Please find HPC projects alphabetically by author or reverse-chronologically below.
- Police Performance: The Case of Mexico
- Boreal forest tree species classification using high-resolution fusion of hyperspectral imagery and lidar data
- Expanding Access to Data-Intensive Remote Sensing Algorithms through Collaboration with the SES Research Community
- Exploring usage for processing large point clouds into images
- Image Features That Are Essential to Estimate Mechanical Properties of Deformable Objects in Dynamic Scenes
- Large Truck Crashes & State Roadside Inspection Practices: A Spatial Analysis
- Data-Driven Multi-modal Fusion for the Analysis of Energetic Material Systems
- Genomics of a deep terrestrial subsurface nematode, Halicephalobus mephisto
- Transcriptome profiling of the amphipod Gammarus minus
- Molecular Modeling of Dopamine Detection by Cyclic Voltammetry
- What's So Spatial about Diversification in Nigeria?
- Harnessing the Power of Parallel Computing for Computer-aided Drug Discovery
- Impact of Marriage Quality on Self and Proxy Reporting of Agricultural Work
- Aquatic Resource Trade in Species (ARTIS) database development
- Merit Awards
Seth Gershenson, Alex Kevorchian
- Variational Genotyping for Polyploids
- Demographics in Law School
Seth Gershenson, Alex Kevorchian
- The Determinants of Student Retention at a Private Selective Post-Secondary Institution
- Teacher Quality, Student Behavior, and Student Achievement
- Smooth National Measurement of Public Opinion across Boundaries and Levels: A View from the Bayesian Spatial Approach
- A Sample of Bankruptcy Court Records, 1898-2005
Mary Eschelbach Hansen
- Military Policy and Civilian Occupational Segregation
- Trumpish Tweets: Discursive Learning in US Public Executives
- Teacher-Student Match and Student Disciplinary Outcomes in North Carolina
Cassandra Hart and Constance Lindsay
- The ACA In Colorado
- Non-equilibrium dynamics of quantum transverse-field Ising model
- Modern Value Chains and the Organization of Agrarian Production
Heath Henderson and Alan G. Isaac
- Cofactor Prevalence and the Prevalence of HIV
- Real Sector Shocks and Banking Sector Contagion
Alan Isaac and Dongping Xie
- The Effect of Spatial Dependence on the Empirical Likelihood
- The Great Recession and Child Welfare
Nicholas E. Kahn
- Cost of Providing Pre-Exposure Prophylaxis and Antiretroviral Therapy for HIV Prevention
- Spacecraft Attitude Motion Planning on SO(3) using Gradient-based Optimization
- Exporting Out of Agriculture: The Impact of WTO Accession on Structural Transformation in China
- Modeling Exposure-Response Relationships
- Milieu: Defining Racial Context with Geolocation Data
- Three Projects on Taxes, Revenue, and Budgets
- Scaling curves of statistical planning algorithms
- Research on Multivariate Probability Distributions
- Income smoothing and cyclone damage in India
- Informal Employment in Egypt: Learning from Modeling with Essential Heterogeneity
- Assessing the impact of inflation targeting in developing countries: A synthetic control approach
- Topological Data Modeling for Cyber Data Analytics
- Truth Table Generator in Python
- Remittances and Food Security in Developing Countries: A Nonparametric Bounds Analysis
- Analysis of Consumer Demand for Health-related Products using Nielsen Scanner Data
- Causal Effects of Dual-Language Immersion Access in Utah: Evidence from a Statewide Expansion
- Medium and Long Term Impact of Rainfall Shocks in and around Birth Year
- Fast Functional Integrals with Applications to Dynamical Systems
- Interval-Valued Data Estimation
- Sentiment Analysis Towards Gender in Twitter in Tanzania
- Multi-sensory Inference of Material Properties of Deformable Objects in Dynamic Scenes
- Effects of precipitation on crayfish population and fish community dynamics
- Solid Fuel Usage and Health: Evidence from a Panel Data Analysis
- Design a Deep Learning Method for Detection Abnormalities in Medical Images
Data-Driven Multi-modal Fusion for the Analysis of Energetic Material Systems
Zois Boukouvalas, CAS/Mathematics and Statistics
The goal of this project is to use advanced Machine Learning techniques and data from multiple sources, and of different nature, i.e. multi-modal, to design, prototype, and evaluate an end-to-end system for the synthesis, analysis, and handling of energetic materials. We are planning to bring together a multi-disciplinary team of faculty, subject matter experts, and students-with expertise in the areas of data science, machine learning, physics, and chemistry-to assist Energetics Technology Center (ETC) in support of rigorous verification and validation of the results that have been obtained thus far from previous effort. These efforts are based on work that ETC, American University, and University of Maryland has conducted over the past three years in a task called “Machine Discovery and Invention”.
Boreal forest tree species classification using high-resolution fusion of hyperspectral imagery and lidar data
Mike Alonzo, CAS/Environmental Science
Forest type (or "species") is an important component of boreal ecosystem structure and function. For instance, broadleaf species grow faster and have higher albedo than needleleaf species. While needleleaf species, are more likely to sit atop large stocks of soil carbon and burn regularly in wildfires. It is currently difficult to fully account for and impossible to map species composition in the Alaskan boreal forest due to impractical accessibility by field survey crews. Thus, NASA and US Forest Service are using an airborne remote sensing platform to capture spectral and structural information about the forest. This information has successfully been used to estimate forest biomass and tree heights. However, while it is a USFS priority, it has not been demonstrated that forest type can be accurately mapped. In this project we are using hyperspectral and lidar data from the airborne platform in a machine learning framework to classify individual trees across large spatial extents.
Military Policy and Civilian Occupational Segregation
Mary Hansen, CAS/Econ
I am studying the extent to which military policies impact civilian occupational choices. Specifically, I am interested in the impact of the end of the Women Army Corps (WAC) in 1978 and the passage Defense Officer Manpower Personnel Management Act (DOPMA) of 1980 on increasing women’s participation in male dominated occupations in the 1980s and 1990s. This research is motivated by a desire to understand the persistence of gendered civilian work.
Aquatic Resource Trade in Species (ARTIS) database development
Jessica Gephart, CAS/Environmental Science
Seafood is among the most highly traded foods and it is becoming increasingly globalized, with trade doubling in recent decades. Seafood globalization has coincided with a period of rapid transformation as production has shifted from wild capture to farming. At the same time, seafood is now widely recognized as a critical source of nutrition, particularly for coastal developing nations. Thus, social and environmental threats to local seafood production, including environmental extremes, price impacts of market integration, networked risks, and increased availability of processed foods, must be evaluated in the context of global trade. These issues are paralleled by similar questions for other natural resources and are central to global human-environmental systems research. However, our collective understanding of the environmental and human outcomes of natural resource globalization is limited by a fundamental gap between production and trade data. We bridge this gap in the proposed Aquatic Resource Trade in Species (ARTIS) database by providing the first global estimates of seafood species trade flows from 1994-2018.
Income smoothing and cyclone damage in India
Stefanie Onder, SIS
Cyclones are among the most devastating natural disasters, especially for farmers in developing countries. This paper studies the coping strategies of Indian farmers in the absence of an insurance mechanism against extreme weather events. I exploit the interaction between the random timing of the cyclone hit and the district-level growing season to isolate the changes in output driven by the farmers’ behavioral adaptation from the cyclone’s damages. I find that farmers partially offset the losses in output by increasing the area planted and changing planting more resilient and nutritious crops. This smoothing strategy is pursued on average for 3 years.
