Published 'Toward Conditional Distribution Calibration in Survival Prediction' at NeurIPS 2024
Published 'Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration' at ICML 2024
Published 'PepRec: Progressive Enhancement of Prompting for Recommendation' at EMNLP 2024
Published 'SurvivalEVAL: A Comprehensive Open-Source Python Package for Evaluating Individual Survival Distributions' at SPACA
Co-authored ECG-based individual survival prediction work presented at SPACA
Published study on supervised ECG features outperforming others in individualized survival prediction at ML4H 2023
Published 'iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models' at CIKM 2023
Background
Research interests lie at the intersection of machine learning, deep learning, bioinformatics, survival analysis, and causal inference in healthcare applications
Currently focused on theoretical optimization of survival analysis
Working on building effective and interpretable individual survival distribution models to identify actionable factors in breast cancer