Please join us on Friday, April 17, 2026, from 12:30 to 1:30 p.m. for the in-person ISP Forum in SENSQ 5317.  We will be featuring two ISP students,  Quinn Wolter & Abhay Nandiraju.

Quinn Wolter Presentation Information

Title: CivicWatch: A Civic-Tech Platform for Tracing Rhetoric, Influence, and Legislative Behavior on Social Media

Abstract: Political discourse increasingly unfolds on social media, producing vast volumes of rhetoric, engagement, and conflict that quickly fragment, decay, and disappear from public attention as events move on. While these signals shape public understanding, they are difficult for voters, journalists, and researchers to interpret in aggregate or over time. CivicWatch is a visual analytics platform designed to support sensemaking over large-scale political social media data by organizing discourse, engagement, and credibility signals into interpretable, event-aware views. The system integrates tens of millions of posts from U.S. state and federal legislators with temporal, topical, and interaction-based metrics (such as civility, misinformation exposure, amplification, and silence) to enable comparative exploration across actors, topics, and moments. This talk presents the design rationale behind CivicWatch, grounded in visual analytics principles and expert input, and discusses how interface design can help diverse audiences reason about political behavior without collapsing complex dynamics into opaque scores. I conclude by outlining how CivicWatch functions as both a civic-facing tool and a research testbed for studying political communication at scale.

Bio: Quinn K Wolter is a first-year PhD student in the Intelligent Systems Program at the University of Pittsburgh, co-advised and working with the PAWS Lab under Professor Peter Brusilovsky and the PICSO Lab under Professor Yu-Ru Lin. His research focuses on designing accessible, human-centered data systems that help diverse audiences make sense of complex information. Drawing from visual analytics, computational social science, and personalized adaptive web systems, Quinn’s work emphasizes transparency, interpretability, and open-source approaches to civic and educational technology.

Abhay Nandiraju’s Presentation Information

Title: Multimodal Representation Learning for Breast Cancer Recurrence Prediction 

Abstract: The prediction of breast cancer recurrence is vital for tailoring patient-specific treatment plans and improving long-term outcomes. This research investigates a multimodal representation learning framework that fuses information from diverse, high-dimensional modalities, including DCE-MRI, mammography, and tabular clinical data. Unlike traditional unimodal approaches, this methodology focuses on synthesizing complementary signals—specifically the spatio-temporal dynamics of DCE-MRI, the spatial features of mammograms, and the longitudinal variables within clinical records. This presentation will detail the technical challenges of managing class-imbalanced, multi-source medical datasets and explore optimization techniques for effective cross-modal feature fusion. By integrating these disparate data streams, this work aims to develop more robust predictive models to enhance clinical decision support and personalized oncology. 

Bio: Abhay is a first-year PhD Student in the Intelligent Systems Program at the University of Pittsburgh, advised by Dr. Shandong Wu. His research interests lie at the intersection of multimodal learning, representation learning, and foundation models, specifically focusing on biomedical images and text.