Department of Computational and Data Sciences

Summer Speaker Series - Mason Online Pandemic Modeling Forum | GMU College of Science

Summer Speaker Series - Mason Online Pandemic Modeling Forum
https://science.gmu.edu/events/summer-speaker-series-mason-online-pandemic-modeling-forum-3

Summer Speaker Series - Mason Online Pandemic Modeling Forum | GMU College of Science Nonlinear Production Networks with an Application to the Covid-19 Crisis David Baqaee (UCLA) Emmanuel Farhi (Harvard)

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Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University

Xiaoyi Yuan
Bachelor of Arts, China University of Political Science and Law, 2013
Master of Arts, Georgetown University, 2015

Geo-Textual Data Analytics: Exploring Places and Their Connections

Monday, July 20, 2020, 10 a.m.

https://gmu.zoom.us/j/3859395896?pwd=YXBTSTJjOW02ekpvU2NjaGNCRHg2Zz09

All are invited to attend.

Committee:

Andrew Crooks, Chair
William G. Kennedy
Arie Croitoru
Andreas Züfle

Place is defined by physical, social, and economic activities and processes. Understanding the complexity of socially constructed places is a fundamental question in geography, sociology, and many other social sciences. Meanwhile, the growing amount of user volunteered geographic information (VGI) leads us to study place through a new perspective. For instance, Flickr users report local activities in various geographic locations that capture individualistic experiences and impressions of the locations. Many previous studies utilizing non-textual VGI have focused primarily on analyzing geographical footprints of places, which separated place from its meaning. This dissertation argues that the textual part of VGI provides us with unprecedented opportunities for deriving patterns of place meanings on an individual level. More specifically, three research questions are pursued in this dissertation. First, how to quantify placeness (i.e., place identities) that has been traditionally studied via theoretical and qualitative methods? Second, as place being innately interconnected, how can we assess connections between places in networks so that we can apply network science to analyze complex connections between places? Third, as geo-textual data can also reveal social events, how to trace critical events across places using geo-textual data? In order to answer these research questions, this dissertation leverages advances in machine learning, natural language processing and network analysis techniques on geo-textual data. By doing so this dissertation is able to build foundations for geo-textual data analytics and thus providing a new lens to study places and the connections between them from the bottom up. Overall, this dissertation showcases an interdisciplinary effort in computational social science research that combines computational textual data analytics and social scientific theories including human geography and sociology.

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Summer Speaker Series - Mason Online Pandemic Modeling Forum

Thursday, July 9, 2020, 5:00 p.m. - 6:30 p.m.

Warwick McKibbin, Professor and Director, ANU Centre for Applied Macroeconomic Analysis (CAMA) in the Crawford School of Public Policy at the Australian National University (ANU) pandemic: Global macroeconomic scenarios of the COVID-19 https://cama.crawford.anu.edu.au/publication/cama-working-paper-series/17010/global-macroeconomic-scenarios-covid-19-pandemic

The COVID-19 global pandemic has caused significant global economic and social disruption. In McKibbin and Fernando (2020), we used data from historical pandemics to explore seven plausible scenarios of the economic consequences if COVID-19 were to become a global pandemic. In this paper, we use currently observed epidemiological outcomes across countries and recent data on sectoral shutdowns and economic shocks to estimate the likely impact of COVID-19 pandemic on the global economy in coming years under six new scenarios. The first scenario explores the outcomes if the current course of COVID-19 is successfully controlled, and there is only a mild recurrence in 2021. We then explore scenarios where the opening of economies results in recurrent outbreaks of various magnitudes and countries respond with and without economic shutdowns. We also explore the impact if no vaccine becomes available and the world must adapt to living with COVID-19 in coming decades. The final scenario is the case where a given country is in the most optimistic scenario (scenario 1), but the rest of the world is in the most pessimistic scenario.

The scenarios in this paper demonstrate that even a contained outbreak (which is optimistic), will significantly impact the global economy in the coming years. The economic consequences of the COVID-19 pandemic under plausible scenarios are substantial and the ongoing economic adjustment is far from over.

