Organizational Unit:

Institute for Data Engineering and Science

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https://hdl.handle.net/1853/70653

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South Big Data Innovation Hub

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Now showing 1 - 10 of 37

PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Closing Remarks

(Georgia Institute of Technology, 2021-02-23) May, Elebeoba
PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 3, Population Level Theme

(Georgia Institute of Technology, 2021-02-23) Grenfell, Bryan ; Yu, Bin ; Peccia, Jordan

Bryan Grenfell - Plenary Talk TITLE: "What Cross-Scale Research Can Tell Us About Predicting, Understanding And Mitigating Future Pandemics?" We briefly review the epidemic and evolutionary dynamics of directly-transmitted infections and their transition from pandemics to endemicity. We discuss how cross-scale dynamics, from protein to pandemic, determine key issues in understanding, predicting and mitigating outbreaks, then build on this to discuss future cross-scale research and public health priorities.
PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 4, Physiological and Environmental Level Theme

(Georgia Institute of Technology, 2021-02-23) Kirschner, Denise ; Kumar, Vipin ; Colwell, Rita

Denise Kirschner - Plenary Talk TITLE: "A Multi-Scale Systems Biology Approach Toward Tuberculosis Infection Interventions".
PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 2, Molecular Level Theme

(Georgia Institute of Technology, 2021-02-22) Turner, Paul ; Riedel, Marc ; Van Valen, David

Paul Turner - Plenary Talk TITLE: "Predicting Evolution of Virus Emergence". Pathogen emergence occurs in many ways, but an overarching goal is to generally predict which pathogens are poised to emerge in the future. From an evolutionary-process perspective, this problem can be addressed by studying four interrelated topics that dictate success (or not) of pathogen emergence: evolvability, adaptability, constraint, and extinction. This talk emphasizes the power of experimental evolution to elucidate rules of pathogen emergence, especially in rapidly-evolving viruses. Also, the talk stresses why molecular biology and genomics can reveal crucial mechanistic details of experimental evolution, and why bridges to disciplines such as data/computer science and the participation of a diverse workforce are vital for studying emergence.
PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Opening Remarks, Welcome Statement, and Technical Background

(Georgia Institute of Technology, 2021-02-22) Panchanathan, Sethuraman ; Basu, Mitra ; Prakash, B. Aditya ; Yin, John ; Torrens, Paul M. ; Wigginton, Krista R.

In the last year, the ongoing COVID-19 pandemic has severely disrupted the livelihoods of our planet’s human inhabitants, infecting over 85 million individuals, and causing nearly 2 million deaths. What actions should have been taken to minimize the severity of this pandemic (and others before it in the past decades such as Zika, SARS and Ebola)? In retrospect, many actions could have played key roles: environmental monitoring for potential animal-to-human infection spillovers, establishment of pipelines for rapid vaccine development and optimal deployment and distribution, designing data science tools to accurately forecast trajectories, fast and adaptive syndromic surveillance and behavior tracking, designing and timing effective interventions, training susceptible individuals for measures needed to inhibit the spread of infectious agents, and others. What lessons have been learned and what gaps in our knowledge, methodologies, technologies, and policies remain?
PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 1, End-to-End Theme

(Georgia Institute of Technology, 2021-02-22) Marathe, Madhav ; Peters, Debra ; Ke, Ruian

Madhav Marathe - Plenary Talk TITLE: "Real-time End-to-End Pandemic Planning, Prediction and Response". The COVID-19 pandemic has brought forth the need for a sustainable capability for pandemic planning, response, and mitigation at various geographic, temporal and social scales. The social, economic, and health impact of the pandemic has been immense and will continue to be felt for decades to come. Since February 2020, our group has been providing local, state, and federal authorities continuous modeling and analytics support as they work assiduously to contain the pandemic. Based on this experience, I will describe the scientific and engineering challenges and opportunities in developing an end-to-end program to better prepare and respond to future pandemics and epidemic outbreaks.
Logical Neural Networks: Towards Unifying Statistical and Symbolic AI

