectively exchange and use the information becomes critical. This session of the Global Connected Healthcare Cybersecurity Virtual Workshop series will discuss reference implementations for existing interface standards and explore critical unanswered questions regarding safety, identity, cybersecurity, and performance attributes of connected systems in the context of sustainable and scalable data and device interoperability.

There are critical needs for clinical-based interoperability standards that address clinical and non-clinical scenarios and define specific functional requirements and non-functional requirements, such as Quality of Service (QoS), quality of measurement (precision and accuracy), and, most importantly, safety to describe the essential performance requirements for such composite systems. Sustainable and scalable interoperability of data and devices can enable connectivity across networks to streamline management, deployment, and integration cycles; facilitate quality assurance and harmonization of data that maximizes its utility across systems and platforms; minimize errors and adverse events; encourage innovation by enhancing the quality of available data; and ultimately contribute to improvements in patient care and clinical outcomes.

The workshop will explore how to best leverage emerging technologies such as blockchain and AI/ML as potential tools to mitigate interoperability challenges. Together we will develop recommendations for a pathway for standards to validate multi-vendor connected health technologies that enable a regulatory roadmap for approval of devices in interoperable composite systems.

Visit the event page for updates and registration details for the entire Global Connected Healthcare Cybersecurity Virtual Workshop Series from February through November 2021.

Request for Information (RFI): Use of Common Data Elements (CDEs) in NIH-Funded Research

Due: May 28, 2021

NIH is requesting public comment on the use of CDEs, particularly in the context of COVID-19 research, including opportunities for advancing research with CDEs, challenges to adopting CDEs, and guidance or tools that could facilitate use of CDEs. These comments will be used to inform NIH’s continuing development of guidance of CDE use for COVID-related research and assist in the planning for adequate funding of CDE efforts through research awards and contracts.

Learn more here.
Duke Machine Learning Virtual Summer School 2021

Dates: June 14-17, 2021

The Duke+Data Science program (+DS) is pleased to announce a virtual offering of the Duke Machine Learning School for summer 2021, which will be held June 14-17.

The 3.5 day curriculum in the Machine Learning Virtual Summer School (MLvSS) is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLvSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLvSS will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.

Eight Duke Machine Learning Schools have been presented since 2017, reaching hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus.

The 2021 MLvSS will be led by a trio of machine learning experts at Duke University: Professors Ricardo Henao, David Carlson, and Timothy Dunn. The event will also feature lectures by other Duke professors and the founding director of the Duke machine learning schools, Lawrence Carin. Hands-on software training will be provided by Duke graduate students who have extensive experience with these tools, and teaching assistants from the Duke AI Health Fellowship program will be available for assistance throughout the course.

Register for the MLvSS here and learn more about the curriculum details here

Winners of the Inaugural CIC Student Paper Challenge

We are delighted to announce the winners of the inaugural COVID Information Commons Student Paper Challenge. This challenge was an opportunity for all students taking undergraduate classes to write a research paper sharing their insights on a topic of their choice related to COVID-19. The winners were officially announced on the May 19th CIC Research Webinar, as highlighted in the certificates above. The three winners are each invited to present their work at a future CIC webinar and have their papers published on the CIC website and Columbia Academic Commons. Congratulations to the three winners, and thank you to all the participants!

Researchers Discuss Insights in May CIC Community Webinar
 
Thank you to all of the participants and speakers who attended the May CIC Community webinar! The webinar included talks from five NSF-funded researchers working to provide new insights around COVID-19: Alka Sapat of Florida Atlantic University, Ruth Serra-Moreno of University of Rochester, David Konisky of Indiana University, Austin Mast of Florida State University, and Peter Pirolli of the Florida Institute for Human and Machine Cognition, Inc.
Build Your PI Page!

As part of the new NSF COVID Awards and PI Database on the COVID Information Commons website, we've already added websites, research findings, and collaboration opportunities provided by over 250 NSF PIs. If you would like to help others further engage with your work by adding or updating any information on your PI page, please fill out this survey.

To learn more about the database, watch this overview.
The COVID Information Commons (CIC) serves as a resource for researchers, students and decision-makers from academia, government, nonprofit, and industry to identify collaboration opportunities, to leverage each other's research findings, and to accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic.

To suggest COVID research-related news, events, and opportunities for an upcoming newsletter, please email info@covidinfocommons.net.

Help build our community by forwarding CIC news widely, and encourage your colleagues to sign up for updates via this web form.

Thanks!  —The CIC Project Team
Twitter
Website
YouTube
LinkedIn
Copyright © 2021 Northeast Big Data Innovation Hub, All rights reserved.


Want to change how you receive these emails?
You can update your preferences or unsubscribe from this list.