Variational Genotyping for Polyploids
David Gerard, CAS/Mathematics & Statistics
Most polyploid genotyping methods assume common features such as allele bias or overdispersion of the data (i) are known a priori or (ii) are estimated jointly with the prior distribution. The former approach is undesirable as it places the onus on the researcher to make sure there are no biases in their data. The latter approach makes results more unstable to deviations from modeling assumptions. In this project, we will study integrating over the uncertainty in likelihood-specific parameters while estimating the prior-specific parameters. We approach this by applying a variational approximation using the Stan probabilistic computing language.
Scaling curves of statistical planning algorithms
Mark Nelson, CAS/Computer Science
Investigate how performance of standard statistical forward planning algorithms (e.g. Monte Carlo Tree Search) scales with computational resources given to an agent, using games as benchmark domains.
Causal Effects of Dual-Language Immersion Access in Utah: Evidence from a Statewide Expansion
Jennifer Steele, SOE
This study leverages a statewide scale-up of dual language immersion (DLI) education to estimate the causal effects of students’ increased access to immersion on their academic achievement in reading, mathematics, and science, all of which are tested in English. Whereas most prior studies of dual language immersion education have analyzed, descriptively or causally, the effects of enrollment in established programs, the current study examines the effects of DLI program launch and expansion within schools on the future performance of all students entering those schools as kindergarteners. We leverage statewide expansion of DLI across six districts in Utah to examine how student performance changes within schools as immersion becomes increasingly available over time. Contingent on school populations and instructional effectiveness being consistent over time, this strategy allows us to disentangle language-of-instruction effects from stable effects of schools that offer immersion.
Design a Deep learning method for detection abnormalities in medical images
Dauphin Yohan, CAS
Recently, deep learning has been applied on medical images, for example on automated lesion detection. In this field, building an effective architecture for neural networks is a challenge. An adequate model can assist radiologists, saving time and reducing cost in the analysis of X-rays. This project goal is to build proper models for lesion detection on medical images of the whole body using deep learning. More specifically, the first analysis will be done on Chest X-Ray images. All proposed methods will be evaluated experimentally on a comparison with the state-of-the-art. We expect to obtain models with better predictive performance and more reliable.
Smooth National Measurement of Public Opinion across Boundaries and Levels: A View from the Bayesian Spatial Approach
Jeff Gill, MATH/STAT
We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial hierarchical modeling using multiple geocoded data sources. This is done with a model-based smooth density blanket for the concept of interest (primarily ideology here) such that arbitrary geographic boundaries are immaterial. We do this with a Bayesian hierarchical model that uses kriging to analyze geocoded survey responses, and we use this analysis to forecast areal units. By exploiting the spatial relationships among observations and units of measurement and point-to-block realignment calls, we extract measurements of ideology as geographically narrow as available covariates We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial hierarchical modeling using multiple geocoded data sources. This is done with a model-based smooth density blanket for the concept of interest (primarily ideology here) such that arbitrary geographic boundaries are immaterial. We do this with a Bayesian hierarchical model that uses kriging to analyze geocoded survey responses, and we use this analysis .
Effects of precipitation on crayfish population and fish community dynamics
Richard Walker, ECON
Factors associated with climate drive population and community dynamics. In rivers, precipitation can have a strong influence on surface flows, the availability of habitat, and water quality. Years with low precipitation can lead to reductions in surface flows and water quality relative to years with more precipitation. Intermittent rivers, rivers that cease to have surface flow during parts of the year, are particularly influenced by changes or variation in hydrology (i.e., rain, snowfall). Using daily precipitation data, I plan to link variation in annual precipitation to patterns in crayfish population and fish community dynamics. Crayfish and fish data were collected from 23 isolated and connected pools between 2009 to 2011. Daily precipitation data files were downloaded from Daymet for North American (1980 to 2017).
Trumpish Tweets: Discursive Learning in US Public Executives
Bill Harder, CTRL
This project adopts a machine learning approach that employs natural language processing to analyze the "Trump-ishness" of U.S. governors' Twitter discourse.
Gender and Job Search in India
Gregory Lane, CAS/Econ
The goal of this work is to understand trends in Indian labor market and determine the extent to which job portals can facilitate the job-search for workers in India, especially for women. Work done by Naukri and other leading companies suggest that there are significant matching frictions in the Indian labor market, and we would like to further understand the extent to which this is driven by 1) job-seekers preferences (for salary, location, benefits) versus 2) companies having preferences with candidate characteristics (e.g. gender) that do not translate into good job performance.
Exploring Usage for Processing Large Point Clouds into Images
Michael Alonzo, CAS/ENVS
Measure shrub biomass in Alaska using massive point clouds generated from overlapping imagery using structure from motion techniques.
Medium and Long Term Impact of Rainfall Shocks in and around Birth Year
Tejesh Pradhan. Faculty Sponsor: Dr. Jessica Leight, CAS/Econ
Epidemiological literature suggests that in utero and extrauterine environment has important implications for development of infants into adulthood. To test this hypothesis, this paper studies the persistent impact of early-life weather conditions on adult socioeconomic outcomes in rural Nepal. Using a multilevel mixed model that allows for correlation within birth cohorts, preliminary results indicate that rainfall shocks reduce future productivity and earnings from education, especially for females.
Spacecraft Attitude Motion Planning on SO(3) using Gradient-based Optimization
Dennis Lucarelli, CAS/Physics
Attitude motion planning is necessary in mission scenarios in which the spacecraft must perform large angle maneuvers with the additional requirement that sensitive instruments must not point to bright objects such as Sun, Moon, and Earth. These so-called "keep-out cones" define state constraints that must be satisfied along the instrument trajectory. This research presents a control synthesis method for constructing an appropriate control torque that achieves the desired rest-to-rest maneuver and ensures that the keep-out cones are avoided.
Molecular Modeling of Dopamine Detection by Cyclic Voltammetry
Hanning Chen, CAS/Chemistry
Dopamine serves as a critical neurotransmitter in human brains. The detection of trace-amount dopamine is thus highly desired for early-stage diseases diagnosis. Using hybrid quantum mechanics/molecular dynamics simulations in conjunction with image-charge method, the binding affinity of dopamine to functionalized micro-electrodes will be investigated to facilitate the systematic design of ultra-fast neural sensors.
Solid Fuel Usage and Health: Evidence from a Panel Data Analysis
Rui Wang, Independent Graduate Research Student
After decades of economy development, China still has more than 700 millions of its citizens living under exposure to high levels of indoor air pollution because of solid fuel using. Currently, there is a lack of credible evidence on the causal impacts of solid fuel usage on other health outcome especially on the context of low-to middle-income countries. Using data from China Nutrition and Health Survey (CNHS), this paper studies the health impact of in-home solid fuel usage on Chinese working age population in. Using a fixed effects model, this paper estimates for each additional year's usage of solid fuel for cooking is associated with a 1.6% (p=0.007) higher chance of having hypertension. Each addition years of solid fuel usage for cooking is also associated with a 0.36 mm Hg (p=0.11) increase in diastolic blood pressure and a 0.50 mm Hg (p=0.16) increase in systolic blood pressure. These findings suggest that solid fuel usage may increase cardiovascular disease risk among Chinese working population. However, I find no different health effects of in-home solid fuel usage on the cooking individuals at home.
The ACA In Colorado
Michael Hatch, DPAP/SPA
This paper focuses on relationship between the implementation of the ACA in Colorado and health care access and utilization and health using data from the Colorado Health Access Survey (Colorado Health Institute, n.d.). Of interest is the change in access to and use of health care services, and self-reported health status of state residents pre- and post-ACA and the heterogeneity of the ACA effect by salient demographic, geographic and socio-economic characteristics. As a validity check, I will also run an analysis of CO and its border states, using data from the Behavioral Risk Factor Surveillance System.
