Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women. - Payal Chandak; Nicholas P. Tatonetti (Patterns, 2020)

Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. - Aniruddh Raghu; Payal Chandak; Ridwan Alam; John V. Guttag; Collin M. Stultz (ICML, 2023)

Machine Learning for Health (ML4H) 2023. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Kristen Severson 001; Mercy Nyamewaa Asiedu; Serina Chang; Bonaventure F. P. Dossou; Qian Huang; Fahad Kamran; Haoran Zhang 003; Sujay Nagaraj; et al. (ML4H@NeurIPS, 2023)

Event-Based Contrastive Learning for Medical Time Series. - Hyewon Jeong; Nassim Oufattole; Aparna Balagopalan; Matthew B. A. McDermott; Payal Chandak; Marzyeh Ghassemi; Collin M. Stultz (CoRR, 2023)

Scientific discovery in the age of artificial intelligence. - Hanchen Wang 002; Tianfan Fu; Yuanqi Du; Wenhao Gao 001; Kexin Huang; Ziming Liu; Payal Chandak; Shengchao Liu; Peter Van Katwyk; Andreea Deac; Anima Anandkumar; Karianne Bergen; et al. (Nat., 2023)

Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing. - Qianwen Wang; Kexin Huang; Payal Chandak; Marinka Zitnik; Nils Gehlenborg (IEEE Trans. Vis. Comput. Graph., 2023)

Machine Learning for Health (ML4H) 2024. - Helen Zhou; Stefan Hegselmann; Elizabeth Healey; Trenton Chang; Caleb Ellington; Michael Leone; Vishwali Mhasawade; Sana Tonekaboni; Winston Chen; Hyewon Jeong; Xiaoxiao Li; Juyeon Heo; et al. (ML4H@NeurIPS, 2024)