Faculty Highlights

• Niranjan Balasubramanian

  Human Centered NLP with User-Factor Adaptation (2017)

  "We pose the general task of user-factor adaptation — adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it. We introduce a continuous adaptation technique, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. We apply this technique with known user factors including age, gender, and personality traits, as well as latent factors, evaluating over five tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and stance detection. Adaptation provides statistically significant benefits for 3 of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm, and +3.0 points for stance." Read More

• Hugo Benitez-Silva

  How Large is the Bias in Self-Reported Disability?  (2004)

  "A pervasive concern with the use of self‐reported health measures in behavioural models is that individuals tend to exaggerate the severity of health problems in order to rationalize their decisions regarding labour force participation, application for disability benefits, etc. We re‐examine this issue using a self‐reported indicator of disability status from the Health and Retirement Study. We study a subsample of individuals who applied for disability benefits from the Social Security Administration (SSA), for whom we can also observe the SSA's decision. Using a battery of tests, we are unable to reject the hypothesis that self‐reported disability is an unbiased indicator of the SSA's decision. Copyright © 2004 John Wiley & Sons, Ltd." Read More

   

• Jeffrey Heinz

  Typology Emerges From Simplicity in Representations and Learning (2021)

  "We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research." Read More

• Jason J. Jones

  A dataset for the study of identity at scale: Annual Prevalence of American Twitter Users with specified Token in their Profile Bio 2015–2020 (2021)

  "Personally expressed identity is who or what an individual themselves says they are, and it should be studied at scale. At scale means with data on millions of individuals, which is newly available and comes timestamped and geocoded. This work introduces a dataset for the study of identity at scale and describes the method for collecting and aggregating such data. Further, tools and theory for working with the data are presented. A demonstration analysis provides evidence that personal, individual development and changing cultural norms can be observed with these data and methods." Read More 

  Using Twitter Bios to Measure Changes in Self-Identity: Are Americans Defining Themselves More Politically Over Time? (2021)

  "Are Americans weaving their political views more tightly into the fabric of their self-identity over time? If so, then we might expect partisan disagreements to continue becoming more emotional, tribal, and intractable. Much recent scholarship has speculated that this politicization of Americans' identity is occurring, but there has been little compelling attempt to quantify the phenomenon, largely because the concept of identity is notoriously difficult to measure. We introduce here a methodology, Longitudinal Online Profile Sampling (LOPS), which affords quantifiable insights into the way individuals amend their identity over time. Using this method, we analyze millions of “bios” on the microblogging site Twitter over a 4-year span, and conclude that the average American user is increasingly integrating politics into their social identity. Americans on the site are adding political words to their bios at a higher rate than any other category of words we measured, and are now more likely to describe themselves by their political affiliation than their religious affiliation. The data suggest that this is due to both cohort and individual-level effects." Read More 

  Stereotypical Gender Associations in Language Have Decreased Over Time (2020)

  "Using a corpus of millions of digitized books, we document the presence and trajectory over time of stereotypical gender associations in the written English language from 1800 to 2000. We employ the novel methodology of word embeddings to quantify male gender bias: the tendency to associate a domain with the male gender. We measure male gender bias in four stereotypically gendered domains: career, family, science, and arts. We found that stereotypical gender associations in language have decreased over time but still remain, with career and science terms demonstrating positive male gender bias and family and arts terms demonstrating negative male gender bias. We also seek evidence of changing associations corresponding to the second shift and find partial support. Traditional gender ideology is latent within the text of published English-language books, yet the magnitude of traditionally gendered associations appears to be decreasing over time." Read More

• Reuben Kline

   Unfair Rules for Unequal Pay: Wage Discrimination and Procedural Justice (2019)