Exporting Out of Agriculture: The Impact of WTO Accession on Structural Transformation in China
Jessica Leight (CAS/Econ)
The rapid expansion of manufacturing exports from China has dramatically reshaped the economies of the U.S. and Europe over the last twenty years. However, there is still relatively little evidence regarding the effect of this export expansion on structural transformation in China itself. Utilizing a newly assembled panel including approximately 2,000 counties between 1996 and 2013, this paper provides new evidence of the effect of positive shocks to the export sector generated by China's accession to the World Trade Organization (WTO) in 2001 on employment, output, and value added in agriculture, manufacturing, and services at the county level.
Heterogeneity of Gains from Commercialization
Natalia Radchenko (CAS/Econ)
Cash crops are promoted by governments and development organizations because they have the potential to increase income, growth, employment, external balances and reduce poverty. Yet, many farmers do not switch to cash crops from staple production despite these potential gains with barrier to entry often cited as the reason. This paper reignites the debate on the reasons for cash crop adoption using a model with essential heterogeneity and a semi-parametric estimation technique which allows for an in-depth exploration of the returns from planting cash crops versus only food crops in terms of both observable and unobservable household characteristics. In essence, it relates the distribution of the potential gains from cash cropping to the likelihood of land allocation towards nonfood crops, which allows testing the comparative advantage mechanism as a driving force underlying the farmer's choice.
The empirical application is run using data from Malawi where farmers face a choice between staple crops and cash crops production. The results show considerable heterogeneity in harvest value returns to cash cropping both within and between groups of farmers choosing different crop portfolios. Comparative advantage considerations influence crop decisions of households: farmers self-select into the activity where they expect higher gains and adopt cash crop when facing weaker market barriers.
Paul Corral, Natalia Radchenko, and Paul Winters, "Heterogeneity of Commercialization Gains in Rural Economy," resubmitted
Remittances and Food Security in Developing Countries: A Nonparametric Bounds Analysis
Michael Smith, Independent Research Student (CAS/Econ)
with Maria Floro (CAS/Econ)
Despite food security being a key policy priority for aid organizations and governments around the world, the effects of remittances on food security in developing countries is largely unknown. Identifying the causal impact is made difficult by the endogenous selection into receiving remittances and the potential mismeasurement of true remittance receipt. Using data from the 2014-2015 waves of the Gallup World Poll, and the first global experiential measure of food security, we use a non-parametric bounds analysis to estimate upper and lower bounds on the causal effect of receiving remittances on individual-level food security in 52 developing countries. Under relatively weak monotonicity assumptions, we show that remittances significantly increase the probability of being food secure in developing countries. These results demonstrate that coordination between the international migration and food security policy agendas is critical and that remittance policies can and should be used as a tool to fight food insecurity in the developing world.
The Great Recession and Child Welfare
Nicholas E. Kahn
The objective of this project is to estimate the effects of foreclosure, unemployment, and their interaction on maltreatment and foster care placements.
Expanding Access to Data-Intensive Remote Sensing Algorithms through Collaboration with the SES Research Community
Michael Alonzo, Assistant Professor, ENVS/CAS
Protected ecosystems, temperate zones, and wealthier countries receive a disproportionate amount of research attention. Fragmented landscapes, tropical biomes, and developing countries, meanwhile, are routinely overlooked despite their greater coverage of the Earth's surface, extreme biodiversity, and critical ecosystem services. Satellite remote sensing can be used to bolster understanding of socio-environmental systems within understudied regions. However, persistent cloud cover, particularly in the tropics, limits the utility of satellite-based landscape monitoring. Moreover, the socio-environmental research community, trained in field-based or qualitative methods, have rarely adopted remote sensing approaches due to data/computational complexity as well as traditional disciplinary isolation. In the proposed Pursuit, we seek to overcome these long-standing, enviro-climatic and disciplinary limitations by convening a diverse group of interdisciplinary, socio-environmental researchers with ongoing projects in understudied regions. We have developed a cloud-resilient remote sensing algorithm ("NITA") to distill dense time series of satellite imagery into metrics of protracted (e.g., drought) and acute (e.g., forest clearing) land-cover change. Socio-environmental researchers have voiced interest in implementing NITA but presently it is only available as a Matlab prototype and may not optimally satisfy the needs articulated by this diverse community. Thus, we propose this data-intensive SESYNC Pursuit to: 1) Refine NITA through collaboration with the SES community and SESYNC data scientists; 2) Broaden the reach of the algorithm by transitioning to open-source code; and 3) Implement NITA with invited participants to augment research coverage in difficult-to-access areas.
Truth Table Generator in Python
Our artificial intelligence project is focused on the visualization of truth tables and the evaluation of truth expressions. It currently uses threading and parallelism processes to evaluate the expression and determine the values. Our project requires data collection that can determine the usefulness, speed and performance of the program itself and whether or not it can be improved.
Assessing the impact of inflation targeting in developing countries: A synthetic control approach
The objective of this paper is to measure the impact of inflation targeting (IT) on real GDP per capita in developing and emerging countries. To that end, we apply a methodology borrowed from the impact evaluation literature, the synthetic control method (SCM). This technique allows us to conduct data-driven comparative studies at the country level by comparing the macroeconomic trajectory of a given IT country with that of a combination of non-IT economies, i.e. the synthetic control. More precisely, the SCM provides an algorithm that finds a convex combination of comparison units, such that the chosen combination best resembles the values of predictors of the outcome variable for the affected unit before the adoption of IT. After constructing the synthetic control, we can simulate the evolution of GDP per capita in each targeting country, had it not implemented IT, and compare it to the actual outcome, thereby inferring the impact of the policy shift. Evidence on the impact of IT is mixed. Evidence on the impact of IT on output is especially mixed. In Chile, Ghana, and Peru, real GDP is higher than that of their respective synthetic controls after the adoption of IT, suggesting a positive impact of IT. However, the opposite is found in other countries, such as Mexico and Guatemala, and inconclusive results are found for various other countries.
Analysis of Consumer Demand for Health-related
Products Using Nielsen Scanner Data
This project uses weekly scanner data on sales of health-related products to study how consumers value product attributes and respond to new information on them. Nielsen Retail Scanner data are made available to academic researchers via the University of Chicago's Kilts Center for Marketing. The data consist of weekly pricing, volume, and store-level information for 2.6 million individual products grouped into 1,100 categories, from 2006 through the present. Data are collected via point-of-sale scanner systems from 90+ retail chains with outlets in 52 market areas across the U.S. The project will use both traditional demand-system and hedonic methods of estimating how consumers value attributes of products that are or may be subject to regulation by the U.S. Food and Drug Administration. Specific applications include: valuing menthol and nicotine as attributes of combusted cigarettes, assessing the extent to which prices of dietary supplements correlate with third-party quality ratings, and estimating consumer surplus from weight-loss products.
Sentiment Analysis Towards Gender in Twitter in Tanzania
I will be doing a text mining project in R for a Big Data Analytics course. I will be using the Rtm module to look at popular sentiment regarding gender in Tanzania for a five-week period in December 2015 through February 2016.
Seth Gershenson, Alex Kevorchian
The goal of merit awards is to attract talented students to campus, yet little is known about the efficacy of such awards and how they affect student behavior, decision making, and performance once on campus. The current study begins to fill this gap.
Demographics in Law School
Seth Gershenson, Alex Kevorchian
The project will address the question of whether the demographic match between law school instructors and students affects students' course grades, future course-taking, or specialization decisions.
Impact of Marriage Quality on Self and Proxy Reporting of Agricultural Work
Existing surveys of agricultural work performed by women in Sub-Saharan Africa may underestimate the hours of work performed by women. This is primarily due to the fact that, quite often, estimates of the work performed rely on reporting by household heads, most often males. This research is based on the surveys conducted in Ghana, which contain both self and proxy reports on the hours of work performed. It is our goal to examine the differences between self and proxy reports, and to examine how quality of marriage, measured by responses of men and women in each household, impact the reporting of work.
