Kristin P. Bennett

Involving students and trainees both in organizing and in participating in competitions and challenges is a powerful pedagogical tool. From kindergarten through the last years of graduate studies, or as a way to upskill to gain new skills on the job, competitions can gamify the learning process and thus motivate people to explore by themselves, assimilate a variety of material, and expand their capabilities. Competitions also contribute to engaging students in learning about problems of societal importance. At the graduate level, designing and implementing competitions can be seen as a hands-on means of learning proper design and analysis of experiments. In the workplace, this remains an effective way to embrace continuous learning and remain up to date as technologies, tools, and approaches evolve. In this chapter, we present a synthesis of various hands-on teaching and learning experiences using competitions as a medium. We report educational efforts conducted in kindergarten, high school, university and working environment. For younger students, competitions take more the form of individual projects qualitatively evaluated by a jury, while at the university level they take the form of automatically graded homework addressing a research problem. The application domains can be very diverse: medicine, ecology, marketing, computer vision, recommendation, text processing, etc., and students enjoy being involved in creating challenges motivated by humanitarian purposes. Within a professional or working environment, competitions are used to help with upskilling, learning new approaches, and tools that can directly be used in one's job or to update one's resume to remain relevant and competitive in the market place.

A treatment may be appropriate for some group (the ``sick" group) on whom it has a positive effect, but it can also have a detrimental effect on subjects from another group (the ``healthy" group). In a non-targeted trial both sick and healthy subjects may be treated, producing heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then difficult, because the effects on the different groups get tangled. We propose an efficient nonparametric approach to estimating the group effects, called {\bf PCM} (pre-cluster and merge). We prove its asymptotic consistency in a general setting and show, on synthetic data, more than a 10x improvement in accuracy over existing state-of-the-art. Our approach applies more generally to consistent estimation of functions with a finite range.

Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Corey Curran, Keerthiram Murugesan, Soham Dan, Vibha Anand, Kristin Bennett. Findings of the Association for Computational Linguistics: NAACL 2025. 2025.

MortalityMinder enables healthcare researchers, providers, payers, and policy makers to gain actionable insights into where and why premature mortality rates due to all causes, cancer, cardiovascular disease, and deaths of despair rose between 2000 and 2017 for adults aged 25–64. MortalityMinder is designed as an open-source web-based visualization tool that enables interactive analysis and exploration of social, economic, and geographic factors associated with mortality at the county level. We provide case studies to illustrate how MortalityMinder finds interesting relationships between health determinants and deaths of despair. We also demonstrate how GPT-4 can help translate statistical results from MortalityMinder into actionable insights to improve population health. When combined with MortalityMinder results, GPT-4 provides hypotheses on why socio-economic risk factors are associated with mortality, how they might be causal, and what actions could be taken related to the risk factors to improve outcomes with supporting citations. We find that GPT-4 provided plausible and insightful answers about the relationship between social determinants and mortality. Our work is a first step towards enabling public health stakeholders to automatically discover and visualize relationships between social determinants of health and mortality based on available data and explain and transform these into meaningful results using artificial intelligence.

We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions.

Disparities in healthcare access and utilization associated with demographic and socioeconomic status hinder advancement of health equity. Thus, we designed a novel equity-focused approach to quantify variations of healthcare access/utilization from the expectation in national target populations. We additionally applied survey-weighted logistic regression models, to identify factors associated with usage of a particular type of health care. To facilitate generation of analysis datasets, we built an National Health and Nutrition Examination Survey (NHANES) knowledge graph to help automate source-level dynamic analyses across different survey years and subjects’ characteristics. We performed a cross-sectional subgroup disparity analysis of 2013-2018 NHANES on U.S. adults for receipt of diabetes treatments and vaccines against Hepatitis A (HAV), Hepatitis B (HBV), and Human Papilloma (HPV). Results show that in populations with hemoglobin A1c level ≥6%, patients with non-private insurance were less likely to receive newer and more beneficial antidiabetic medications; being Asian further exacerbated these disparities. For widely used drugs such as insulin, Asians experienced insignificant disparities in odds of prescription compared to White patients but received highly inadequate treatments with regard to their distribution in U.S. diabetic population. Vaccination rates were associated with some demographic/socioeconomic factors but not the others at different degrees for different diseases. For instance, while equity scores increase with rising education levels for HBV, they decrease with rising wealth levels for HPV. Among women vaccinated against HPV, minorities and poor communities usually received Cervarix while non-Hispanic White and higher-income groups received the more comprehensive Gardasil vaccine. Our study identified and quantified the impact of determinants of healthcare utilization for antidiabetic medications and vaccinations. Our new methods for semantics-aware disparity analysis of NHANES data could be readily generalized to other public health goals to support more rapid identification of disparities and development of policies, thus advancing health equity.