  "Do people judge some forms of wage discrimination to be more unfair than others? We report an experiment in an online labor market in which participants were paid based on discriminatory rules. We test hypotheses about fairness based on procedural justice, divisiveness, and affective polarization between partisans. Workers transcribed text and then learned that they earned more or less money than other workers for doing the same job. We manipulated whether the unequal pay was based on their political party, eye color, or an arbitrary choice between two doors. Consistent with the divisiveness hypothesis, participants judged discriminatory pay to be less fair when it was based on a stable characteristic, political party, or eye color, compared to a transient choice (between doors). We find mixed evidence about how affective polarization exacerbates the unfairness of partisan discrimination. We discuss implications for the procedural justice of wage discrimination." Read More

  Loss-Framed Arguments Can Stifle Political Activism (2019)

  "Research commonly finds that citizens are loss averse: they dislike losses far more than similarly sized gains. One implication is that arguments for policy change framed in terms of losses to be avoided often have a larger impact on public opinion than arguments framed in terms of gains to be enjoyed. Although several scholars have observed this pattern with respect to public opinion, we know far less about the effect of loss- and gain-framed arguments on political activism. This is a critical omission given the disproportionate impact of political activists on the policymaking process. Using field and survey experiments, we investigate the impact of gain- and loss-framed arguments on climate change activism. We find that loss-framed arguments can be less mobilizing, even when they are otherwise more persuasive, than gain-framed arguments. Our results deepen our theoretical understanding of what motivates political activism, especially in an era of professionalized politics." Read More

  Personality and Prosocial Behavior: A Multilevel Meta-Analysis (2017)

  "We investigate the effect of personality on prosocial behavior in a Bayesian multilevel meta-analysis (MLMA) of 15 published, interdisciplinary experimental studies. With data from the 15 studies constituting nearly 2500 individual observations, we find that the Big Five traits of Agreeableness and Openness are significantly and positively associated with prosocial behavior, while none of the other three traits are. These results are robust to a number of different model specifications and operationalizations of prosociality, and they greatly clarify the contradictory findings in the literature on the relationship between personality and prosocial behavior. Though previous research has indicated that incentivized experiments result in reduced prosocial behavior, we find no evidence that monetary incentivization of participants affects prosocial tendencies. By leveraging individual observations from multiple studies and explicitly modeling the multilevel structure of the data, MLMA permits the simultaneous estimation of study- and individual-level effects. The Bayesian approach allows us to estimate study-level effects in an unbiased and efficient manner, even with a relatively small number of studies. We conclude by discussing the limitations of our study and the advantages and disadvantages of the MLMA method." Read More 

  Racial Salience, Viability, and the Wilder Effect Evaluating Polling Accuracy For Black Candidates (2015)

  "This study assesses whether polling discrepancies for black candidates can be explained by an interaction of racial salience in an election and the candidate’s electoral strength. We hypothesize that voters will have few nonracial justifications for their lack of support for a black candidate when race is a salient issue and the candidate is electorally viable. As a result, polls will experience more problems with socially desirable response bias in such contexts. We use pre-election polls for the near-complete universe of black US Senate and gubernatorial candidates from 1982 to 2010, statewide polls from the 2008 and 2012 presidential elections, and an original measure of the racialization to test our hypothesis. Our results demonstrate that the racialization of the election leads polls to significantly overestimate only support for state-level black candidates and President Obama in contexts where the candidate has the most electoral strength. In the conclusion, we discuss how the results inform pollsters about potential hazards for social desirability response bias." Read More 

• Bonita London

  Evolution of Social Identity Terms in Lay and Academic Sources: Implications for Research and Public Policy (2018)

  "Increasingly, individuals identify with two or more racial or cultural, yet are sometimes externally misclassified, contributing to experiences of invisibility within U.S. society. Using computational techniques, we examined the transmission of cultural identity terms through time, providing some evidence for the changing representation of social identity. We examined the usage patterns of identity terms with the prefixes (mono-, bi-, multi-), modifying the social identity terms: culture, ethnicity, and race (e.g., comparing monocultural, monoethnic, and monoracial). For bicultural and multicultural terms, those with -racial suffixes were the earliest used terms, while those with -cultural and -ethnic suffixes gained more popularity in recent years. We examined the evolution of the higher frequency social identity terms in lay sources (the NY Times, Reddit), and found that interracial and multicultural were the most popular over time, peaking recently. We examined the potential time lag in the sequence of identity terms among academic (PsycINFO, NIH, and NSF Databases), lay (the NY Times), and mixed sources (Google Books N-Grams), demonstrating that newer terms (e.g., multicultural) are first used and gain prevalence in lay sources, then mixed sources, and eventually academic sources. The implications of these findings for research, public policy, and psychosocial experiences of individuals are discussed." Read More

