Payal Chandak

According to our database, Payal Chandak authored at least 9 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Models.

Payal Chandak, Victoria Alkin, David Wu, Maya Dagan, Taposh Dutta Roy, Maria Clara Saad Menezes, Ayush Noori, Nirali Somia, John S. Brownstein, Ran D. Balicer, Rebecca W. Brendel, Noa Dagan, Isaac S. Kohane, Gabriel A. Brat

CoRR, May, 2026

EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records.

Payal Chandak, Gregory Kondas, Isaac S. Kohane, Matthew B. A. McDermott

CoRR, March, 2026

2024

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, Payal Chandak, Ayush Noori, Sarah Jabbour, Jessica Dafflon, Jerry Ji, Jivat Neet Kaur, Amin Adibi, Xu Cao, Meera Krishnamoorthy, Yidi Huang, Fabian Gröger, Aishwarya Mandyam, Niloufar Saharhkhiz, Teya S. Bergamaschi, William Boag, Jeroen Berrevoets, Matthew Lee, Kyle Heuton, Peniel N. Argaw, Haoran Zhang

Proceedings of the Machine Learning for Health, 2024

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

Scientific discovery in the age of artificial intelligence.

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun K. Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik

Nat., 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

Machine Learning for Health (ML4H) 2023.

Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel N. Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen

Proceedings of the Machine Learning for Health, 2023

Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series.

Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz

Proceedings of the International Conference on Machine Learning, 2023

2020

Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women.

Payal Chandak, Nicholas P. Tatonetti

Patterns, 2020