Topological Data Modeling for Cyber Data Analytics
We will explore and demonstrate the significance of mathematical methods based in topological data modeling (TDM) for analyzing big data with high complexity. We will ground our demonstrations in cyber applications and data sets, and address the scalability of TDM, both in terms of data volume and complexity, amongst the first to do so on these exciting new methods.
Invited talk, "Local Topological Analysis of Complex Systems." Joint Mathematics Meetings Special Session on Sheaves in Topological Data Analysis, Atlanta, GA. January 4, 2017.
Invited talk, "Modeling and analyzing sensor data topologically." Morgan State University Mathematics Colloquium, Baltimore, MD. October 13, 2016.
M. Robinson, "Sheaf and cosheaf methods for analyzing multi-model systems," (accepted book chapter, to appear) arXiv:1604.04647
M. Robinson, M. Stein, H. Owen, "Tracking before detection using partially ordered sets and optimization," AU-CAS-MathStats Technical Report No. 2015-5.
Cost of Providing Pre-Exposure Prophylaxis and Antiretroviral Therapy for HIV Prevention
Jelena Kmezic (PhD Candidate CAS/Econ)
The guiding research question is: at what cost can antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) treatment be used to drive HIV prevalence in sub-Saharan Africa toward zero? To answer this my dissertation develops an agent based model (ABM) of the spread of HIV building on existing models of concurrent sexual behavior and explores the role played by two core groups, commercial sex workers and mining sector workers, in presence of ART and PrEP treatment. Model shows that high-risk groups do not add significantly to the HIV epidemic and that proper treatment provision may lead to HIV extinction.
Cost of Providing Pre-Exposure Prophylaxis and Antiretroviral Therapy for HIV Prevention: Calibration of an Agent Based Model for Sub-Saharan Africa, Summer 2017 (expected)
Agent Based Model of ART and PrEP Treatment Efficacy in Sub-Saharan Africa Presentation
CHRS Seminar Fall 2016
Non-equilibrium dynamics of quantum transverse-field Ising model
In this project, we numerically study the steady state of a transverse-field Ising model under dissipation. Prior work suggests that the combination of the dissipation and transverse-field leads to a non-equilibrium state whose properties resembles a classical Ising model under the influence of thermal fluctuation. An effective temperature is derived based on the dissipation, interaction and transverse field parameter of the model. With the quantum Monte Carlo method, We numerically calculate various quantities of the model to test the validity of the theoretical prediction and evaluate the effective temperature in various parameter regions.
Anzi Hu and Mohammad Maghrebi, "Non-equilibrium dynamics of a driven Ising model coupled to a dissipative bath," American Physics Society's March Meeting 2017.
Police performance: The Case of Mexico
This dissertation examines the effect of exogenous factors on police performance at the municipality level in Mexico. The influence of factors outside the control of police organizations affect their overall performance and thus, their ability to provide efficient public services and, in turn, spending far more resources to provide the same level of service. The author employs frontier methods to analyze police performance. In particular, the author employs partial non-parametric frontier methodologies like order-m and oder alpha to estimate the efficiency scores. The scores are then regressed against a set of exogenous variables to analyze their effects on police performance.
Image features that are essential to estimate mechanical properties of deformable objects in dynamic scenes
Humans visually estimate material properties of objects (is the sweater heavy or light?) when deciding how to interact with them (e.g. grip strength).Nonetheless, very little is known about how humans estimate mechanical properties from visual information. Lack of this knowledge prevents further understanding on how material perception is used by the visual system for action planning. Our goal is to seek image features that allow humans to estimate mechanical properties of deformable objects in dynamic scenes. We aim to solve this problem using three main strategies: (1) Create high- delity cloth simulations using physics engine in graphics software. (2) Manipulate image cues and use psychophysical experiments to systematically measure material perception. (3) Build a deep neural network to learn image features that can be used to estimate material properties.
Teacher-Student Match and Student
Disciplinary Outcomes in North Carolina
Cassandra Hart and Constance A. Lindsay (SPA/Public Administration and Policy)
Many studies have detailed the significant negative relationship between student disciplinary actions and subsequent student outcomes (e.g.: Skiba, Michael, Nardo, and Peterson, 2002). Fewer studies have empirically assessed the role of teacher characteristics in determining student disciplinary actions; teachers serve as gatekeepers who report the severity and frequency of disciplinary actions. Given research that indicates that teacher race and gender can matter for minority students, this gatekeeper role may have implications for minority students' outcomes (Dee, 2005; Clotfelter, Ladd & Vigdor, 2006; Bates & Glick, 2013). In particular, same race and/or same gender teachers may report student disciplinary actions differently. The racial composition of the teaching workforce is vastly mismatched to the current public school student population. Therefore, exploring this critical relationship between teacher demographic characteristics and student discipline outcomes is essential. This paper proposes to model predictors of student disciplinary actions, based on teacher characteristics, and their interactions with student characteristics.
This study uses statewide administrative data from North Carolina to address these research questions. North Carolina offers roughly 20 years of data where students can be matched to individual teachers at the classroom level. The state also collects disciplinary records for each student, including offenses committed by students and whether expulsion/suspension consequences are attached to each disciplinary incident. The state includes demographic data for both students and teachers. This is crucial in order to identify the effects we study here.
We use multiple strategies to identify the effects of teacher-student demographic match on student outcomes. First, following past researchers (Dee, 2005) we will conduct student fixed effects comparisons that uncover whether students are disproportionately likely to face disciplinary consequences in years (K-5) when their home classroom teacher is of a different race/sex group from them, compared to when they share their teachers' characteristics. We will supplement these analyses with a second, novel strategy in this literature: identifying match effects off of twin differences. This strategy will take advantage of the fact that until the mid-2000s, North Carolina state policy encouraged that twins be assigned to separate classes. In some cases, this will mean that one twin is assigned to a same-race (gender) teacher while another twin is assigned to a different-race (gender) teacher. Exploring whether these different assignments are then associated with different discipline rates provides a strong alternative estimation of match effects.
To explore these issues at the secondary level, we adapt an analytic strategy used in past literature on the effects of students' being matched to tenured vs. adjunct college instructors (Bettinger & Long, 2010). This strategy uses deviations from the long-term trends in the race/sex composition of faculty at a given school-grade level to determine whether, for instance, male students have lower disciplinary incident rates in years when a higher share of teachers in their specific school-grade combination are male compared to the historical average.
These analyses should have important implications for debates over the importance of diversifying the teaching workforce.
Lindsay, Constance A. & Cassandra M.D. Hart Teacher-Student Match and Student Disciplinary Outcomes in North Carolina, APPAM 2015.
Lindsay, Constance A., and Cassandra MD Hart. "Teacher Race and School Discipline." Education Next 17, no. 1 (2017).
Lindsay, Constance A. & Cassandra M.D. Hart. "Teacher-Student Race Match and Student Disciplinary Outcomes for Black Students in North Carolina," forthcoming in Education Evaluation and Policy Analysis.
Huffington Post, November 1, 2016
EdSource, November 1, 2016
Education Week, November 1, 2016
Politico, November 1, 2017
What's So Spatial about Diversification in Nigeria?: Projects in the Spatial Economics in Agriculture
Paul Corral (Graduate Student) and Natalia Radchenko (CAS/Econ)
As time passes the importance of income diversification, even within farm households, is increasingly being noted. The belief that farm households rely mostly on own production in order to achieve food security has given way to the perception that diversification is the norm (Barrett et al., 2001; Davis et al., 2009). In fact some authors (Davis et al., 2009) suggest that in most countries most households diversify, and the majority of household income comes from off-farm activities. While several studies have been carried out which have found the determinants to household choice to diversify, no study to date has taken into account spatial dependence or the "neigborhood effect." Ignoring spatial relations may lead to biased or inconsistent estimates (Holloway et al., 2002). Thus taking into account spatial dependence could aide policy makers in order to ensure that resources are directed to areas where these would yield the highest returns.
