ISP AI Forum: Arun Narayanan & Zhengbo Zhou

Please join us for the ISP Forum where we will be featuring Zhengbo Zhou and Arun Narayanan. Come enjoy lunch and refreshments starting at noon, followed by presentations from 12:30–1:30 PM. The event will be held in person in SENSQ 5317. Zhengbo Zhou’s Presentation Information Presentation Title: Modeling Spatiotemporal Asymmetries in Longitudinal Mammography for Breast Cancer Risk Prediction Abstract: Breast cancer risk prediction from longitudinal mammograms requires modeling subtle changes not only over time but also between the left and right breasts. In this talk, I will present STA-Risk, a deep learning framework that explicitly models spatiotemporal asymmetries in longitudinal mammography for future breast cancer risk prediction. The framework incorporates side and temporal encodings together with an asymmetry-aware learning objective to capture bilateral differences and longitudinal changes across screening examinations. I will also discuss challenges in cross-cohort domain shift and how joint training across datasets can improve robustness and generalization. These findings highlight the potential of explicitly modeling spatial and temporal asymmetries for personalized breast cancer risk assessment from routinely acquired screening mammograms. Bio: Zhengbo Zhou is a PhD in the Intelligent Systems Program at the University of Pittsburgh, advised by Prof. Shandong Wu. His research focuses on artificial intelligence for medical imaging, particularly longitudinal representation learning, breast cancer risk prediction, and multimodal learning. Arun Narayanan’s Presentation Information Title: Addressing a Bias in Evaluating of Student Self-Explanations of Worked Programming Examples Abstract: Worked examples are step-by-step solutions to problems in a specific domain, offered to students to acquire domain-specific problem-solving skills. The power of worked examples could be magnified by combining them with self-explanations, which ask students