  Psychosocial Pathways to STEM Engagement among Graduate Students in the Life Sciences (2016)

  "Despite growing diversity among life sciences professionals, members of historically underrepresented groups (e.g., women) continue to encounter barriers to academic and career advancement, such as subtle messages and stereotypes that signal low value for women, and fewer opportunities for quality mentoring relationships. These barriers reinforce the stereotype that women's gender is incompatible with their science, technology, engineering, and mathematics (STEM) field, and can interfere with their sense of belonging and self-efficacy within STEM. The present work expands this literature in two ways, by 1) focusing on a distinct period in women's careers that has been relatively understudied, but represents a critical period when career decisions are made, that is, graduate school; and 2) highlighting the buffering effect of one critical mechanism against barriers to STEM persistence, that is, perceived support from advisors. Results of the present study show that perceived support from one's advisor may promote STEM engagement among women by predicting greater gender-STEM identity compatibility, which in turn predicts greater STEM importance among women (but not men). STEM importance further predicts higher sense of belonging in STEM for both men and women and increased STEM self-efficacy for women. Finally, we describe the implications of this work for educational policy." Read More

  Unstable Identity Compatibility: How Gender Rejection Sensitivity Undermines the Success of Women in Science, Technology, Engineering, and Mathematics Fields (2013)

  "Although the perceived compatibility between one's gender and science, technology, engineering, and mathematics (STEM) identities (gender-STEM compatibility) has been linked to women's success in STEM fields, no work to date has examined how the stability of identity over time contributes to subjective and objective STEM success. In the present study, 146 undergraduate female STEM majors rated their gender-STEM compatibility weekly during their freshman spring semester. STEM women higher in gender rejection sensitivity, or gender RS, a social-cognitive measure assessing the tendency to perceive social-identity threat, experienced larger fluctuations in gender-STEM compatibility across their second semester of college. Fluctuations in compatibility predicted impaired outcomes the following school year, including lower STEM engagement and lower academic performance in STEM (but not non-STEM) classes, and significantly mediated the relationship between gender RS and STEM engagement and achievement in the 2nd year of college. The week-to-week changnges in gender-STEM compatibility occurred in response to negative academic (but not social) experiences." Read More

• Christian Luhmann

  Wisdom of the (Expert) Crowd: Performance of aggregation models for FHR evaluations (on-going project)

  "Cardiotocogram (CTG) is one of the most commonly used medical tools that provides physicians information about the fetal heart rate (FHR) and uterine activity. However, the clinical utility of CTG is low: it does not substantially improve fetal outcomes and it increases the probability of unnecessary cesarean sections. Existing work has attributed the low clinical utility to clinicians’ ability to reliably interpret CTG recordings. One strategy to increase the reliability of decisions is to aggregate the decisions of multiple decision-makers (leveraging the “wisdom of crowds”). In the current study, we apply four different aggregation techniques to fetal evaluations made by nine expert obstetricians taken from the TU-CHB Intrapartum Cardiotocography Database. Results indicate that sophisticated aggregation techniques outperformed simpler approaches. However, due to the low rate of adverse outcomes (i.e., hypoxia), a naive model, which “guesses” that all babies are healthy with high probability, outperformed all aggregation models as well as the best-performing expert. The reason for this is that obstetricians were dramatically miscalibrated: all experts systematically underestimated the health of the babies and that experts were “ better performing” only to the extent that they underestimated the health of the babies less. These findings suggest the need for changes to both clinical practice and fetal evaluation guidelines." -Medhini Urs

  Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach (2021)

  "Background: Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal, or because it is encoded, but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER-vs.-reinforcing existing skills). Methods: We utilized a discriminative and contemporary machine learning approach - Deep Convolutional Neural Networks (CNN) - to classify facial emotions viewed by individuals with and without ASD(N= 88) from concurrently-recorded electroencephalography signals. Results: The CNN classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, CNN accuracy was greater in the ASD group, and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature-importance analyses suggest that a late temporal window of neural activity (1000-1500ms) may be uniquely important in facial emotion classification for individuals for ASD. Conclusions: Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prosthetics." Read More

• Klaus Mueller

  WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings (2021)

  "Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling such intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups based on race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at this http URL." Read More

  Measuring Social Biases of Crowd Workers using Counterfactual Queries (2020)

  "Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the inherent social biases of crowd workers. To ensure such social biases aren't passed onto the curated datasets, it's important to know how biased each crowd worker is. In this work, we propose a new method based on counterfactual fairness to quantify the degree of inherent social bias in each crowd worker. This extra information can be leveraged together with individual worker responses to curate a less biased dataset." Read More

  Toward Interactively Balancing the Screen Time of Actors Based on Observable Phenotypic Traits in Live Telecast (2020)

  "Several prominent studies have shown that the imbalanced on-screen exposure of observable phenotypic traits like gender and skin-tone in movies, TV shows, live telecasts, and other visual media can reinforce gender and racial stereotypes in society. Researchers and human rights organizations alike have long been calling to make media producers more aware of such stereotypes. While awareness among media producers is growing, balancing the presence of different phenotypes in a video requires substantial manual effort and can typically only be done in the post-production phase. The task becomes even more challenging in the case of a live telecast where video producers must make instantaneous decisions with no post-production phase to refine or revert a decision. In this paper, we propose Screen-Balancer, an interactive tool that assists media producers in balancing the presence of different phenotypes in a live telecast. The design of Screen-Balancer is informed by a field study conducted in a professional live studio. Screen-Balancer analyzes the facial features of the actors to determine phenotypic traits using facial detection packages; it then facilitates real-time visual feedback for interactive moderation of gender and skin-tone distributions. To demonstrate the effectiveness of our approach, we conducted a user study with 20 participants and asked them to compose live telecasts from a set of video streams simulating different camera angles, and featuring several male and female actors with different skin-tones. The study revealed that the participants were able to reduce the difference of screen times of male and female actors by 43%, and that of light-skinned and dark-skinned actors by 44%, thus showing the promise and potential of using such a tool in commercial production systems." Read More

• Owen Rambow

  Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions (2018)

  "Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interaction. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do --- an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings." Read More

  Dialog Structure Through the Lens of Gender, Gender Environment, and Power (2017)

  "Understanding how the social context of an interaction affects our dialog behavior is of great interest to social scientists who study human behavior, as well as to computer scientists who build automatic methods to infer those social contexts. In this paper, we study the interaction of power, gender, and dialog behavior in organizational interactions. In order to perform this study, we first construct the Gender Identified Enron Corpus of emails, in which we semi-automatically assign the gender of around 23,000 individuals who authored around 97,000 email messages in the Enron corpus. This corpus, which is made freely available, is orders of magnitude larger than previously existing gender identified corpora in the email domain. Next, we use this corpus to perform a large-scale data-oriented study of the interplay of gender and manifestations of power. We argue that, in addition to one's own gender, the "gender environment" of an interaction, i.e., the gender makeup of one's interlocutors, also affects the way power is manifested in dialog. We focus especially on manifestations of power in the dialog structure --- both, in a shallow sense that disregards the textual content of messages (e.g., how often do the participants contribute, how often do they get replies etc.), as well as the structure that is expressed within the textual content (e.g., who issues requests and how are they made, whose requests get responses etc.). We find that both gender and gender environment affect the ways power is manifested in dialog, resulting in patterns that reveal the underlying factors. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions." Read More