Paul Corral and Natalia Radchenko, " What's so spatial about diversification in Nigeria?" resubmitted
Paul Corral and Natalia Radchenko, "Agricultural commercialization and food security in rural
economies: Malawian experience," Journal of Development Studies, 2017, forthcoming.
Transcriptome profiling of the amphipod Gammarus minus
David Carlini (CAS/Biology)
The cave realm is an ideal system for studying the evolution and genetics of regressive morphological traits. Gammarus minus, a freshwater amphipod living in the cave and surface streams in the eastern United States, provides an opportunity to examine the evolution of troglomorphic (= cave specific) traits. In G. minus, multiple pairs of genetically related, physically proximate cave and surface populations exist which exhibit an astounding degree of intraspecific morphological divergence. The morphology, ecology, and genetic structure of these populations are well characterized, yet the genetic basis of morphological divergence remains unknown. RNA-Seq data from one pair of morphologically distinct sister populations inhabiting surface and cave habitats was collected (four individuals from each population) and used to identify genes associated with the evolution of troglomorphic traits. Candidate genes were identified as those exhibiting either large difference in expression level or in their rates of evolution between cave and surface populations. Genes identified to be of interest vis-Ã -vis the RNA-Seq results will then be characterized more extensively at the population level through DNA sequencing and through quantitative analysis of gene expression. Population level characterization will include at least one additional cave/surface pair of populations, with convergent patterns of loss-of-function or gain-of-function mutations in cave populations to be taken as strong evidence of adaptive evolution.
Carlini DB, Fong DW. Submitted to PLoS One. The transcriptomes of cave and surface populations of Gammarus minus (Crustacea: Amphipoda) provide evidence for positive selection on cave downregulated transcripts.
Carlini DB. 2016. Speleotranscriptome profiling casts light on differential expression and polymorphism in cave and surface populations of the amphipod Gammarus minus. 2016 International Conference on Subterranean Biology. Fayatteville, AR. Refereed.
Carlini DB. 2015. Transcriptome profiling of cave and surface populations of the amphipod Gammarus minus. Annual Meeting of the Society for Molecular Biology and Evolution (SMBE). Vienna, Austria. Refereed.
Carlini, DB., Fong, D., "Molecular genetic variation among Lirceus usdagalun, L. culveri, and L. hargeri populations using next generation sequencing methods," United States Fish and Wildlife Service, Federal. Received $33,000. Awarded November 1, 2016.
Carlini, DB., "Testing for parallel evolution in Gammarus minus cave populations using whole transcriptome data.," Cave Conservancy of the Virginias, Private. Received $13,664. Awarded April 18, 2016.
Carlini, DB., "Transcriptome profiling of the cave amphipod Gammarus minus: the genetic basis and evolution of troglomorphic traits," The Cave Conservancy of the Virginias, Private. Received $15,720.
Multi-sensory inference of material properties of deformable objects in dynamic scenes
Bei Xiao (CAS/Computer Science)
Humans are good at recognizing objects as well the materials they are made of. Most previous research has focused on understanding optical properties from static images. We want to understand how humans perceive mechanical and tactile material properties of objects in dynamic scenes by observing how objects interact with external forces. Our long-term goal is to develop a computer vision system to recognize and classify material properties from real videos and images. To achieve our goals, we use computer graphics software to simulate videos that contain dynamic behavior of objects in complex scenes with physical-realistic dynamics. The fast development of computer graphics and physical simulation allow realistic rendering of deformable objects in dynamic scenes. Such stimuli also allow us to systematically isolate scene and image parameters and figure out the mapping between perception and physics. The dataset can also be used as training dataset to train algorithms to learn relevant features and classify and recognize materials in images and videos that don't have ground truth data. In addition, we also aim to facility computer rendering user-interface by identifying important perceptual dimensions of mechanical and tactile attributes. Currently, the computer capacity in the lab is limited. Hence, my team and I are planning to use the cluster to batch render our stimuli.
Bi, W., Xiao, B., Jain, E., & Joerg, S. (2016, July). Perceptual constancy of mechanical properties of cloth under variation of external forces. In Proceedings of the ACM symposium on applied perception (pp. 19-23).
Bermudez, L., & Xiao, B. (2016). Estimating material properties of cloth from dynamic silhouettes. Journal of Vision, 16(12), 630-630. VSS, 2016.
Perceptual constancy of mechanical properties of cloth under variation of external forces. Workshop on Perceptual Representation of Illumination and Materials, Prism 6, 2016, Schloss Rauischholzhausen
Beyond translation: Image deformation and dynamics in material and shape perception, Bei Xiao will present a talk: Perception of material properties of deformable object in dynamic scenes, European Conference on Visual Perception, August, 2017, Berlin.
External Grant submission
NSA CompCog: Invariant perception of material properties across dynamic scenes, Feb 1st, 2017
Real Sector Shocks and Banking Sector Contagion
Alan G. Isaac and Dongping Xie (both CAS/Econ)
The goal of this project is to develop a network model of financial contagion in the presence of a real sector. We add a non-bank corporate sector to a model of the banking sector based on Gai, Haldane, Kapadia (2011). We use agent-based methods in order to identify firm-level and bank-level heterogeneities that play a crucial role in the propagation of liquidity problems. Agents in the non-bank corporate sector need to borrow from financial intermediaries. We show that the interactions between the two sectors can generate and propagate financial fragility. We uncover a tendency for the banking sector to become increasingly concentrated over time.
Eastern Economic Association 40th Annual Conference, Boston, MA. 8 March 2014.
The Graduate Workshop at Johns Hopkins University, Baltimore, MD. July, 2014.
Thesis or dissertation
Three Essays on Monetary Policy Transmission and Banking Crises, defended December 2016
Genomics of a deep terrestrial subsurface nematode,
John Bracht (CAS/Biology)
The surprising discovery of a nematode from the deep terrestrial subsurface, Halicephalobus mephisto, fundamentally altered our perception of the adaptability and ecological range of metazoa. This finding has profound implications for our understanding of the evolution of life on earth and the search for life on exoplanets. In this project, we will assemble the genome of this organism and determine the mechanisms by which it adapted to an extreme environment containing very little O2, high pressure, and elevated temperature. Halicephalobus mephisto is a moderately thermophilic (35-41 °C), parthenogenetic, bacteriofagous nematode discovered within a fluid-filled fracture, 1.4 km beneath the surface in South Africa. The high pCH4 (2.5 bars), high sulfide (~1 ppm) and low pO2 (0.07 bars) present significant environmental challenges to this aerobic metazoan. Nematodes are notoriously adept at adapting to novel environments, having explored a wide range of free-living, zooparasitic, or phytoparasitic environments. The process of environmental adaptation and selection leaves a lasting imprint on genomes, giving us a powerful approach to study the adaptation of this metazoan to its extreme environment.
Used by 13 students in Bracht's Computational Genomics course in Fall 2016
Allen, S. and Bracht, JR. "The genome of a nematode isolated from the deep, hot terrestrial subsurface reveals horizontal transfer and amplification of Hsp70 genes as an adaptive strategy." July 5, 2016. Gold Coast, Australia. Poster.
Bracht, JR. "The subterranean genome of the devil worm." October 7, 2016, American University CTRL HPC seminar series.
DC NASA Space Grant Consortium, AU STEM Faculty Summer Research Program, Investigating the Limits of Life: Genomics of Complex Life in the Deep Terrestrial Subsurface, Summer 2016.
