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Accelerating the Pace of Big Data

Just how Big is Big Data? It’s difficult to wrap our heads around it. We now carry in our pockets computers (a.k.a. smartphones) that have 1 million times more memory than NASA’s Apollo Guidance Computer, which was used to land the first human beings on the moon. The world’s most powerful supercomputer is the Summit housed at the Oakridge National Laboratory. It can perform 200 quadrillion (1 x 1015) calculations per second. 

It’s an indescribable number, somewhere between the number of ants alive on earth at one time and the number of grains of sand on earth. Suffice it to say, today’s researchers have some freakin’ amazing computer power at their disposal.

The truth, however, is that many scientific fields have yet to realize the potential of Big Data. That’s where the National Science Foundation’s (NSF) Big Idea, Harnessing the Data Revolution (HDR), comes in. This $30 million initiative is among 10 Big Ideas identified by NSF in 2017. Projects funded by this initiative are doing everything from improving our chances of detecting dark matter to resolving the tree of life

Coordinating the building of a national data infrastructure, sharing best practices and identifying gaps that need to be addressed by the community is no small effort. In April, 160 researchers — all of whom are Principal Investigators on HDR-funded projects — gathered online for the KI-facilitated 2020 HDR All-hands Meeting. Organizers from the NSF Big Data Innovation Hubs converted the meeting, originally scheduled to take place in the Washington, D.C. area, to a virtual event due to the COVID-19 pandemic.

“We want this to be a connected ecosystem so that results and findings and expertise that are generated in one project really can be circulated and built upon,” says Renata Rawlings-Goss, Ph.D., Executive Director of the South Big Data Innovation Hub (SouthBDHub), one of four regional Hubs across the country. “This meeting was about identifying projects, community building and forming collaborations — especially among those from disciplines that don’t normally work together.”

Tufts University’s Lenore Cowen, Ph.D., was impressed by the number of collaborators she was able to meet at the virtual meeting. “We are already pursuing some of these collaborations and others we are just beginning to follow-up on as the semester ends, summer begins and we have more time to talk further,” says Cowen, Professor of Computer Science and Director of T-Tripods.

Given the fairly recent start of the HDR Big Idea, Cowen says it was important not to cancel this meeting. “This is very new territory and the community got a lot out of it.”

The University of Utah’s Chris Meyers, Ph.D., says the meeting was perhaps even better virtual than in-person. “It was very seamless to move from discussions. The KIStorm software was great. I plan to use this as a model for future virtual meetings,” says Meyers, who is a Professor of Electrical and Computer Engineering.

Anastasios Sidiropoulos, Ph.D., agreed. “I was hoping to meet new colleagues, exchange ideas, and learn interesting research directions that the HDR projects are pursuing. My expectations were met fully,” says Sidiropoulos, an Associate Professor of Computer Science at the University of Illinois, Chicago. “I think virtual meetings like this are the future of scientific discourse.”

By Camille Mojica Rey, PhD, Science Communications Director, Knowinnovation

Using a Virtual Ecological Research Assistant (VERA) to explain the impact of social distancing

COVID-19 continues to spread across the country and around the world. The current strategy for managing the spread of COVID-19 is social distancing, and a new white paper from Georgia Tech applies the use of an interactive Artificial Intelligence (AI) tool to conceptualize the impact of social distancing on the spread of COVID-19.

Funded with a South Big Data Hub SPOKE grant, the Virtual Ecological Research Assistant (VERA) is web application that enables users to construct conceptual models of ecological systems, and run interactive simulations of these models. This allows users to explore ecological systems and perform “what if” experiments to either explain an existing ecological system or attempt to predict the outcome of future changes to one.

Researchers using VERA have now documented utilizing the tool to develop develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity.

Read the full white paper here.

Spoke Project Addresses COVID-19 with VERA

The NSF Spoke Project ‘Using Big Data for Environmental Sustainability: Big Data + AI Technology = Accessible, Usable, Useful Knowledge’ has repurposed VERA to model the effect of social distancing on the spread of COVID-19, including the SIR model of epidemiology. VERA enables a user to build conceptual models and agent-based simulations, and conduct “what if” virtual experiments.

Read the white paper abstract below:

COVID-19 continues to spread across the country and around the world. Current strategies for managing the spread of COVID-19 include social distancing. We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the
spread of COVID-19. Then, VERA automatically spawns agent-based simulations from the conceptual models, and, given a data set, automatically fills in the values of the simulation parameters
from the data. Next, the user can view the simulation results, and, if needed, revise the simulation parameters and run another experimental trial, or build an alternative conceptual model. We describe
the use VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity.

View the project and white paper ‘VERA_Epidemiology – White Paper 1: Using VERA to explain the impact of social distancing on the spread of COVID-19 HERE