   

• H. Andrew Schwartz

  Correcting Sociodemographic Selection Biases for Population Prediction from Social Media (2022)

  "Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population -- a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. Across four tasks of predicting U.S. county population health statistics from Twitter, we find standard restratification techniques provide no improvement and often degrade prediction accuracies. The core reasons for this seems to be both shrunken estimates (reduced variance of model predicted values) and sparse estimates of each population's socio-demographics. We thus develop and evaluate three methods to address these problems: estimator redistribution to account for shrinking, and adaptive binning and informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods significantly outperforms the standard restratification approaches. Combining approaches, we find substantial improvements over non-restratified models, yielding a 53.0% increase in predictive accuracy (R^2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks."  Read More

  Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020)

  "An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks." Read More

   

• Steven Skiena

  Stereotypical Gender Associations in Language Have Decreased Over Time (2020)

  "Using a corpus of millions of digitized books, we document the presence and trajectory over time of stereotypical gender associations in the written English language from 1800 to 2000. We employ the novel methodology of word embeddings to quantify male gender bias: the tendency to associate a domain with the male gender. We measure male gender bias in four stereotypically gendered domains: career, family, science, and arts. We found that stereotypical gender associations in language have decreased over time but still remain, with career and science terms demonstrating positive male gender bias and family and arts terms demonstrating negative male gender bias. We also seek evidence of changing associations corresponding to the second shift and find partial support. Traditional gender ideology is latent within the text of published English-language books, yet the magnitude of traditionally gendered associations appears to be decreasing over time." Read More

• Adryan Wallace

  The Intersections of Gender Inequality, Regional Economic Integration and State Fragility (2022)

  "Gender equality is an important indicator of human security both in terms of political stability and economic development. When states are unable to address the interests and economic priorities of their constituents, the excluded populations can experience a level of marginalization that can lead to intrastate conflict. Regional economic communities (RECs) and regional integration organizations (RIOs) have adopted a series of policy interventions to address inequality and political stability. More specifically the RECs, RIOs, and domestic governments of member organizations have drafted and implemented gender mainstreaming strategies to address gender inequality. Women cannot be treated as a monolith; disparities in access to resources intersect with other demographic categories and factors. This paper is part of a larger study which uses gender inequality to predict state fragility (GISF), through the lens of systematically sampled African countries by combining econometric and case study datasets. The larger GISF model features from all subregions of the continent however this paper focuses on a subset of cases.
  The goal of this paper is thus to develop and test a theoretical framework capable of predicting when the constraints imposed by social structures create tensions which can lead to a dysfunctional state, conflict, and potentially violence. In order to capture the variations in women’s experiences in RECs and RIOs, I have developed an Index of Agency (IOA). The IOA reveals how combinations of inequalities produced by political, economic, and social institutions combine to impact women’s economic security. Agency is operationalized as having the autonomy to determine one's own economic activities and to successfully address institutional obstacles. The model captures rich data and aims to a) identify weaknesses in state structures that are producing inequalities, b) develop sustainable approaches to addressing inequalities before they lead to conflict, and c) provide targeted approaches to mainstreaming gender equality into regional economic integration policies. The proposed paper is part of a larger project where the model is tested in two countries in each sub-region of the African continent (West, East, North, Southern Africa) because of variability in the presence of multiple RECs and RIOs, levels of inequality and gender equality, and the presence or absence of inter and intrastate conflicts. This UN OSAA paper focuses primarily on a set of West African country case studies, Nigeria and Ghana.
  The current pandemic continues to highlight the consequences of maintaining high levels of inequalities in multiple areas on human security. This paper proposes centering the varied experiences of women can help identify structural weakness in the state and RECs. Once identified policies can be developed to generate institutional changes that can mitigate fiscal stressors that often lead to conflict. The study provides a careful examination of the intersecting relationships among inequalities in the context of economic, political, social, educational, Information Communication Technology, and public health institutions. The gender disparities in access to health care include the following indicators: treatment, insurance coverage, medication, personnel and the proximity of facilities. The investments in human capital required to reduce gender inequalities simultaneously serve to promote inclusive and sustainable economic growth. The capacities of state structures to respond to the needs of the most vulnerable members of societies is an important metric of both economic and political stability. Local women’s organizations need to have decision making authority in tailoring gender equality policies and monitoring and evaluating them. Placing women’s groups at the center of policy implementation and assessments generates institutional changes that are dynamic and capable of responding to new inequalities that may emerge. State fragility can be significantly reduced when tensions surrounding the intersections between economic disparities and other demographic pressures are alleviated." Read More 