Allen S, Magnabosco C, Borgonie G, Erasmus M, Van Herdeen E, Onstott T, Sebra B, Deikus G, Goldman A, Onstott T, Bracht, JR. The genome of a subterrestrial nematode reveals horizontal transfer and gene family expansion as adaptive strategies.
Milieu: Defining Racial Context with Geolocation Data
Ryan Moore (SPA/Government)
Across disciplines, scholars strive to better understand individuals' milieus €"the people, places, and institutions individuals encounter in their daily lives. In particular, political scientists argue that racial contexts shape individuals' attitudes about candidates, policies, and people of various races and ethnicities. Yet the current standard of measuring milieus is to place survey respondents in one or two geographic containers like counties or census tracts and then to ascribe all of that container's characteristics to the individual's milieu. Using a new dataset of over 2.6 million GPS records from over 400 individuals, we compare conventional static measures of racial context to dynamic and precise measures of their milieus. In particular, we demonstrate how low-level static measures (such as census block) tend to overstate how extreme individuals' racial contexts are, and how this overstatement can lead to underestimates of racial contextual effects.
Fast functional integrals with applications to dynamical systems
John Tillinghast (CAS/Mathematics)
Functional integrals (also called "path integrals") are integrals over function spaces, such as the set of Brownian motion paths from one point to another. They have been used for decades in probability and physics. As high-dimensional integrals they tend to be computation-intensive, generally requiring Monte Carlo sampling. This talk explains how to use sparse Laplace approximation, including higher-order Laplace approximation, for functional integrals. We introduce a way to calculate the higher-order terms in O(N) time, where N is the number of time points. For an infectious disease data set, we use functional integrals to estimate the model parameters. We compare the parameter estimates and approximate integrals to results from importance sampling. For this example, the higher-order approximation is extremely accurate, and even the basic approximation gives very good estimates of the model parameters. Speed is significantly greater than for STAN (a new, general-purpose tool for Hamiltonian MCMC) while getting near-identical parameter estimates.
The Determinants of Student Retention at a Private Selective Post-Secondary Institution
Seth Gershenson (SPA/DPAP)
Despite progress in leveling the playing field for disadvantaged college students, there continue to be gaps in college completion rates between students of different backgrounds. These gaps have the potential to perpetuate socioeconomic disparities, as four-year degrees become more of a prerequisite for labor market success. Existing research suggests that differential completion rates are driven almost entirely by differences in the persistence of admitted students, as opposed to differences in admission rates. Accordingly, the proposed research project seeks to identify the determinants of persistence at a selective private post-secondary institution.
Actionable University Responses to Undergraduate Student Persistence &Completion, IES, $225,000 ($5,000 for HPC)
Large Truck Crashes & State Roadside Inspection Practices:
A Spatial Analysis
Janine Bonner (Independent Researcher)
This project will combine the practices of analyzing large data, mapping, and drawing horizontal conclusions, while utilizing the statistical practices of linear regression and spatial analysis. The goal of the project is to draw correlations between individual state inspection practices and fatal crash rates by state. The intention is to show whether or not better regulatory compliance of state inspection practices helps reduce crash rate and/or severity.
Harnessing the power of parallel computing
for computer-aided drug discovery
Stefano Costanzi (CAS/Chemistry & Center for Behavioral Neuroscience)
My research revolves around the study of the cellular targets of drugs, the identification and pharmacological characterization of molecules that modulate their activity, and the examination of the cellular consequences resulting from such pharmacological intervention. Through the application of computational and experimental biochemical pharmacology techniques, the ultimate goal of my laboratory is to rationally identify molecules potentially endowed with a desired pharmacological activity and subsequently test their biological effect on mammalian cells that express the target of interest either naturally or artificially. The main research focus is on the discovery of compounds that act through G protein-coupled receptors (GPCRs), the single family of cellular targets most exploited by currently marketed medicines.
"Computer-aided discovery of biased agonists and allosteric modulators of the Î²2-adrenergic receptor," American University Faculty Research Support Grant, AY 2014-2015
"Enabling the Application of Virtual Screening to GPCR Homology Models," National Institute of General Medical Sciences, 2016-2019
"P2Y12 receptors: molecular modeling and computer-aided drug discovery" "Purines 2014" conference, Bonn (Germany), July 23-27, 2014.
Costanzi, Stefano, Matthew Skorski, Alessandro Deplano, Brett Habermehl, Mary Mendoza, Keyun Wang, Michelle Biederman, Jessica Dawson, and Jia Gao. "Homology modeling of a Class A GPCR in the inactive conformation: A quantitative analysis of the correlation between model/template sequence identity and model accuracy."Journal of Molecular Graphics and Modelling 70 (2016): 140-152.
Teacher Quality, Student Behavior, and Student Achievement
Seth Gershenson (SPA/DPAP)
This project seeks to estimate the effect of misbehavior on academic performance, the effect of teachers and peers on misbehavior, and the implications of omitting measures of misbehavior from value-added models of the education production function for rankings and estimates of teacher effectiveness.
"Panel data evidence on the effect of school size on student achievement" (joint with Laura Langbein) at the 2013 AEFP and AERA Annual Meetings
"Linking Teacher Quality, Student Attendance, and Student Achievement," presented at 2014 Association for Education Finance & Policy Annual Conference and 2014 American Educational Research Association Annual Meeting
"Are Student Absences Worth the Worry in U.S. Primary Schools? (joint with Alison Jacknowitz, Alison and Andrew Brannegan)," presented at 2013 Southern Economic Association Annual Conference and 2014 American Educational Research Association Annual Meeting
Spencer Foundation Research Grant for "Linking Teacher Quality, Student Attendance, and Student Achievement," 2013-14, $39,427.
W.E. Upjohn Institute Early Career Research Grant. "The Effect of High-Stakes Accountability Policies on Teacher Absences," 2014-15
Estimation and Inference in Value-Added Models when the Relationship between True Academic Achievement and Test Scores Varies Across Time and Teachers, IES, $225,000 ($5,000 for HPC)
The Soft Bigotry of Low Expectations: Estimating the Impact of Stigmatization on Student Outcomes, Spencer Foundation, $40,000 ($2,000 for HPC)
The Implications of Summer Learning Loss for Measuring Teacher and School Effectiveness, IES, $200,000 ($5,000 for HPC)
Published Working Papers
Gershenson, Seth, & Hayes, Michael S. 2016. The effect of civic unrest on student achievement: Evidence from Ferguson, Missouri. IZA Discussion Paper No. 10091.
Gershenson, Seth, & Tekin, Erdal. 2015. The effect of community traumatic events on student achievement: Evidence from the beltway sniper attacks. NBER Working Paper 21055.
Holt, Stephen B., & Gershenson, Seth. 2015. The impact of teacher demographic representation on student attendance and suspensions. IZA Discussion Paper No. 9554.
Birdsall, Chris, Gershenson, Seth, & Zuniga, Raymond. 2016. S
tereotype Threat, Role Models, and Demographic Mismatch in an Elite Professional School Setting. IZA Discussion Paper No. 10459.
Gershenson, Seth. 2016. Linking teacher quality, student attendance, and student achievement. Education Finance & Policy, 11(2): 12-149. DOI: 10.1162/EDFP a 00180.
Gershenson, Seth, & Langbein, Laura. 2015. The effect of primary school size on academic achievement. Educational Evaluation and Policy Analysis, 37(1S): 135S-155S. DOI: 10.3102/0162373715576075
Gershenson, Seth. 2016. Performance standards and employee e ort: Evidence from teacher absences. Journal of Policy Analysis & Management, 35(3): 615-638. DOI: 10.1002/pam.21910.