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CSI PH.D. STUDENT PUBLISHED IN INTERNATIONAL CONFERENCE ON EDUCATIONAL DATA MINING FOR DOCTORAL CONSORTIUM TRACK

Ajay Kulkarni, Computational Sciences and Informatics Ph.D. student, received some very good news. His paper "Towards Understanding the Impact of Real-Time AI-Powered Educational Dashboards (RAED) on Providing Guidance to Instructors" was accepted for the EDM 2020: the 13th International Conference on Educational Data Mining scheduled for July 10-13, 2020. For additional information on the virtual conference details, go to: https://educationaldatamining.org/edm2020/"

CSI PH.D. STUDENTS ACCEPTED TO CARNEGIE MELLON UNIVERSITY, PITTSBURGH SUMMER SCHOOL

Congratulations to Computational Sciences and Informatics Ph.D. students, Ajay Kulkarni and Taylor Stevens, on their acceptance into Carnegie Mellon's virtual Simon Initiative LearnLab Summer School. Ajay and Taylor will be pursuing the Educational Data Mining (EDM) track. The focus of the summer school scheduled to run from July 27-31 will be on Educational Data Mining. For additional information on Carnegie Mellon's Summer School, visit: https://learnlab.org/index.php/simon-initiative-summer-school/

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Summer Speaker Series - Mason Online Pandemic Modeling Forum

Jun 26, 2020, 3:00 PM - 4:30 PM

Global Supply Chains in the Pandemic - Zhen Huo, Assistant Professor Economics Department Yale University

We study the role of global supply chains in the impact of the Covid-19 pandemic on GDP growth for 64 countries. We discipline the labor supply shock across sectors and countries using the fraction of work in the sector that can be done from home, interacted with the stringency with which countries imposed lockdown measures. Using the quantitative framework and methods developed in Huo, Levchenko, and Pandalai-Nayar (2020), we show that the average real GDP downturn due to the Covid-19 shock is expected to be −31.5%, of which −10.7% (or one-third of the total) is due to transmission through global supply chains. However, “renationalization” of global supply chains does not in general make countries more resilient to pandemic-induced contractions in labor supply. The average GDP drop would have been −32.3% in a world without trade in inputs and final goods. This is because eliminating reliance on foreign inputs increases reliance on the domestic inputs, which are also subject to lockdowns. Whether renationalizing supply chains insulates a country from the pandemic depends on whether it plans to impose a more or less stringent lockdown than its trading partners. Finally, unilateral lifting of the lockdowns in the largest economies can contribute as much as 6-8% to GDP growth in some of their smaller trade partners.

Andrei A. Levchenko, Professor, Economics
University of Michigan

Zhen Huo
Assistant Professor
Economics Department
Yale University

Nitya Pandalai-Nayar, Assistant Professor
Department of Economics
University of Texas at Austin

Barthélémy Bonadio, PhD Student
Economics
University of Michigan

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COVID-19 Health System Response Monitor

Mason Online Pandemic MODeling Forum : Reporting on policy responses in Norway - Ingrid Saunes, Norwegian Institute of Public Health

The European Observatory on Health Systems and Policies (E-OBS) supports and promotes evidence-informed health policy-making through analysis of the health-care systems in Europe. Their publication series Health Systems in Transition (HiT) describe each country’s health systems and continuously update them. During the COVID-19 pandemic policy responses are presented and analysed at a designated web-page https://www.covid19healthsystem.org/mainpage.aspx. The reporting cover areas from preventing transmission, ensuring sufficient physical infrastructure and workforce capacity, effective provision of health services, paying for health services, as well as governance and measures in other sectors. In addition there are topic specific collection of relevant information, as data reporting and coding.

This presentation will give a brief intro to the collaboration with E-OBS, before I present the timeline of the Norwegian policy responses, details on some aspects of the health system and policies, including information on the Covid-modelling in Norway, it’s current role and sampling of the E-OBS analysis.

https://science.gmu.edu/events/summer-speaker-series-mason-online-pandemic-modeling-forum-0

COVID-19 Health System Response Monitor The Health Systems and Policy Monitor is an innovative platform that provides a detailed description of health systems and provides up to date information on reforms and changes that are particularly policy relevant.

Mason Online Pandemic MODeling Forum
(MOP-MOD Forum)

Friday June 12, 2020 - 11:00 a.m. EST (4:00 p.m. UK time)

Production networks and epidemic spreading: How to restart the UK economy? Anton Pichler, University of Oxford

We analyze the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two-month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.

