Masha Volkova Wins Best Poster Award at 2026 Data Science Poster Fair
Department of Computer Science & Engineering master's student Masha Volkova was awarded the Best Poster Award at the Spring 2026 Data Science Poster Fair. They were honored for their project, "Multimodal Predictive Model for Clinical Outcomes in Emergency Departments".
Ross Volkov and Wenjie Zhang were the runner ups for the award for their projects, "Entity aware 3D dynamic intersection modeling" and "Zero-Shot Blind Inpainting with Pretrained Diffusion Priors.
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 Masha, Ross, and Wenjie for receiving this year's awards! Their abstracts are listed below.
Masha Volkova - Best Poster Winner
Project: Multimodal Predictive Model for Clinical Outcomes in Emergency Departments
Advisor: Feng Xie, Division of Computational Health Sciences
Abstract: Emergency departments are responsible for quickly and accurately identifying patients who require hospitalization or are at risk of critical outcomes. Machine learning models trained on electronic health record (EHR) data can be leveraged to improve early prediction of these outcomes in emergency department (ED) patients. These models can support clinical decisions that are made during key timepoints, such as ED triage and disposition, enabling more efficient resource allocation and improved care for patients.
Electronic health record data includes multiple modalities, including structured clinical data, clinical notes, and diagnostic ECG data. Each data modality captures different aspects of patient health. However, many existing predictive models rely on a single data modality, failing to fully leverage complementary information available across EHR data sources. This study evaluates whether combining multiple EHR data modalities within a multimodal model improves prediction of clinical outcomes for ED patients.
The MIMIC-IV, MIMIC-IV-ED, MIMIC-IV-Note, and MIMIC-IV-ECG datasets were used to develop predictive models for four binary classification tasks: (1) hospitalization prediction at ED triage, (2) critical outcome prediction at ED triage, (3) hospitalization prediction at ED disposition, and (4) critical outcome prediction at ED disposition.
For each prediction task, three models were trained on different EHR data modalities: structured clinical data, clinical notes (diagnostic and radiology), and electrocardiogram (ECG) data. Structured data was modeled using a gradient boosting classifier, clinical notes using a transformer-based natural language processing (NLP) model, and ECG data using a fine-tuned HuBERT-ECG transformer model for binary classification. The performance of each single-modality model was evaluated independently, and the predicted probabilities from each individual model were combined to form a multimodal prediction model.
Among the single-modality models, the structured data model achieved the highest AUROC performance, followed by the clinical notes and ECG models. The multimodal model outperformed the individual modality models with AUROC values of 0.80 for hospitalization prediction at ED triage, 0.87 for critical outcome prediction at ED triage, 0.998 for hospitalization prediction at ED disposition, and 0.92 for critical outcome prediction at ED disposition. These results suggest that multimodal approaches capture complementary clinical information from varying EHR data sources and achieve higher performance than single-modality models. Furthermore, these findings indicate that adjusting the prediction timepoint from ED triage to ED disposition improves model performance due to increased availability of relevant data during the ED visit.
This study demonstrates the potential of multimodal machine learning models to improve clinical outcome prediction in emergency departments and highlights the value of integrating structured data, clinical notes, and ECG data within a multimodal model to support informed clinical decision making.
Ross Volkov - Runner Up
Project: Entity aware 3D dynamic intersection modeling
Advisor: Seongjin Choi, Department of Civil, Environmental, and Geo- Engineering
Abstract: SPARC is an end-to-end deep learning–based approach for 3D reconstruction of traffic intersections from a sparse multi-camera setup. Accurate 3D reconstruction of traffic intersections is critical for enabling safer autonomous navigation, improving traffic modeling, and supporting the development of intelligent transportation systems. Traditional multi-view reconstruction approaches are often limited to 2D bird’s-eye-view representations or semantic predictions, while existing methods for full 3D RGB reconstruction typically require a large number of diverse viewpoints. To address these limitations, the SPARC pipeline combines monocular depth estimation with pairwise correspondence and point cloud optimization to generate accurate 3D RGB representations from as few as four camera views. The method is evaluated on a custom dataset generated using the CARLA simulator and demonstrates improved reconstruction fidelity compared to baseline approaches.
Wenjie Zhang - Runner Up
Project: Zero-Shot Blind Inpainting with Pretrained Diffusion Priors
Advisor: Ju Sun, Department of Computer Science & Engineering