Gershenson, Seth. 2016. Should value-added models control for student absences? Teachers College Record. September, ID No. 21629.
Gershenson, Seth, Jacknowitz, Alison, & Brannegan, Andrew.
2017. Are student absences worth the worry in U.S. primary schools? Education Finance & Policy, 12(2). (doi:10.1162/EDFP_a_00207)
Interval-Valued Data Estimation
Tual Tuang (CAS/Economics)
Faculty Sponsor: Amos Golan (CAS/Economics)
There is a growing body of literature in dealing with interval data. Most of the focus is on only the first and second moments of the interval, then apply the classical regression type estimation. This project will try to look at it from Information-Theoretic perspective and considers higher moments in estimating and predicting such interval-valued data.
Trual Tang, Yangqin Fan, Amos Golan and Aman Ullah, "Information - Theoretic Model Selection and Estimation for Interval Data," Commodity Futures Trading Commission, 2015
Trual Tang, Yangqin Fan, Amos Golan and Aman Ullah, "Information - Theoretic Model Selection and Estimation for Interval Data," Federal Reserve Bank of Philadelphia, 2016
Trual Tang, Yangqin Fan, Amos Golan and Aman Ullah, "Information - Theoretic Model Selection and Estimation for Interval Data," Moody's Analytics, 2016
Trual Tang, Yangqin Fan, Amos Golan and Aman Ullah, "Information - Theoretic Model Selection and Estimation for Interval Data," Fannie Mae, 2016
Three Projects on Taxes, Revenue, and Budgets
Daniel Mullins (SPA/Public Administration and Policy)
The Future of the Property Tax:
Institutional Factors which Shape its Acceptability, Yield and Burden Distribution
The Role of Local Revenue and Expenditure Limitations in Shaping the Composition of Debt and Its Implications for Efficiency, and Intergenerational and Intergovernmental Equity in Local Public Finance
Population Sorting, Economic Segregation and Growing Fiscal and
Budgetary Disparities in U.S. Communities
Paper 1: This study seeks to first update the findings of Mullins and Mikesell (2009) in an effort to better understand the lag properties of subnational tax bases with particular scrutiny placed upon property tax dynamics. Specifically, we assess the relative performance of tax bases, both vertically (local vs. state jurisdictions) and horizontally (across tax instruments).
Paper 2: The proposed paper is the first study to examine whether the enactment of TELs have changed the composition of debt in local governments. Particularly, we are interested in whether governments constrained by binding TELs have experienced increases in their ratio of debt to general revenue for financing government services and whether there has been a shift toward non-guaranteed debt.
Paper 3: This paper uses Census of Population and Housing Data, the American Community Survey and the Government Finance Series to identify the factors that lead to increased disparity and economic sorting as well as those that provide a revenue net to communities with the most limited resource capacities. It concludes that economic sorting is becoming more pronounced and that such sorting artificially and geographically constrains the resource pool available for addressing public policy needs across all communities.
Informal Employment in Egypt:
Learning from Modeling with Essential Heterogeneity
Natalia Radchenko (CAS/Economics)
The paper focuses on the wage differentials between formal/informal sectors on the Egyptian labour market. The objective is to analyse the nature of the informal employment (involuntary engagement of workers in a segmented labor market versus voluntary choice of workers). The Egyptian labor market has significantly evolved since major structural adjustments were implemented in 1991. In particular the public sector, which employed the majority of highly educated workers, has decreased significantly. The growth in private formal employment has not been however sufficient to absorb the growing labour force and the arrival of large cohorts of civil servants. Consequently, informal employment has grown significantly in the last two decades. All these trends are likely to increase or modify labour market segmentation. At the same time, they are likely to influence worker's behaviour and induce behavioural changes regarding the informal sector. We thus seek to investigate this issue by focusing on sectorial wage gaps using recently developed non parametric methods to estimate the model with essential heterogeneity. In particular, the marginal treatment approach is used to investigate whether wage gaps are due to compensatory differentials or to segmentation between non-competing groups.
Radchenko, Natalia. "Heterogeneity in Informal Salaried Employment: Evidence from the Egyptian Labor Market Survey." World Development 62 (2014): 169-188.
Natalia Radchenko "Informal employment in developing economies: multiple heterogeneity," Journal of Development Studies, 2017, forthcoming.
Inter-American Development Bank, February 2014, "Informal Employment in African Economies: Multiple Heterogeneity"
Southern Economics Association, November 2016, Informal Employment in African Economies: Multiple Heterogeneity"
"Informal Employment in African Economies: Multiple Heterogeneity," 2014.
The Effect of Spatial Dependence on the Empirical Likelihood
Monica Jackson (CAS/Mathematics & Statistics)
The empirical likelihood is a nonparametric likelihood function that is analogous to its parametric counterpart. In particular, observations are assumed to be independent, and identically distributed.There are several empirical likelihood research papers regarding dependent data. However, there is no literature concerning the effect of spatial dependence on empirical likelihood procedures that are carried out assuming independence. This research presents the effect of such a violation on the asymptotic distribution of the empirical likelihood ratio. To determine whether the sampling distribution follows the specified distribution, we propose a spatially weighted Kolmogorov-Smirnoff Goodness-of-Fit test.
Joint Statistics Meeting, American Statistical Association, "The effects of spatial correlation on the empirical likelihood," (with Nancy Glenn), Vancouver, Canada, August 2016
Conference for African American Researchers in the Mathematical Sciences, "The effects of spatial correlation on the empirical likelihood," (with Nancy Glenn), Princeton, NJ, June 2016
Cofactor Prevalence and the Prevalence of HIV
Alan Isaac (CAS/Economics)
The objective of this research is to investigate the role of cofactor infections in understanding the heterosexual spread of HIV. Recent research applies agent-based models to the epidemics of HIV in sub-Saharan Africa. The use of agent-based modeling in epidemiology, including in the epidemiology of HIV, is relatively recent but has been very influential. Agent-based methods allow researchers to readily model the social determinants of disease incidence and prevalence, including the detailed networks of sexual partnership. Many sub-Saharan African countries have HIV prevalence that is one to four dozen times the prevalence in other countries. Explaining this enormous difference has consumed substantial research effort, since it has important implications for treatment and prevention. Conventional explanations of high prevalence of HIV in sub-Saharan Africa have focused almost exclusively on sexual behavior. However, there are strong reasons to explore other explanations. Specifically, a more detailed examination of the disease environment is likely to contribute more to understanding HIV prevalence in the region rather than variance in sexual behavior has been able to. One reason to expect this is the research that show that sexual behavior in high prevalence countires often appears to be more conservative than in many lower-incidence countries, like the US and the UK. Another reason to expect this emerged in recent research by Sawers, Isaac, and Stillwaggon (SIS). SIS found a substantial difficulty in the agent-based literature that has proposed that a high prevalence of sexual-partnership concurrency underpins high HIV prevalence. SIS showed that the agent-based research had ignored the implications of a broad empirical literature on coital diluation. This research extends the SIS model to explore how cofactors can make sexual networks more effective at spreading HIV. For this research, our working hypothesis is that cofactor infections are key to understanding the heterosexual spread of HIV in sub-Saharan Africa and perhaps elsewhere.
Alan Isaac and Larry Sawers, "Partnership Duration, Concurrency, and HIV-Prevention Policy in Sub-Saharan Africa"
Research on Multivariate Probability Distributions
John Nolan (CAS/Mathematics & Statistics)
Some of my current research is related to classes of probability distributions. The stable laws and the extreme value laws generally do not have closed form expressions for their densities and cdfs. I am developing programs to compute these quantities, and some of them are computationally intensive. Some of these formulas are parallelizable, and I would like to experiment with this.