( 2021-01-15) Gray, Alexander

Recently there has been renewed interest in the long-standing goal of somehow unifying the capabilities of both statistical AI (learning and prediction) and symbolic AI (knowledge representation and reasoning). We introduce Logical Neural Networks, a new neuro-symbolic framework which identifies and leverages a 1-to-1 correspondence between an artificial neuron and a logic gate in a weighted form of real-valued logic. With a few key modifications of the standard modern neural network, we construct a model which performs the equivalent of logical inference rules such as modus ponens within the message-passing paradigm of neural networks, and utilizes a new form of loss, contradiction loss, which maximizes logical consistency in the face of imperfect and inconsistent knowledge. The result differs significantly from other neuro-symbolic ideas in that 1) the model is fully disentangled and understandable since every neuron has a meaning, 2) the model can perform both classical logical deduction and its real-valued generalization (which allows for the representation and propagation of uncertainty) exactly, as special cases, as opposed to approximately as in nearly all other approaches, and 3) the model is compositional and modular, allowing for fully reusable knowledge across talks. The framework has already enabled state-of-the-art results in several problems, including question answering.
Challenges and Opportunities at the Nexus of Synthetic Biology, Machine Learning, and Automation

( 2020-11-13) Zhao, Huimin

Inspired by the exponential growth of the microelectronic industry, my lab has been attempting to build a biofoundry that integrates biology, automation and artificial intelligence (AI)/machine learning for rapid prototyping and manufacturing of biological systems for synthesis of bioproducts ranging from chemicals to materials to therapeutic agents. In this talk, I will discuss the challenges and opportunities at the nexus of synthetic biology, machine learning, and automation and highlight a few of our accomplishments and the recently launched NSF AI research institute for molecular synthesis. Specifically, I will introduce three interconnected stories, including: (1) development of the Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB) for next-generation synthetic biology applications; (2) development of genome-scale engineering tools for rapid metabolic engineering applications, and (3) integration of biocatalysis and chemical catalysis for synthesis of value-added chemicals, which necessitates the development of AI-enabled synthesis planning and catalyst design tools.
Collision Course: Artificial Intelligence meets Fundamental Interactions

( 2020-10-30) Thaler, Jesse

Modern machine learning has had an outsized impact on many scientific fields, and fundamental physics is no exception. What is special about fundamental physics, though, is the vast amount of theoretical, experimental, and observational knowledge that we already have about many problems in the field. Is it possible to teach a machine to “think like a physicist” and thereby advance physics knowledge from the smallest building blocks of nature to the largest structures in the universe? In this talk, I argue that the answer is “yes”, using the example of particle physics at the Large Hadron Collider to highlight the fascinating synergy between theoretical principles and machine learning architectures. I also argue that by fusing the “deep learning” revolution with the time-tested strategies of “deep thinking” in physics, we can galvanize research innovation in artificial intelligence more broadly.
Curating a COVID-19 data repository and forecasting county-level death counts in the United States

( 2020-10-23) Yu, Bin

As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead. Using data from January 22 to June 20, 2020, we develop and combine multiple forecasts using ensembling techniques, resulting in an ensemble we refer to as Combined Linear and Exponential Predictors (CLEP). Our individual predictors include county-specific exponential and linear predictors, a shared exponential predictor that pools data together across counties, an expanded shared exponential predictor that uses data from neighboring counties, and a demographics-based shared exponential predictor. We use prediction errors from the past five days to assess the uncertainty of our death predictions, resulting in generally-applicable prediction intervals, Maximum (absolute) Error Prediction Intervals (MEPI). MEPI achieves a coverage rate of more than 94% when averaged across counties for predicting cumulative recorded death counts two weeks in the future. Our forecasts are currently being used by the non-profit organization, Response4Life, to determine the medical supply need for individual hospitals and have directly contributed to the distribution of medical supplies across the country. We hope that our forecasts and data repository at this https URL can help guide necessary county-specific decision-making and help counties prepare for their continued fight against COVID-19.