  Engineering our  Own Futures:   Lessons on Holistic Development from Muslim  Women’s Civil Society Groups   in Nigeria, Ghana and  Tanzania (2016)

  "Muslim women’s organizations in East and  West Africa have cultivated successful strategies to mitigate the varied domestic economic and political outcomes produced by globalization. Although China and the other BRICS countries are providing multi-polar development models, their results may not differ significantly from  their western  counterparts if groups  that are often left out of the decision-making processes are not included.  There is an urgent need for social scientists to make the experiences of African women as designers of development the central point of theorizing in order to inform how we conceptualize economic and political participation and measure inequality.  This paper will utilize case studies from local women’s non-governmental  and community-based organizations  in Kano, Nigeria, Tamale, Ghana and Dar es Salaam, Tanzania to help develop mechanisms for sustainable economic growth and substantive representation which, I argue, can help generate state institutions that are more responsive to the needs of their citizens. Mainstreaming gender as an analytical frame is essential because it interrogates privilege, illustrates how it is distributed among and between  women and  men and  provides insights  into  partnerships  that can be  forged  across  genders.  Furthermore, the  institutional  linkages  of  women’s organizations both within and across national contexts strengthens the ability of African countries to look internally and share their development best practices through sub-regional entities and the African Union. Finally, civil society  needs  to  be  redefined  and  contextualized  using  the  perspectives  of citizens at the grassroots level to produce holistic policy recommendations for all three tiers of governance (domestic, sub-regional and regional)." Read More

  Agency through Development: Hausa Women's NGOs and CBOs in Kano, Nigeria (2014)

  "Analyzing the participation of Hausa women in religiously influenced nongovernmental organizations (NGOs) devoted to development work provides critical insights into the complex intersection of gender, religion, class, culture, and politics and economics. Based on interviews with leaders and employees of various NGOs, including community-based organizations (CBOs), in Kano, Nigeria, in 2010–11, this in-depth case study provides important examples of how various types of NGOs navigate political pressures when it comes to funding; it recognizes the understudied importance of women's labor contributions in the context of the development apparatus in Africa; it highlights the role of women as progenitors rather than benefactors of economic development; and it illustrates the unique role that faith-based organizations (FBOs) can and do play in terms of reaching certain marginalized segments of the population." Read More

• Wei Zhu

  Path Analysis of Sea-Level Rise and Its Impact (2022)

  "Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level changes (both global and regional) and subsequently predicting the magnitude of such changes. To this end, we have designed a novel analysis pipeline including three sequential steps: (1) a dynamic structural equation model (dSEM) to identify pathways between the global mean sea level (GMSL) and various predictors, (2) a vector autoregression model (VAR) to quantify the GMSL changes due to the significant relations identified in the first step, and (3) a generalized additive model (GAM) to model the relationship between regional sea level and GMSL. Historical records of GMSL and other variables from 1992 to 2020 were used to calibrate the analysis pipeline. Our results indicate that greenhouse gases, water, and air temperatures, change in Antarctic and Greenland Ice Sheet mass, sea ice, and historical sea level all play a significant role in future sea-level rise. The resulting 95% upper bound of the sea-level projections was combined with a threshold for extreme flooding to map out the extent of sea-level rise in coastal communities using a digital coastal tracker." Read More

  Route Optimization via Environment-Aware Deep Network and Reinforcement Learning (2021)

  "Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers)." Read More