A Sample of Bankruptcy Court Records, 1898-2005
Mary Eschelbach Hansen
This project will construct a long-run micro-level data set from original bankruptcy case files. Despite considerable study of personal bankruptcy, we still do not understand why personal bankruptcy rates have risen so dramatically in the late 20th century, nor do we understand the relationship between the business cycle and business and personal bankruptcy filings. The accumulated scholarship in law, economics, and sociology leads us to believe that these relationships are complex. They are mediated by both the letter and practice of the bankruptcy law, other credit laws, the liquidity of financial institutions, and the regulations imposed on credit markets. The creation of a micro-level data set on bankruptcy that covers a long time period will accelerate research that has been held back by data that are weak relative to the importance of the problem. Initial stages of this research have been funded by the Institute for New Economic Thinking and the Alfred P. Sloan Foundation.
The project has significant intellectual merit; it will transform the interdisciplinary dialog between economics and social work by providing evidence-based predictions for a change in policy. The project's broader impact on at-risk children cannot be overemphasized. Policy that improves outcomes for abused and neglected children can have very high rates of return; for example, the return on society's investment on adoption of children from foster care exceeds 100 percent (Hansen, 2008).
Mary Eschelbach Hansen, (2015). "Sources of Credit and the Extent of the Credit Market: A View from Bankruptcy Records, Mississippi 1929-1936," Enterprising America (William Collins and Robert Margo, editors) NBER/Chicago University Press, pp. 179-214.
Mary Eschelbach Hansen and Michelle M. Miller, (2016). "A New View of Women in Bankruptcy: Evidence from Maryland since 1940," American Bankruptcy Institute Journal 34, 11 (November 2016), pp. 81-83.
Mary Eschelbach Hansen and Nicholas L. Ziebarth, (2017). "Credit Relationships and Business Bankruptcy during the Great Depression" AEJ: Macroeconomics (April 2017). [Link article title to https://www.aeaweb.org/journals/mac/forthcoming]/pr> Grants Institute for New Economic Thinking, "Emergency Preservation of Federal Bankruptcy Court Records," (co-PI Michelle Miller, Rutgers Business School) August 1, 2011-April 30, 2012.
Institute for New Economic Thinking, "Emergency Preservation of Federal Bankruptcy Court Records," (co-PI Michelle Miller, Rutgers Business School) August 1, 2011-April 30, 2012.
Alfred P. Sloan Foundation (Grant Number 2011-6-16), "Digital Preservation of Bankruptcy Court Records, 1898-2000," (co-PI Michelle Miller, Rutgers Business School) September 15, 2011-October 31, 2013.
National Conference of Bankruptcy Judges Endowment for Education, "Opening New Views into Bankruptcy and Credit Markets Using Court Records," January 1, 2013-December 31, 2013.
National Science Foundation, Economics Program Dissertation Improvement Grant (SES-1324468), "Doctoral Dissertation Research in Economics: The Impact of Medicare on Bankruptcy," dissertation supervisor for Megan Fasules, August 15, 2013-July 31, 2014.
National Conference of Bankruptcy Judges Endowment for Education, "Continuation Funding for Opening New Views into Bankruptcy and Credit Markets Using Court Records," January 1, 2014-June 30, 2014.
National Science Foundation, "Collaborative Research: Opening New Views into Bankruptcy and Credit Markets Using Court Records," June 2014-June 2016.
Thesis or Dissertation
Dongping Xie (2017). Three Essays on Monetary Policy Transmission and Banking Crises. American University.
Jess Chen (2016). Local Spillovers in Bankruptcy. American University.
Matthew E. Davis (2016). Natural Disasters, Bankruptcy and Disaster Aid: Evidence from the United States. American University.
Megan Lynn Fasules (2015). The Impact of Medicare on Bankruptcy. American University.
Undergraduate Honors Theses or Economic Capstone Papers
John Pedersen (2015). Gender and Bankruptcy. Economics Capstone Project.
Zach Duey (2014). Impacts of Bank Failures on Firm Entry and Exit. Honors Capstone.
Mary Eschelbach Hansen, "Financial System Liquidity and Bankruptcy: Mississippi 1929-31," Economic History Association Annual Meeting, September 2012.
Mary Eschelbach Hansen and Megan Fasules, "Interactions between Social Insurance Programs: The Impact of Medicare on the Characteristics of Petitioners for Bankruptcy," Society for Government Economists Annual Meeting, November 2013
Mary Eschelbach Hansen, "Sources of Credit and the Extent of the Credit Market: A View from Bankruptcy Records, Mississippi 1929-1936," Enterprising America NBER Conference, December 14, 2013
Mary Eschelbach Hansen and Megan Fasules, "Interactions between Social Insurance Programs: The Impact of Medicare on the Characteristics of Petitioners for Bankruptcy," American Economic Association Annual Meeting, January 2014.
Megan Fasules, "The Impact of Medicare on Personal Bankruptcy," Poster presentation for Economic History Association Annual Meeting, September 2014
Zach Duey (2014). Impacts of Bank Failures on Firm Entry and Exit. Honors Capstone Conference. Recipient of Best Social Science Presentation.
Matthew Davis, "Natural Disasters, Disaster Aid, and Bankruptcy Filings in the United States, Dickinson College, October 2015.
Matthew Davis, "Disasters, Federal Disaster Relief Policy and Bankruptcy Filings," Poster Presentation, Annual Meeting of the Economic History Association, September 2015
Matthew Davis, "Government Policy and Adverse Events," Annual Meeting of the Southern Economic Association, November 2015.
Mary Eschelbach Hansen, "A New View of Women in Bankruptcy," Annual Meeting of the Economic and Business History Society, May 2016.
Michelle M. Miller, "A New View of Women in Bankruptcy," Annual Meeting of the American Law and Economics Association, May 2016.
Dongping Xie, "Bank Lending Standards and Business Debts," Economic History and Development Workshop, George Mason University, August 2016.
Mary Eschelbach Hansen and Bradley A. Hansen, "The Renegotiation of the Relationship between Consumers and their Creditors, 1966-1979," Eastern Economic Association Meetings, February 2017.
Modeling Exposure-Response Relationships
Elizabeth Malloy (CAS/Mathematics and Statistics)
Malloy will examine the utility of computationally-intensive, data-adaptive approaches to splines and other smoothers in Cox regression models. Smoothing avoids a priori specification of the functional form of the relationship between exposure and a response. Because inferences about the relative risk of a response are drawn from the final model, selection of the model is critical. Malloy's preliminary studies show that there is excessive variation in model fit when different standard smoothing methods are applied to the same data (Govindarajulu, et al. 2009; Malloy et al. 2009). In this project, Malloy proposes to (1) implement model averaging methods within a given class of smoothers, and (2) extend the "super learner" algorithm (van der Laan et al. 2007) to estimate the dose-response from a set of candidate smoothers within Cox models based on cross-validated risk.
Malloy's research has intellectual merit because it will investigate the benefits of model averaging and the feasibility of using the super learner algorithm in estimation with smoothing methods. The broader impact of the research is in its application in epidemiology. The work will inform understanding of the underlying factors related to disease incidence or mortality. For example, Malloy and her collaborator, Ellen A. Eisen (School of Public Health, UC €" Berkeley) plan to use the results of the research to estimate the incidence of skin cancer in relation to cumulative exposure to oil-based metalworking fluids in a cohort study of 23,650 autoworkers.
"The Super Learner for Estimating Nonlinear Associations in the Cox Regression Model," Malloy, E.J., Gautier, P., Cook, C., and Bergeron, M., Joint Statistical Meetings, poster, 2013
Malloy, E.J., Gautier, P., Cook, C. and Bergeron, M. 2013."The Super Learner for Estimating Nonlinear Associations in the Cox Regression Model."