Anton Pichler is a PhD student in Mathematics under the supervision of Professor Doyne Farmer and Professor Cameron Hepburn. He is a member of the Complexity Economics group at the Institute of New Economic Thinking at the University of Oxford. His research interests encompass technological evolution, innovation and complex economic networks. In his thesis, Anton develops methods for technology forecasting which incorporate economic network data with a special focus on green energy technologies. Anton holds a BA in Political Science and a BSc in Economics, both awarded by the University of Vienna. He completed the MSc degree in Quantitative Finance at the Vienna University of Economics and Business.

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Mason Online Pandemic MODeling Forum
(MOP-MOD Forum)

The propagation of the economic impact through supply chains: The case of a mega-city lockdown against the spread of COVID-19

Hiroyasu Inoue and Yasuyuki Todo

Friday June 5, 2020 9:00 a.m. -10:30 a.m. EST

Abstract: This study quantifies the economic effect of a possible lock down of Tokyo to prevent spread of COVID-19. The negative effect of the lock down may propagate to other regions through supply chains because of shortage of supply and demand. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo when production activities that are not essential to citizens’ survival in Tokyo were shut down for a certain period. We find that when Tokyo is locked down for a month, the indirect effect on other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan, or 5.3% of its annual GDP. Although the production shut down in Tokyo accounts for 21% of the total production in Japan, the lockdown would result in a reduction of the daily production in Japan by 86% in a month.

Hiroyasu Inoue, Ph.D. in Informatics from Kyoto University, has been an associate professor at the Graduate School of Simulation Studies, University of Hyogo since 2015. From 2011 to 2012, he was a visiting professor at the Center for Complex Network Research at Northeastern University and from 2018 to 2019, he visited Kiel University as well. His research fields are complex systems, computational social science, artificial intelligence, and data science. His recent works mainly focus on large-scale simulations based on micro data of economies and use parallel computers.

Yasuyuki Todo, Ph.D. in Economics from Stanford University, has been a Professor at the Graduate School of Economics, Waseda University since 2014, after serving as the Department Head at the Department of International Studies, the University of Tokyo. He is also a Faculty Fellow at Research Institute of Economy, Trade and Industry. His research fields are international economics, development economics, and applied micro-econometrics, currently focusing on the role of social and economic network in economic growth and resilience based on firm- and household-level data from various countries. He has published more than 50 academic papers in refereed journals including Nature Sustainability, the Journal of Industrial Economics, the Journal of Regional Science, Ecological Economics, Research Policy, and World Development.

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Meeting number (access code): 161 706 3448
Meeting password: Ei49sHmB46a

Professor Robert Axtell, Computational Social Science Ph.D. Program, Department of Computational and Data Sciences, College of Science, will be hosting the Mason Online Pandemic Modeling Forum to be held weekly on Fridays throughout the summer, focusing on the effects of the SARS-CoV-2 virus and COVID-19 disease. The first session will be held on June 5th. Speakers from all over the world will discuss their models. For presenters from North America, we will try to stick to the usual 3:00-4:30 PM time but we will adjust the time from week-to-week for overseas speakers.

We look forward to seeing you online!

Additional details coming soon.

Congratulations to Ciara Sibley and Andrew Crooks for receiving the SpringSim 2020 Runner Up Paper Award for their work entitled: "Exploring the Effects of Link Recommendations on Social Networks: An Agent-Based Modeling Approach"

STUDENT SCHOLARSHIP AWARDED TO CDS 303-SCIENTIFIC DATA MINING STUDENT

Congratulations to David Joyner, a Biology major, who was awarded the College of Science Honorable Mention Scholarship for his project “Investigating the Relationship between Temperature and Spread of Sars-Cov2.” This project was one developed in Dr. Anamaria Berea’s CDS 303 course entitled “Scientific Data Mining.” With Dr. Berea’s encouragement and assistance with presentation and video, David submitted his project for consideration in the undergraduate research OSCAR presentations. Check out his project here:

https://gmu.studentopportunitycenter.com/2020/05/investigating-the-relationship-between-temperature-and-spread-of-sars-cov2/

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Research Colloquium on Computational Social Science/Data Science

May 15, 2020, 3:00 - 4:30 PM
The Economic Cost of COVID Lockdowns: An Out-of-Equilibrium Analysis

Antoine Mandel (Paris School of Economics, Université Paris I Panthéon-Sorbonne)

Vipin Veetil (Indian Institute of Technology Madras)

