Ritwick Banerjee Wins Best Poster Award at 2025 Data Science Poster Fair
Department of Computer Science & Engineering master's student Ritwick Banerjee was awarded the Best Poster Award at the Spring 2025 Data Science Poster Fair. He was honored for his project, "Mood Maps: Causal effects of moods - Depression, Mania and more".
Mihir Ashok Momaya and Chinmay Arora were the runner ups for the award for their projects, "Method to Forecast Kidney Function Overtime" and "Geographical SDOH and Diabetes outcomes", respectively.
The data science graduate program hosts an annual poster fair each spring. As a part of their degree requirements students present the research they have conducted over the span of one or multiple semesters under the guidance of their faculty advisor. The event is open to the public, and each poster and presentation was judged by a variety of students, faculty, and industry members.
Learn more about this year's poster projects.
Congratulations to Ritwick, Mihir, and Chinmay for receiving this year's awards! Their abstracts are listed below.
Ritwick Banerjee - Best Poster Winner
Project: Mood Maps: Causal effects of moods - Depression, Mania and more
Advisor: Erich Kummerfeld, Institute for Health Informatics
Abstract: Bipolar disorder (BD) is a chronic mental health condition characterized by extreme mood fluctuations, encompassing periods of emotional highs (mania) and lows (depression). Investigating how manic and depressive symptoms causally influence one another could generate a symptom hierarchy with implications for refining the current conceptualizations of mood episodes, identifying treatment interventions, and providing insights into the biological underpinnings of the disorder. To analyze symptom relationships in the BD symptom network, we employed Causal Discovery Analysis (CDA) to identify causal relationships among manic and depressive symptoms in the 4 canonical mood states of BD: mania, depression, mixed state, and euthymia. Our analysis utilized data from ten NIMH-funded studies (N=6021 participants, 17,044 mood state observations), which assessed symptoms using the Young Mania Rating Scale and the Montgomery-Asberg Depression Rating Scale. We examined the causal structure of the symptom network in each of the 4 mood states, including whether symptoms with the greatest influence varied across mood states. Our findings indicated that mania, depression, mixed states, and euthymia exhibited distinct causal structures. The mood and energy symptoms characteristic of each mood episode exerted the strongest influence on the respective symptom networks. Interestingly, symptoms that were mainly effects in mood episodes were mainly causes in euthymia. We conclude that mania, depression, and mixed states are not only marked by heightened symptom severity but also by a reconfiguration of the causal structure of the symptom network.
Mihir Ashok Momaya - Runner Up
Project: Method to Forecast Kidney Function Overtime
Advisor: Saumya Sinha, Department of Industrial and Systems Engineering
Abstract: Delayed graft function (DGF) is a significant post-transplant complication that affects kidney transplant recipients, often leading to increased morbidity and potential graft loss. This study aims to identify patients with DGF using predefined clinical indicators within seven days post-transplant and assess its long-term impact. Patients are categorized into three groups: those who experience graft loss within one year, after one year, and those without DGF who still experience graft loss.
This is a retrospective study design using longitudinal de-identified data from electronic health records (EHR) available through the University of Minnesota Clinical and Translational Science Institute (CTSI) comprising over 200 million rows across multiple tables, was refined using advanced data science techniques to ensure quality and usability. This involved preprocessing, normalization, handling missing values, structuring data for machine learning applications and establishing clean data pipelines for future predictive modeling. Key risk factors, including urinalysis results, dialysis history, and biochemical markers, were analyzed to distinguish DGF patients. Statistical and machine learning models were employed to characterize differences between those with DGF-related graft loss and those who retained graft function.
Preliminary findings suggest that DGF patients with early graft loss exhibit distinct clinical profiles compared to those who recover. Understanding these differences can improve early detection, optimize post-transplant care, and potentially enhance graft survival outcomes. Future work involves refining predictive models and validating results across larger cohorts to improve patient stratification and treatment planning.
Chinmay Arora - Runner Up
Project: Geographical SDOH and Diabetes outcomes
Advisor: Erich Kummerfeld, Institute for Health Informatics
Abstract: Diabetes remains a significant public health challenge, with social determinants of health (SDoH) playing a crucial role in disease prevalence and outcomes. This study aims to analyze the relationship between neighborhood deprivation and diabetes prevalence in Minnesota by integrating the Area Deprivation Index (ADI 2020), Social Vulnerability Index (SVI 2020), and patient data from MHealth Fairview (2016–2020). Specifically, the research will evaluate the impact of neighborhood deprivation on diabetes, assess the role of Federally Qualified Health Networks (FQHNs) in supporting vulnerable populations, and identify spatial and demographic disparities in health outcomes.
To achieve these objectives, the study will map diabetes prevalence alongside ADI and SVI indicators to visualize geographic hotspots. Correlations between deprivation indices and health outcomes will be calculated using statistical techniques such as multivariate regression, while machine learning methods will be employed for feature selection and predictive modeling. Geospatial variables will be incorporated to enhance the accuracy of the analysis. Key research questions include understanding how ADI and SVI relate to diabetes prevalence and control, identifying spatial patterns of diabetes outcomes in vulnerable populations, and determining whether insights from deprivation indices can guide targeted interventions.
Expected outcomes include the identification of high-risk geographic areas in Minnesota with elevated diabetes prevalence and poor disease control, as well as quantitative relationships between neighborhood deprivation and health outcomes. The findings will provide evidence to support targeted interventions in high-deprivation communities. By partnering with FQHNs and vulnerable communities, the study seeks to validate results and prioritize actionable insights. While study participants may not receive direct benefits, the knowledge gained will contribute to broader public health efforts aimed at reducing diabetes-related health disparities.