ABSTRACT: Estimates will be presented for the cost of the lockdown of some sectors of the world economy in the wake of COVID-19. We develop a multi sector disequilibrium model with buyer-seller relations between agents located in different countries. The production network model allows us to study not only the direct cost of the lockdown but also indirect costs which emerge from the reductions in the availability of intermediate inputs. Agents determine the quantity of output and the proportions in which to combine inputs using prices that emerge from local interactions. The model is calibrated to the world economy using input-output data on 56 industries in 44 countries including all major economies. Within our model, the lockdowns are implemented as partial reductions in the output of some sectors using data on sectoral decomposition of capacity reductions. We use computational experiments to replicate the temporal sequence of the lockdowns implement in different countries. China, Italy, Mexico, and France incur a high cost of the lockdowns as proportion of their GDP, while US, India, and Brazil are more moderately affected. World output falls by 7% at the early stage of the crisis when only China is under lockdown and by 23% at the peak of the crisis when many countries are under a lockdown. These direct impacts are amplified as the shock propagates through the world economy because of the buyer-seller relations. Supply-chain spillovers are capable of amplifying the direct impact by more than two folds. Naturally, the sustainability between intermediate inputs is a major determinant of the amplification. We also study the process of economic recovery following the end of the lockdowns. Price flexibility and minor technological adaptations help in reducing the time it takes for the economy to recover. The world economy takes about one quarter to move towards the new equilibrium in the optimistic and unlikely scenario of the end of all lockdowns. Recovery time is likely to be significantly greater if partial lockdowns persist.

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Jason Kinser Receives David J. King Award!

The Stearns Center and the Office of Faculty Affairs announce that the 2020 David J. King Teaching Award has been given to Dr. Jason Kinser, Associate Professor and Chair in the Department of Computational and Data Sciences.

The David J. King Award was created in 2002 and is named after Dr. King, a visionary teacher and leader who served as Vice President for Academic Affairs from 1982 to 1988. The award recognizes one faculty member each year for their lifetime achievement in enhancing teaching, learning, and overall educational excellence at George Mason University. Without question, Dr. Kinser lives up to the legacies of Dr. King and of prior recipients of this award. Among other achievements, the awards committee commends Dr. Kinser on his tireless support for and innovation in data sciences education at Mason, noting that he has taught over 40 courses with dedication and innovation, helped shepherd data sciences from a boutique program to a growing department, and provided leadership in the creation or renovation of three degree programs in the field.

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Research Colloquium on Computational Social Science/Data Sciences

May 8, 2020 3:30 pm - 4:00 pm

Classification of Synthetic Aperture Radar Images of Icebergs and Ships Using Random Forests Outperforms Convolutional Neural Networks.

William F. Lamberti (M.S., Statistical Science), Ph.D. Candidate in Computational Sciences and Informatics with a concentration in Data Science at George Mason University.

Synthetic aperture radar (SAR) is a common technique for capturing vessels and icebergs on the ocean surface. Convolutional Neural Networks (CNNs) are a popular approach to interpret classes captured in images which include ships and icebergs. However, CNNs are difficult to explain and are computationally expensive. In this paper, we built a random forest (RF) model which outperforms CNN based approaches by 7% and 11% on the testing and validation data, respectively. The RF model used interpretable metrics. These powerful metrics provide insight to what is important to distinguish the two classes from one another. Thus, despite noise present in the SAR images, the RF model was able to provide meaningful classifications between ships and icebergs.

William F. Lamberti (M.S., Statistical Science) is currently a Presidential Scholar and PhD Candidate in Computational Sciences and Informatics with a concentration in Data Science at George Mason University. His research interests include image analysis and computer vision, classification models in machine and statistical learning, small data problems, and explainable metrics and models. He has given talks on R at NASA Langley Research Center, George Mason University, and JSM 2019. He has also received teaching honors as the 2016-2017 Outstanding Graduate Teaching Assistant at George Mason University Statistics Department.

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Meeting number (access code): 613 433 014
Meeting password: C55pausVzG5

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Research Colloquium on Computational Social Science/Data Science

Dr. Robert Axtell - Economic Remedies Due to COVID-19
Friday, May 1, 3:00-4:30 p.m.

As this wave of SARS-CoV-2 infection and COVID-19 disease reaches its peak, questions abound concerning how best to lift stay-at-home and related physical and social distancing strictures. Specifically, with unemployment approaching levels not seen since the Great Depression and quarterly output well below a year ago, ways to ‘restart’ the economy are being widely discussed and, to some extent, modeled. This week I will lead a broad overview discussion of such models as Congress debates more economic remedies.

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